MicroStrategy is a long-standing business intelligence and analytics vendor that operates worldwide. Founded in 1989, this publicly traded company with hundreds of millions of dollars in revenue recently held its first in-person conference since prior to the pandemic. Similar to previous in-person events, the event was well attended by about 2,000 attendees and exhibitors. The theme, “MicroStrategy One,” is a way to explain the breadth of capabilities the company offers. The breadth of the product offering is one of the company’s greatest strengths, but also one of its biggest challenges.
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Topics: embedded analytics, Analytics, Business Intelligence, Digital Technology, natural language processing, Analytics & Data
The data platforms market has traditionally been divided between products specifically designed to support operational or analytic workloads, with other market segments having emerged in recent years for data platforms targeted specifically at data science and machine learning (ML), as well as real-time analytics. More recently, we have seen vendor strategies evolving to provide a more consolidated approach, with data platforms designed to address a combination of analytics and data science, as well as hybrid operational and analytic processing. Snowflake, which has been hugely successful in recent years with its cloud-based analytic data platform, is a prime example. The company has expanded its purview to address data engineering and data science, as well as transactional data. Additionally, it now provides users with the ability to access and process data in on-premises environments as part of its strategy to address an increasing range of use cases.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, AI and Machine Learning, Analytics & Data, operational data platforms, Analytic Data Platforms
The recent publication of our Value Index research highlights the impact of intelligent applications on the operational data platforms sector. While we continue to believe that, for most use cases, there is a clear, functional requirement for either analytic or operational data platforms, recent growth in the development of intelligent applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations, has increasing influence over requirements for operational data platforms to support real-time analytic functionality. Operational data platform vendors, including MongoDB, are responding to these evolving requirements with new functionality to support the development and deployment of intelligent applications.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, Analytics and Data, operational data platforms, Analytic Data Platforms
Early last December, just before ChatGPT became the new, bright, shiny object, The Economist magazine ran a story proclaiming that we had finally arrived at the age of boring artificial intelligence (AI). From my perspective, it’s unfortunate that didn’t last and that AI has been relegated back to the buzzword league. AI will be an increasingly important feature of business software through the end of this decade. Ventana Research asserts that by 2026, almost all vendors of software designed for finance organizations will have incorporated some AI capabilities to reduce workloads and improve performance. The same observation applies, to some significant degree, to other parts of an enterprise, so it’s important for people in operational roles to understand what AI can and cannot do. It’s also important for vendors to clearly and concretely communicate what they mean when they say, “AI-enabled.” Moreover, I prefer the alternative term “augmented intelligence” because it emphasizes that these systems enhance — rather than replace — the capabilities of the humans employing them, especially through improved decision-making and by eliminating the need to perform repetitive work.
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Topics: Office of Finance, Business Intelligence, Business Planning, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, AI and Machine Learning, continuous supply chain, digital finance, Purchasing/Sourcing/Payments, Consolidate/Close/Report
Organizations are continuously searching for new business opportunities hidden in their data. They are using various technologies including artificial intelligence and machine learning (AI/ML) to uncover granular insights that can support decision-making. Existing tools and dashboards are effective for observing standard metrics; however, they do not address follow-up questions, such as why things are happening or how those events impact performance. Organizations also struggle to derive complete value from big data. Our Analytics and Data Benchmark Research shows that only 1 in 5 organizations are very confident in their ability to analyze large volumes of data.
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Topics: Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Decision Intelligence
Data analytics provide valuable insights and enable organizations to make better decisions, improve performance and gain a competitive advantage in the marketplace. Analytics can change frequently depending on the data being analyzed and the methods used to gather and process it. Factors such as new data, changes in the underlying systems or updates to algorithms can all contribute to differences in an analysis. AnalyticOps helps ensure data is accurate, up-to-date and consistent across different systems and teams, and that analytical models are robust, reliable and continuously improved.
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Topics: embedded analytics, Analytics, Business Intelligence
insightsoftware provides applications for finance departments and other business users in midsize and larger organizations, offering a broad range of functions including analysis, internal and regulatory reporting, planning, consolidation, tax provision and treasury. The software brings together applications that enable business users to maximize data collected in existing systems and streamline the performance of a range of office of finance functions, all while limiting or eliminating the involvement of IT professionals.
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Topics: Office of Finance, embedded analytics, Business Intelligence, Business Planning, Financial Performance Management, ERP and Continuous Accounting, digital finance, profitability management, Revenue, Lease and Tax Accounting
Markets have been more volatile than ever. It creates a need for decision makers to utilize technologies such as artificial intelligence and machine learning (AI/ML) to better understand the external factors that impact their business. By identifying these factors, organizations can better plan for changing market environments and seize market opportunities. However, manual modeling is a time-consuming process and results in a limited number of models and tests. Also, updating those models is slow and laborious. With the addition of market volatility, it creates multiple challenges for CFOs, managers and financial planning specialists. With limited exposure to external drivers of demand and delivery, the process becomes very costly. Developing accurate forecasts requires integrating exogenous data with the internal performance data, but it’s challenging to find quality external data and then get that raw data clean enough to input into any model. My colleague, Robert Kugel, recently shared his perspective on using external data for forecasting, budgeting and planning to enhance predictive capabilities.
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Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning
Ventana Research recently announced its 2023 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.
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Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, natural language processing, Process Mining, Analytics and Data, Collaborative & Conversational Computing
Organizations conduct data analysis in many ways. The process can include multiple spreadsheets, applications, desktop tools, disparate data systems, data warehouses and analytics solutions. This creates difficulties for management to provide and maintain updated information across multiple departments. Our Analytics and Data Benchmark Research shows that organizations face a variety of challenges with analytics and business intelligence. One-third of participants find it difficult to integrate analytics and BI with other business processes. Participants also find that not all software is flexible enough for the constantly changing business environment, and that it is hard to access all data sources.
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Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning
For far too long, business intelligence technologies have left the rest of the exercise to the reader. Many of these tools do an excellent job providing information in an interactive way that lets organizations dive into the data and learn a lot about what has happened across all aspects of the business. More recently, many of these tools have added augmented intelligence capabilities that help explain why things happened. But rarely did any of these tools provide information about what to do or how to evaluate the alternative ways in which you might respond.
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Topics: Analytics, Business Intelligence, Digital Technology, AI and Machine Learning, Analytics & Data
The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products and services that are available from data and analytics vendors. Data platform providers, both operational and analytic, have had to adapt to changing customer demand. The initial response — making existing products available for deployment on cloud infrastructure — only scratched the surface in terms of responding to emerging expectations. We now see the next generation of products, designed specifically to deliver innovation by taking advantage of cloud-native architecture, being brought to market both by emerging startups, and established vendors, including InterSystems.
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Topics: Business Intelligence, Cloud Computing, Data Management, Data, natural language processing, AI and Machine Learning, data operations, Analytics & Data, operational data platforms, Analytic Data Platforms
Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics. Our research has shown that creating and managing metrics in a semantic model improves analytics processes.
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Topics: Analytics, Business Intelligence, Data Governance, Data, Digital Technology, Analytics & Data
Ventana Research uses the term “data pantry” to describe a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. It’s a pantry because all the data one needs is readily available and easily accessible, with labels that are immediately recognized and understood by the users of the application. In tech speak, this means the semantic layer is optimized for the intended audience. It is stocked with data gathered from multiple sources and immediately available for analysis, forecasting, planning and reporting. This does away with the need for analysts to repeatedly perform data extraction, enrichment or transformation motions from the required source systems, all but eliminating the substantial amount of time analysts and business users routinely spend on data preparation.
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Topics: Continuous Planning, Business Intelligence, Data Management, Business Planning, Data, Financial Performance Management, Enterprise Resource Planning, AI and Machine Learning, continuous supply chain, data operations, digital finance, profitability management, Analytics & Data, Streaming Data & Events
In previous perspectives in this series, I’ve discussed some of the realities of cloud computing including costs, hybrid and multi-cloud configurations and business continuity. This perspective examines the realities of security and regulatory concerns associated with cloud computing. These issues are often cited by our research participants as reasons they are not embracing the cloud. To be fair, the majority of our research participants are embracing the cloud. However, among those that have not yet made the transition to the cloud, security and regulatory concerns are among the most common issues cited across the various studies we have conducted.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Digital Technology, AI and Machine Learning, Analytics & Data, Governance & Risk
Recently, I suggested you need to “mind the gap” between data and analytics. This perspective addresses another gap — the gap in skills between business intelligence (BI) and artificial intelligence/machine learning (AI/ML).
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Topics: Analytics, Business Intelligence, Digital Technology, AI and Machine Learning, Analytics & Data
Earlier this year, I wrote about the increasing importance of data observability, an emerging product category that takes advantage of machine learning (ML) and Data Operations (DataOps) to automate the monitoring of data used for analytics projects to ensure its quality and lineage. Monitoring the quality and lineage of data is nothing new. Manual tools exist to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data observability vendors, including Monte Carlo Data, have emerged in recent years with the goal of increasing the productivity of data teams and improving organizations’ trust in data using automation and artificial intelligence and machine learning (AI/ML).
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Topics: Business Intelligence, Cloud Computing, Data Management, Data, data operations
One of the most significant considerations when choosing an analytic data platform is performance. As organizations compete to benefit most from being data-driven, the lower the time to insight the better. As data practitioners have learnt over time, however, lowering time to insight is about more than just high-performance queries. There are opportunities to improve time to insight throughout the analytics life cycle, which starts with data ingestion and integration, includes data preparation and data management, as well as data storage and processing, and ends with data visualization and analysis. Vendors focused on delivering the highest levels of analytic performance, such as SQream, understand that lowering time to insight relies on accelerating every aspect of that life cycle.
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Topics: Business Intelligence, Data, AI and Machine Learning, data operations, Analytic Data Platforms
Embedded business intelligence (BI) continues to transform the business landscape, enabling organizations to quickly interpret data and convert it into actionable insights. It allows organizations to extract information in real time and answer wide-ranging business questions. Embedding analytics helps tackle the issue of extracting information from data which is a time-consuming process. Our research shows organizations spend more time cleaning and optimizing data for analysis rather than creating insights. On top of that, they are adding more data sources and information systems which in turn introduces more complexity. Our Analytics and Data Benchmark Research shows that organizations face various challenges with analytics and BI. More than one-third of participants (35%) responded that they find it hard to integrate analytics and BI with business processes and connect to multiple data sources. By embedding analytics and BI into business processes and workflows, organizations can enable users to make critical decisions fast, enhancing overall business agility.
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Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Streaming Analytics
In today’s data-driven world, organizations need real-time access to up-to-date, high-quality data and analysis to keep pace with changing market dynamics and make better strategic decisions. By mining meaningful insights from enterprise data quickly, they gain a competitive advantage in the market. Yet, organizations face a multitude of challenges when transitioning into an analytics-driven enterprise. Our Analytics and Data Benchmark Research shows that more than one-quarter of organizations find it challenging to access data sources and integrate data and analytics in business processes. Vendors such as IBM offer a broad set of analytics tools with self-service capabilities that allows organizations to reduce IT dependencies and enables decision-makers to recognize performance gaps, market trends and new revenue opportunities. Its technology can simplify data access for self-service applications, enabling users to make business decisions informed by insights and take the guesswork out of decision-making.
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Topics: embedded analytics, Analytics, Business Intelligence, IBM, IBM Watson, AI and Machine Learning
The starting point of an era is never precise and rarely conforms to neat calendar delineations. For example, the start of the 20th century is associated with the outbreak of war in 1914. So I expect that decades from now, the consensus will hold that what became known as the 21st century began in the year 2020, with the pandemic serving as a catalyst that accelerated already existing trends and forced changes to prevailing norms and practices. This and other disruptive events that have followed are reverberating through economic and social networks and will ultimately result in some new equilibrium, but the ructions on the way there will be sharp and ever-present. Large-scale disruptions in most aspects of doing business have forced change on organizations. In this climate, the financial planning and analysis group can play a far more important role by using technology to enhance organizational agility and improve performance.
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Topics: Office of Finance, Business Intelligence, Business Planning, Financial Performance Management, AI and Machine Learning, digital finance, profitability management, operational data platforms
IBM Planning Analytics with Watson is a comprehensive, cloud-based business planning application that supports what Ventana Research calls integrated business planning. We coined this term in 2007 to describe a high-participation approach to business planning that integrates strategy, operations and finance. Our Next Generation Business Planning Benchmark Research demonstrated the value of IBP: Organizations that link planning processes get better results. Sixty-six percent of organizations that have an integrated method say it works well or very well, compared to only 25% that have little or no connection between plans.
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Topics: Predictive Analytics, Office of Finance, embedded analytics, Business Intelligence, Business Planning, Financial Performance Management, Watson, Digital transformation, AI and Machine Learning, digital finance, profitability management
If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.
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Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Management, data operations, Analytics & Data
I have previously written about growing interest in the data lakehouse as one of the design patterns for delivering hydroanalytics analysis of data in a data lake. Many organizations have invested in data lakes as a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads, especially semi- and unstructured data that is unsuitable for storing and processing in a data warehouse. However, early data lake projects lacked structured data management and processing functionality to support multiple business intelligence efforts as well as data science and even operational applications.
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Topics: Business Intelligence, Data Governance, Data Management, Data, AI and Machine Learning, Streaming Data & Events, Analytic Data Platforms
I have written recently about the similarities and differences between data mesh and data fabric. The two are potentially complementary. Data mesh is an organizational and cultural approach to data ownership, access and governance. Data fabric is a technical approach to automating data management and data governance in a distributed architecture. There are various definitions of data fabric, but key elements include a data catalog for metadata-driven data governance and self-service, agile data integration.
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Topics: Business Intelligence, Cloud Computing, Data Governance, Data Management, Data, AI and Machine Learning, data operations, operational data platforms
Ventana Research’s Data Lakes Dynamics Insights research illustrates that while data lakes are fulfilling their promise of enabling organizations to economically store and process large volumes of raw data, data lake environments continue to evolve. Data lakes were initially based primarily on Apache Hadoop deployed on-premises but are now increasingly based on cloud object storage. Adopters are also shifting from data lakes based on homegrown scripts and code to open standards and open formats, and they are beginning to embrace the structured data-processing functionality that supports data lakehouse capabilities. These trends are driving the evolution of vendor product offerings and strategies, as typified by Cloudera’s recent launch of Cloudera Data Platform (CDP) One, described as a data lakehouse software-as-a-service (SaaS) offering.
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Topics: Business Intelligence, Data Governance, Data Management, Data, AI and Machine Learning, data operations, Analytics and Data, Streaming Data & Events, operational data platforms, Analytic Data Platforms
As I recently pointed out, process mining has emerged as a pivotal technology for data-driven organizations to discover, monitor and improve processes through use of real-time event data, transactional data and log files. With recent advancements, process mining has become more efficient at discovering insights in complex processes using algorithms and visualizations. Organizations use it to better understand the current state of systems and business processes. It is also used to enable business process intelligence and improvement in any function or industry using events and activity models for data-driven decision-making. We assert that through 2024, 1 in 4 organizations will look to streamline their operations by exploring process mining to optimize workflow and business processes.
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Topics: Analytics, Business Intelligence, AI and Machine Learning, Process Mining, Streaming Analytics
Earlier this year I described the growing use-cases for hybrid data processing. Although it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads respectively, there is increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. There are multiple data platform approaches to delivering real-time data processing and analytics, including the use of streaming data and event processing and specialist, real-time analytic data platforms. We also see operational data platform providers, such as Aerospike, adding analytic processing capabilities to support these application requirements via hybrid operational and analytic processing.
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Topics: Business Intelligence, Cloud Computing, Data, AI and Machine Learning, Streaming Data & Events, operational data platforms, Analytic Data Platforms
A predictive finance department is one that can command technology to be more forward-looking and action-oriented while still fulfilling its core role of handling the financial elements of its organization including accounting, treasury and corporate finance. Beyond just automating rote tasks, technology also facilitates a shift toward becoming a predictive finance organization. Greater amounts of information, now available in near real time, and the increasing use of artificial intelligence (AI), enable more immediate analyses and assessments of possible courses of action, providing executives and managers the ability to better anticipate change and the agility to adapt quickly to unexpected circumstances.
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Topics: Office of Finance, Business Intelligence, Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance
Process mining is defined as the analysis of application telemetry including log files, transaction data and other instrumentation to understand and improve operational processes. Log data provides an abundance of information about what operations are occurring, the sequences involved in the processes, how long the processes are taking and whether or not the processes are completed successfully. As computing power has increased and storage costs have decreased, the economics of collecting and analyzing large amounts of log data have become much more attractive.
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Topics: Analytics, Business Intelligence, AI and Machine Learning, Process Mining
Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also, there are more regulations and compliance requirements than ever before. It is critical for organizations to understand the kind of data they have, who is handling it, what it is being used for and how it needs to be protected. They also have to avoid putting too many layers and wrappers around the data as it can make the data difficult to access. These challenges create a need for more automated ways to discover, track, research and govern the data.
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Topics: Business Intelligence, Data Governance, Data Management, AI and Machine Learning, data operations
Kinaxis recently announced it has acquired a Netherlands-based company, MPO, a cloud-based software offering that orchestrates multiparty supply chain execution. The combination is designed to enable Kinaxis to extend its concurrent planning platform to handle core elements of supply chain execution. Kinaxis acquired all the shares of MPO for approximately US$45 million, with some of the final consideration dependent on performance. MPO will continue to operate as a standalone business, but will be increasingly integrated into Kinaxis’ operations worldwide.
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Topics: Business Intelligence, Business Planning, Operations & Supply Chain, Enterprise Resource Planning, AI and Machine Learning, continuous supply chain
Business intelligence has evolved. It now includes a spectrum of analytics, one of the most promising of which has been described as augmented intelligence. Some organizations have used the term to describe the practical reality that artificial intelligence with machine learning is not replacing human intelligence, but augmenting it. The term also represents the application of AI/ML to make business intelligence and analytics tools more powerful and easier to use. It’s this latter usage that I prefer and I’d like to explore in this perspective.
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Topics: Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Analytics & Data, Collaborative & Conversational Computing
Organizations are managing and analyzing large datasets every day, identifying patterns and generating insights to inform decisions. This can provide numerous benefits for an organization, such as improved operational efficiency, cost optimization, fraud detection, competitive advantage and enhanced business processes. By bringing the right, actionable data to the right user, organizations can potentially speed up processes and make more effective operational decisions.
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Topics: embedded analytics, Business Intelligence, Internet of Things, AI and Machine Learning, Streaming Analytics
The analytics and business intelligence market landscape continues to grow as more organizations seek robust tools and capabilities to visualize and better understand data. BI systems are used to perform data analysis, identify market trends and opportunities and streamline business processes. They can collect and combine data from internal and external systems to present a holistic view.
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Topics: Analytics, Business Intelligence, Data Governance, Data Management, AI and Machine Learning, Analytics & Data
Anaplan offers a cloud-based business planning platform that incorporates a modeling and calculation engine. The tool makes it relatively easy to add or expand the scope of plans that can be connected and monitored on a single platform. This Integrated Business Planning (IBP) approach enables organizations to use the software for financial planning or budgeting, sales, supply chain, workforce, marketing and IT planning. These are the types of plans in which companies often need to create models that incorporate their specific requirements, business systems and strategy. I expect that by 2025, one-fourth of financial planning and analysis (FP&A) groups will have implemented IBP.
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Topics: Office of Finance, Continuous Planning, Business Intelligence, Business Planning, Financial Performance Management, AI and Machine Learning, continuous supply chain, digital finance, profitability management
I recently explained how emerging application requirements were expanding the range of use cases for NoSQL databases, increasing adoption based on the availability of enhanced functionality. These intelligent applications require a close relationship between operational data platforms and the output of data science and machine learning projects. This ensures that machine learning and predictive analytics initiatives are not only developed and trained based on the relationships inherent in operational applications, but also that the resulting intelligence is incorporated into the operational application in real time to support capabilities such as personalization, recommendations and fraud detection. Graph databases already support operational use cases such as social media, fraud detection, customer experience management and recommendation engines. Graph database vendors such as Neo4j are increasingly focused on the role that graph databases can play in supporting data scientists, enabling them to develop, train and run algorithms and machine learning models on graph data in the graph database, rather than extracting it into a separate environment.
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Topics: Business Intelligence, Data, AI and Machine Learning, operational data platforms, Analytic Data Platforms
I often use the term “analytics” to refer to a broad set of capabilities, deliberately broader than business intelligence. In this Perspective, I’d like to share what decision-makers should consider as they evaluate the range of analytics requirements for their organization.
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Topics: Business Intelligence, natural language processing, AI and Machine Learning, Streaming Analytics, Analytics & Data
Organizations are collecting vast amounts of data every day, utilizing business intelligence software and data visualization to gain insights and identify patterns and errors in the data. Making sense of these patterns can enable an organization to gain an edge in the marketplace and plan more strategically.
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Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning
When joining Ventana Research, I noted that the need to be more data-driven has become a mantra among large and small organizations alike. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven is clearly something to aspire to. However, it is also a somewhat vague concept without clear definition. We know data-driven organizations when we see them — the likes of Airbnb, DoorDash, ING Bank, Netflix, Spotify, and Uber are often cited as examples — but it is not necessarily clear what separates the data-driven from the rest. Data has been used in decision-making processes for thousands of years, and no business operates without some form of data processing and analytics. As such, although many organizations may aspire to be more data-driven, identifying and defining the steps required to achieve that goal are not necessarily easy. In this Analyst Perspective, I will outline the four key traits that I believe are required for a company to be considered data-driven.
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Topics: embedded analytics, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, natural language processing, data lakes, AI and Machine Learning, data operations, Digital Business, Streaming Analytics, data platforms, Analytics & Data, Streaming Data & Events
I previously described the concept of hydroanalytic data platforms, which combine the structured data processing and analytics acceleration capabilities associated with data warehousing with the low-cost and multi-structured data storage advantages of the data lake. One of the key enablers of this approach is interactive SQL query engine functionality, which facilitates the use of existing business intelligence (BI) and data science tools to analyze data in data lakes. Interactive SQL query engines have been in use for several years — many of the capabilities were initially used to accelerate analytics on Hadoop — but have evolved along with data lake initiatives to enable analysis of data in cloud object storage. The open source Presto project is one of the most prominent interactive SQL query engines and has been adopted by some of the largest digital-native organizations. Presto managed-services provider Ahana is on a mission to bring the advantages of Presto to the masses.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms, Analytics & Data
I’ve never been a fan of talking about semantic models because most of the workforce probably doesn’t understand what they are, or doesn’t recognize them by name. But the findings in our recent Analytics and Data Benchmark Research have changed my mind. The research shows how important a semantic model can be to the success of data and analytics processes. Organizations that have successfully implemented a semantic model are more than twice as likely to report satisfaction with analytics (77%) compared with a 33% overall satisfaction rate. Therefore, I owe it to all of you to write about them.
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Topics: Business Intelligence, Data Management, AI and Machine Learning, data operations, Analytics & Data, semantic model
As I recently described, it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads, albeit with growing demand for hybrid data processing use-cases and functionality. Specialist operational and analytic data platforms have historically been the since preferred option, but there have always been general-purpose databases that could be used for both analytic and operational workloads, with tuning and extensions to meet the specific requirements of each.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, data platforms, Analytics & Data
Organizations are scaling business intelligence initiatives to gain a competitive advantage and increase revenue as more data is created. Lack of expertise, data governance and slow performance can impact these efforts. Our Analytics and Data Benchmark Research finds some of the most pressing complaints about analytics and BI include difficulty integrating with other business processes and flexibility issues. Kyvos is a BI acceleration platform that enables BI and analytics tools to analyze massive amounts of data. It offers support for online analytical processing-based multidimensional analytics, enabling workers to access large datasets with their analytics tools. It operates with major cloud platforms, including Google Cloud, Amazon Web Services and Microsoft Azure.
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Topics: Business Intelligence, Data Governance
I recently wrote about the potential benefits of data mesh. As I noted, data mesh is not a product that can be acquired, or even a technical architecture that can be built. It’s an organizational and cultural approach to data ownership, access and governance. While the concept of data mesh is agnostic to the technology used to implement it, technology is clearly an enabler for data mesh. For many organizations, new technological investment and evolution will be required to facilitate adoption of data mesh. Meanwhile, the concept of the data fabric, a technology-driven approach to managing and governing data across distributed environments, is rising in popularity. Although I previously touched on some of the technologies that might be applicable to data mesh, it is worth diving deeper into the data architecture implications of data mesh, and the potential overlap with data fabric.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
I recently described the use cases driving interest in hybrid data processing capabilities that enable analysis of data in an operational data platform without impacting operational application performance or requiring data to be extracted to an external analytic data platform. Hybrid data processing functionality is becoming increasingly attractive to aid the development of intelligent applications infused with personalization and artificial intelligence-driven recommendations. These applications can be used to improve customer service; engagement, detect and prevent fraud; and increase operational efficiency. Several database providers now offer hybrid data processing capabilities to support these application requirements. One of the vendors addressing this opportunity is SingleStore.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data
There is a fundamental flaw in information technology, or at least in the way it is most commonly delivered. Most technology systems are developed under the assumption that all people will use the system primarily in the same way. Sure, there are some options built in — perhaps the same action can be initiated by either clicking on a button, selecting a menu item or invoking a keyboard short-cut. The problem is that when every variation needs to be coded into the system, the prospect of providing personalized software programs to every individual is impractical.
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Topics: Business Intelligence, Data Management, natural language processing, AI and Machine Learning, data operations, Analytics & Data
Organizations have been using data virtualization to collect and integrate data from various sources, and in different formats, to create a single source of truth without redundancy or overlap, thus improving and accelerating decision-making giving them a competitive advantage in the market. Our research shows that data virtualization is popular in the big data world. One-quarter (27%) of participants in our Data Lake Dynamic Insights Research reported they were currently using data virtualization, and another two-quarters (46%) planned to include data virtualization in the future. Even more interesting, those who are using data virtualization reported higher rates of satisfaction (79%) with their data lake than those who are not (36%). Our Analytics and Data Benchmark Research shows more than one-third of organizations (37%) are using data virtualization in that context. Here, too, those using data virtualization reported higher levels of satisfaction (88%) than those that are not (66%).
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Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning, Streaming Analytics
I recently wrote about the importance of data pipelines and the role they play in transporting data between the stages of data processing and analytics. Healthy data pipelines are necessary to ensure data is integrated and processed in the sequence required to generate business intelligence. The concept of the data pipeline is nothing new of course, but it is becoming increasingly important as organizations adapt data management processes to be more data driven.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, AI and Machine Learning, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and infrastructure and adapt to its principles of domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance. Many organizations will need to make technological changes to facilitate adoption of data mesh, however. Starburst Data is associated with accelerating analysis of data in data lakes but is also one of several vendors aligning their products with data mesh.
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Topics: Business Continuity, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, data lakes, Digital Business, data platforms, Analytics & Data
Data mesh is the latest trend to grip the data and analytics sector. The term has been rapidly adopted by numerous vendors — as well as a growing number of organizations —as a means of embracing distributed data processing. Understanding and adopting data mesh remains a challenge, however. Data mesh is not a product that can be acquired, or even a technical architecture that can be built. It is an organizational and cultural approach to data ownership, access and governance. Adopting data mesh requires cultural and organizational change. Data mesh promises multiple benefits to organizations that embrace this change, but doing so may be far from easy.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
For years, maybe decades, we have heard about the struggles between IT and line-of-business functions. In this perspective, we will look at some of the data from our Analytics and Data Benchmark Research about the roles of IT and line-of-business teams in analytics and data processes. We will also look at some of the disconnects between these two groups. And, by looking at how organizations are operating today and the results they are achieving, we can discern some of the best practices for improving the outcomes of analytics and data processes.
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Topics: Analytics, Business Intelligence, Data, Digital Technology, AI and Machine Learning, Analytics & Data
Despite widespread and increasing use of the cloud for data and analytics workloads, it has become clear in recent years that, for most organizations, a proportion of data-processing workloads will remain on-premises in centralized data centers or distributed-edge processing infrastructure. As we recently noted, as compute and storage are distributed across a hybrid and multi-cloud architecture, so, too, is the data it stores and relies upon. This presents challenges for organizations to identify, manage and analyze all the data that is available to them. It also presents opportunities for vendors to help alleviate that challenge. In particular, it provides a gap in the market for data-platform vendors to distinguish themselves from the various cloud providers with cloud-agnostic data platforms that can support data processing across hybrid IT, multi-cloud and edge environments (including Internet of Things devices, as well as servers and local data centers located close to the source of the data). Yellowbrick Data is one vendor that has seized upon that opportunity with its cloud Data Warehouse offering.
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Topics: Analytics, Business Intelligence, Data Governance, Data, AI and Machine Learning, data operations, data platforms
Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance. Traditional processes are slow when transforming large and diverse datasets into something which is easily consumable in BI. And, it can take days or weeks to create reports and dashboards — maybe longer if processes change and new data sources are introduced. Our Analytics and Data Benchmark Research shows that the most time-consuming processes are preparing data, reviewing it for quality issues and preparing reports for presentation and distribution.
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Topics: Big Data, Analytics, Business Intelligence, Cloud
As I stated when joining Ventana Research, the socioeconomic impacts of the pandemic and its aftereffects have highlighted more than ever the differences between organizations that can turn data into insights and are agile enough to act upon it and those that are incapable of seeing or responding to the need for change. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. One of the key methods that accelerates business decision-making is reducing the lag between data collection and data analysis.
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Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
I recently described how the data platforms landscape will remain divided between analytic and operational workloads for the foreseeable future. Analytic data platforms are designed to store, manage, process and analyze data, enabling organizations to maximize data to operate with greater efficiency, while operational data platforms are designed to store, manage and process data to support worker-, customer- and partner-facing operational applications. At the same time, however, we see increased demand for intelligent applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. The need for real-time interactivity means that these applications cannot be served by traditional processes that rely on the batch extraction, transformation and loading of data from operational data platforms into analytic data platforms for analysis. Instead, they rely on analysis of data in the operational data platform itself via hybrid data processing capabilities to accelerate worker decision-making or improve customer experience.
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Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data, Streaming Data & Events
Organizations of all sizes are dealing with exponentially increasing data volume and data sources, which creates challenges such as siloed information, increased technical complexities across various systems and slow reporting of important business metrics. Migrating to the cloud does not solve the problems associated with performing analytics and business intelligence on data stored in disparate systems. Also, the computing power needed to process large volumes of data consists of clusters of servers with hundreds or thousands of nodes that can be difficult to administer. Our Analytics and Data Benchmark Research shows that organizations have concerns about current analytics and BI technology. Findings include difficulty integrating data with other business processes, systems that are not flexible enough to scale operations and trouble accessing data from various data sources.
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Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, Streaming Analytics
Ventana Research recently announced its 2022 Market Agenda for the Office of Finance, continuing the guidance we have offered since 2003 on the practical use of technology for the finance and accounting department. Our insights and best practices aim to enable organizations to operate with agility and resiliency, improving performance and delivering greater value as a strategic partner.
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Topics: Office of Finance, Business Intelligence, Collaboration, Business Planning, Financial Performance Management, ERP and Continuous Accounting, Revenue, blockchain, robotic finance, Predictive Planning, AI and Machine Learning, lease and tax accounting, profitability management
Ventana Research recently announced its 2022 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments in order to improve business outcomes.
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Topics: embedded analytics, Analytics, Business Intelligence, Digital Technology, natural language processing, Process Mining, Analytics & Data, Collaborative & Conversational Computing
Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables line-of-business personnel to query the data faster, generate reports themselves without depending on IT, and make quick decisions. Some organizations have started using NLP in self-service analytics to quickly identify patterns and simplify data visualization. Our Analytics and Data Benchmark Research finds that about 81% of organizations expect to use natural language search for analytics to make timely and informed decisions.
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Topics: embedded analytics, Analytics, Business Intelligence, Data Integration, Data, natural language processing, data lakes, AI and Machine Learning, data operations, data platforms
Few trends have had a bigger impact on the data platforms landscape than the emergence of cloud computing. The adoption of cloud computing infrastructure as an alternative to on-premises datacenters has resulted in significant workloads being migrated to the cloud, displacing traditional server and storage vendors. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research currently use cloud computing products for analytics and data, and a further one-quarter plan to do so. In addition to deploying data workloads on cloud infrastructure, many organizations have also adopted cloud data and analytics services offered by the same cloud providers, displacing traditional data platform vendors. Organizations now have greater choice in relation to potential products and providers for data and analytics workloads, but also need to think about integrating services offered by cloud providers with established technology and processes. Having pioneered the concept, Amazon Web Services has arguably benefitted more than most from adoption of cloud computing, and is also in the process of expanding and adjusting its portfolio to alleviate challenges and encourage even greater adoption.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data
Organizations today are working with multiple applications and systems, including enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) and other systems, where data can easily become fragmented and siloed. And as the organization increases its data sources and adds more systems and custom applications, it becomes challenging to manage the data consistently and keep data definitions up to date. This increases the need to use master data management (MDM) software that can provide a single source of truth to drive accurate analytics and business operations.
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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Analytics & Data
Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is not enough to generate energy from that water. A hydroelectric power station is required to harness and unleash the power-generating potential of a lake or reservoir, utilizing a combination of turbines, generators and transformers to convert the energy of the flowing water into electricity. A hydroanalytic data platform, the data equivalent of a hydroelectric power station, is required to harness and unleash the intelligence-generating potential of a data lake.
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Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms
Talend is a data integration and management software company that offers applications for cloud computing, big data integration, application integration, data quality and master data management. The platform enables personnel to work with relational databases, Apache Hadoop, Spark and NoSQL databases for cloud or on-premises jobs. Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a unified view of all data. Its code generation architecture uses a visual interface to create Java or SQL code.
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Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. If an organization goes through the trouble of measuring and reporting on a metric, the analysis ought to include all the information needed to evaluate that metric effectively. A number, by itself, does not provide any indication of whether the result is good or bad. Too often, the reader is expected to understand the difference, but why leave this evaluation to chance? Why not be more explicit about what results are expected?
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Topics: Analytics, Business Intelligence, Internet of Things, Data, Digital Technology, AI and Machine Learning, Streaming Analytics
Databricks is a data engineering and analytics cloud platform built on top of Apache Spark that processes and transforms huge volumes of data and offers data exploration capabilities through machine learning models. It can enable data engineers, data scientists, analysts and other workers to process big data and unify analytics through a single interface. The platform supports streaming data, SQL queries, graph processing and machine learning. It also offers a collaborative user interface — workspace — where workers can create data pipelines in multiple languages — including Python, R, Scala, and SQL — and train and prototype machine learning models.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, Data, data lakes, AI and Machine Learning
The work environment today demands that your organization advances the efficiency to execute business processes for continuous operations to have a positive impact on business performance. The capability to be responsive to any range of minor to disruptive business events is required to support business continuity and level of organizational readiness to meet the needs of digital business. Ventana Research asserts that in 2025, one-quarter of organizations will remain digitally ineffective in achieving the business priorities for customer-, product- and people-related processes. It is essential to eliminate bottlenecks and become an organization that places action and decision-making at is center to optimize the execution of business processes.
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Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Cloud Computing, Contact Center, Data, Digital Technology, Operations & Supply Chain, Enterprise Resource Planning, Digital transformation, natural language processing, AI and Machine Learning, continuous supply chain, agent management, Digital Business, Experience Management, Field Service, Process Mining, Streaming Analytics
Access to external data can provide a competitive advantage. Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning (ML) efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part of ML analyses though. Our research shows that external data sources are also a routine part of data preparation processes, with 80% of organizations incorporating one or more external data sources. And a similar proportion of participants in our research (84%) include external data in their data lakes.
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Topics: Analytics, Business Intelligence, Internet of Things, Data, Digital Technology, Lease Management, AI and Machine Learning, Streaming Data, Streaming Analytics
Alteryx is a data analytics software company that offers data preparation and analytics tools to simplify and automate data wrangling, data cleaning and modeling processes, enabling line-of-business personnel to quickly access, manipulate, analyze and output data. The platform features tools to run a variety of analytic functions such as diagnostic, predictive, prescriptive and geospatial analytics in a unified platform, and can connect to various data warehouses, cloud applications, spreadsheets and other sources.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, AI and Machine Learning
Collibra is a data governance software company that offers tools for metadata management and data cataloging. The software enables organizations to find data quickly, identify its source and assure its integrity. Line-of-business workers can use it to create, review and update the organization's policies on different data assets. Collibra’s software uses a microservice architecture and open application programming interfaces to connect to various data ecosystems. Its data intelligence cloud platform can automatically classify data from various sources such as online transaction processing databases, master repositories and Excel files without moving the data, so the information assets stay protected.
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Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
Subscription management and billing services help organizations offer unique benefits and enhance delivery to customers. By making services more personalized, organizations can acquire – and retain – more customers.
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Topics: Sales, Office of Finance, Continuous Planning, embedded analytics, Analytics, Business Intelligence, Business Planning, Product Information Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, AI and Machine Learning, revenue and lease accounting, continuous supply chain, Subscription Management, partner management, digital finance, Process Mining, Streaming Analytics, supplier relationship management
A looming challenge for companies in the developed world is price inflation, an issue periodically fretted over – but not experienced at a macroeconomic level in most developed economies – over the past four decades. Price inflation has been a frequent bugaboo that never emerged because of persistent disinflationary forces in the world economy over the past forty years. It remains to be seen to what extent recent price rises are persistent or transitory but “what if?” was the most important phrase organizations used in 2020. What if this time it really is different?
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Topics: Office of Finance, Business Intelligence, Business Planning, Financial Performance Management, ERP and Continuous Accounting
Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization. Rapidminer Studio is its visual workflow designer for the creation of predictive models. It offers more than 1,500 algorithms and functions in their library, along with templates, for common use cases including customer churn, predictive maintenance and fraud detection. It has a drag and drop visual interface and can connect to databases, enterprise data warehouses, data lakes, cloud storage, business applications and social media. The platform also supports push-down processing for data prep and ETL inside databases to minimize data movement and optimize performance.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, data lakes, AI and Machine Learning
Confluent Platform is a streaming platform built by the original creators of Apache Kafka. It enables organizations to organize and manage streaming data from various sources. Confluent launched its IPO in June this year and raised $828 million to further expand its business. Confluent Platform was brought to several public cloud vendor marketplaces last year as Confluent Cloud. The offering is currently available in Azure, AWS, and GCP marketplaces. Furthermore, the company strengthened its partnership with Microsoft at the beginning of this year, establishing Confluent Cloud as a fully managed Apache Kafka service directly available on Microsoft Azure. Azure customers can access the extensive library of pre-built connectors, a unified billing model with options to use Azure committed spend on Confluent Cloud, and deeper integrations with Azure services.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data, AI and Machine Learning
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
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Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, blockchain, data lakes, AI and Machine Learning
Environmental, social and governance reporting by public corporations has become a top-of-mind issue for senior executives and boards of directors as countries increasingly consider or mandate its implementation in some form. The fundamental rationale for ESG reporting is rooted in the inability of purely financial measures to capture externalities (such as greenhouse gas emissions) or provide metrics that enable an objective assessment of management’s ability to properly determine trade-offs between short-term results and long-term sustainability. And, while in the United States the Sarbanes-Oxley Act mandates that auditors assess governance, the focus of this assessment is on preventing financial fraud as opposed to broader objectives that may be important to the functioning of the company as a sustainable entity.
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Topics: Human Capital Management, Office of Finance, Business Intelligence, Data Governance, Data Preparation, Data, Financial Performance Management, ERP and Continuous Accounting
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Digital Technology, natural language processing, AI and Machine Learning
A year of business uncertainty, lockdowns and operational disruptions forced finance and accounting organizations to adapt and change in many ways that are proving to be permanent. The need to operate virtually resulted in some organizations accelerating their adoption of technology, bringing them closer to achieving a transformation of the finance and accounting function: reshaping the department into an organization that is more forward-looking and strategic. Strategic in the sense of providing greater visibility into how the company and each of its business units is performing and insight into how to achieve better results going forward. Its focus is on what is happening next and not merely on what just happened. It does not only explain past results but uses that context to provide guidance about the choices executives and managers have, and the likely impact of those choices. To truly achieve this degree of transformation requires a different departmental structure, one that incorporates a Finance IT capability.
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Topics: Office of Finance, Business Intelligence, Data Governance, Data Preparation, Business Planning, Financial Performance Management, ERP and Continuous Accounting, blockchain, robotic finance, Predictive Planning, AI and Machine Learning
The annual Ventana Research Digital Innovation Awards showcase advances in the productivity and potential of business applications, as well as technology that contributes significantly to the improved processes and performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations to advance business and IT.
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Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Continuous Planning, embedded analytics, Learning Management, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Business Planning, Contact Center, Data, Product Information Management, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, Revenue, blockchain, natural language processing, data lakes, Total Compensation Management, robotic finance, Predictive Planning, employee experience, candidate engagement, Conversational Computing, Continuous Payroll, AI and Machine Learning, collaborative computing, mobile computing, continuous supply chain, Subscription Management, agent management, extended reality, intelligent marketing, sales enablement, work experience management, lease and tax accounting, robotic automation
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2021 Ventana Research Value Index: Embedded Analytics and Data is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and included vendors.
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Topics: Analytics, Business Intelligence, Data Preparation, Information Management, Data, natural language processing
We are happy to share some insights about ThoughtSpot drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Microsoft Power BI drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Tableau drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about SAS drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Board International drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Yellowfin drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Domo drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
We are happy to share some insights about Oracle Analytics Cloud drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
We are happy to share some insights about IBM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, natural language processing, AI and Machine Learning
Alation recently announced the release of its 2021.1 version, introducing new data governance capabilities, enhancements in search and discovery through data domains, and extended connector and query coverage for data sources. Alation’s new federated authentication enables users to query cloud services such as Amazon Web Services, Snowflake, Tableau and more, using a single sign-on. The release also includes a Search application programming interface that allows for the integration of Alation Search with third-party tools. And, with the addition of the Open Connector Framework software development kit in the 2021.1 update, Alation enables organizations to create connectors for data sources not already supported by Alation.
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Topics: Analytics, Business Intelligence, Collaboration, Data Preparation, Data, Information Management (IM), AI and Machine Learning
Unit4’s Financial Planning and Analysis (formerly Prevero) is a planning and budgeting application designed for the requirements of midsize corporations and the public sector. These organizations are challenged in buying software because they have almost all the requirements of larger enterprises but have a smaller budget and limited technical resources.
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Topics: Office of Finance, embedded analytics, Analytics, Business Intelligence, Business Planning, Financial Performance Management, Price and Revenue Management, Digital Technology, ERP and Continuous Accounting, Predictive Planning, AI and Machine Learning, collaborative computing
Observed both here and elsewhere, average sales quota attainments appear to be in an exorable decline. As I discussed in my recent Analyst Perspective, "The Art and Science of Sales from the 'Inside Out'," vendors of sales technology have reacted to this by adding a slew of new functionality including the potential for artificial intelligence (AI) to be a game changer for sales. One can argue that this use of AI is still relatively immature having been generally available only since 2014, but that is still over five years of market availability.
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Topics: Sales, Human Capital Management, Analytics, Business Intelligence, Sales Performance Management, candidate engagement, AI and Machine Learning, sales enablement
Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models. AutoML can also enable organizations to make data science initiatives more accessible across the organization.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
IBM Planning Analytics, formerly known as TM1, is a comprehensive planning and analytics application designed to integrate and streamline an organization’s planning processes. It can support multiple planning use cases on a single platform, including financial, headcount, sales and demand planning. The software automates enterprise-wide data collection to make it repeatable and scalable across multiple users and departments. It supports sophisticated driver-based modeling that enables rapid what-if or scenario-based planning, while its built-in analytics provide deep business intelligence capabilities. This enables senior executives and managers to work interactively to immediately assess their current position and consider the impact of various options to address opportunities and issues rather than laboring through a lengthy process.
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Topics: Office of Finance, embedded analytics, Analytics, Business Intelligence, Collaboration, Business Planning, ERP and Continuous Accounting, Predictive Planning, AI and Machine Learning
Digital commerce affects almost everyone’s lives. It is hard to remember a time when one could not sign on to a website like Amazon, order a product, pay for it and have it delivered to your front door within days, not weeks. Although catalogues have been around for a century or so, the digital-commerce revolution has changed the way we think about shopping for many of our everyday and special occasion products. Extend this to digital services, such as streaming videos or online games, and there is barely a sector that has not been touched by digital commerce. And, for organizations, it is an essential component of their revenue-management efforts that enables the digital transformation and monetization of goods and services.
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Topics: Sales, Customer Experience, Analytics, Business Intelligence, Product Information Management, Price and Revenue Management, Digital Commerce, AI and Machine Learning
Having just completed the 2021 Ventana Research Value Index for Analytics and Data, I want to share some of my observations about how the market has advanced since our assessment two years ago. The analytics software market is quite mature and products from any of the vendors we assess can be used to effectively deliver information to help your organization improve its operations. However, it’s also interesting to see how much the market continues to advance and how much investment vendors continue to make.
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Topics: Big Data, Key Performance Indictors, embedded analytics, exadata, Analytics, Business Collaboration, Business Intelligence, Collaboration, Data Preparation, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing
Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed here are essential for organizations to effectively manage their data assets.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Data, AI and Machine Learning, Microsoft Azure
Ventana Research recently announced its 2021 market agenda for the Office of Finance, continuing the guidance we’ve offered since 2003 on the practical use of technology for the finance and accounting department. Our insights and best practices aim to enable organizations to operate with agility and resiliency, improving performance and delivering greater value as a strategic partner.
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Topics: Office of Finance, enterprise profitability management, Business Intelligence, Collaboration, Business Planning, Financial Performance Management, ERP and Continuous Accounting, Revenue, blockchain, robotic finance, Predictive Planning, AI and Machine Learning, lease and tax accounting, virtual audit, virtual close
Ventana Research recently announced its 2021 market agenda for Analytics, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.
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Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Process Mining, Streaming Analytics
The industry is making huge strides with artificial intelligence (AI) and machine learning (ML). There is more data available to analyze. Analytics vendors have made it easier to build and deploy models, and AI/ML is being embedded into many types of applications. Organizations are realizing the value that AI/ML provides and there are now millions of professionals with AI or ML in their title or job description. AI/ML is even being used to make many aspects of itself easier. Organizations that want to build and deploy their own AI/ML models need to be realistic about the capabilities that are available today. As a practical matter, organizations should anticipate that a robust AI/ML deployment in the current environment requires a set of specialized skills and operational processes, including data operations (dataops) and ML operations (MLops). Collaboration across these disciplines and processes is also required.
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Topics: Sales, Customer Experience, Marketing, Analytics, Business Intelligence, Data Preparation, Digital Technology, AI and Machine Learning
Organizations are dealing with exponentially increasing data that ranges broadly from customer-generated information, financial transactions, edge-generated data and even operational IT server logs. A combination of complex data lake and data warehouse capabilities are required to leverage this data. Our research shows that nearly three-quarters of organizations deploy both data lakes and data warehouses but are using a variety of approaches which can be cumbersome. A single platform that can provide both capabilities will help address organizations’ requirements.
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Topics: PROS Pricing, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
Businesses are transforming their organizations, building a data culture and deploying sophisticated analytics more broadly than ever. However, the process of using data and analytics is not always easy. The necessary tools are often separate, but our research shows organizations prefer an integrated environment. In our Data Preparation Benchmark Research, we found that 41% of participants use Analytics and Business Intelligence tools for data preparation.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Information Management, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
Traditional on-premises data processing solutions have led to a hugely complex and expensive set of data silos where IT spends more time managing the infrastructure than extracting value from the data. Big data architectures have attempted to solve the problem with large pools of cost-effective storage, but in doing so have often created on-premises management and administration challenges. These challenges of acquiring, installing and maintaining large clusters of computing resources gave rise to cloud-based implementations as an alternative. Public cloud is becoming the new center for data as organizations migrate from static on-premises IT architectures to global, dynamic and multi-cloud architectures.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, data lakes, AI and Machine Learning
Teradata is not a name that is commonly associated with the customer experience marketplace, but that is likely to change as customer experience (CX) practitioners wrestle with the problems created by the multiple streams of data thrown off by the many applications and customer touchpoints they have to manage. Teradata’s Vantage CX is a tool for ingesting and managing customer information at great scale, combining the functions of a modern CDP with the analytics that makes customer data actionable.
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Topics: Customer Experience, Marketing, Analytics, Business Intelligence, Contact Center, Data, Digital Marketing, Digital Commerce, AI and Machine Learning, intelligent marketing
Although historically there has been a hard divide between what are colloquially called “Inside and Field Sales,” changes over the last 10 years have narrowed the distinction. The pandemic has only accelerated the path to unifying sales activities commonly performed to engage buyers and customers. Characterized by a very disciplined and controlled endeavor, inside sales teams have been heavier users of technology. This has enabled more productive engagement including emails and calls, as well as provided techniques such as gamification to set competitive internal dynamics that help motivate sales professionals.
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Topics: Sales, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Sales Performance Management (SPM), natural language processing, AI and Machine Learning, intelligent sales, sales enablement
The annual Ventana Research Digital Innovation Awards showcases advances in the productivity and potential of business applications, as well as technology that contributes significantly to improved efficiency and productivity in the processes and the performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing
Ventana Research has been evaluating analytics and business intelligence (BI) software for a long time—almost 20 years. Our methodology for these assessments is referred to as a Value Index. We use weightings derived from our benchmark research about how you, as buyers of these technologies, value and evaluate vendors. You can view our 2019 Value Index results here. I am in the process of completing the 2020 evaluation now.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing
The last decade has seen exponential growth amongst subscription-based business models. Pioneered in the B2C market with cloud-based SaaS offerings, the last decade has seen exponential growth in the share of the economy that is now subscription based. Increasingly, this modern business model is permeating throughout more traditional style industries and companies. But regardless of whether a company is natively subscription based, or is transitioning, maintaining this growth requires organizations to foster long-term relationships with customers and deliver products and services that get better over time.
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Topics: Sales, Customer Experience, Office of Finance, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Contact Center, Product Information Management, Price and Revenue Management, Digital Commerce, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, robotic finance, AI and Machine Learning, revenue and lease accounting, Subscription Management, agent management, intelligent sales, sales enablement
An important recent development in software designed for the Office of Finance is the addition of what we’re calling a data aggregation device (DAD) for analytical applications. A DAD automates the collection of data from disparate sources using, for example, application programming interfaces (APIs) and robotic process automation (RPA). With a DAD, users of the analytical application have immediate access to a much broader data set; one that incorporates operational as well as financial data from internal and external sources. The larger data set enables a much more expansive set of analyses than has been feasible in the past because the process of acquiring the data is automated, and the data aggregation is handled in a controlled manner. This control means that data in the system is authoritative, accurate, consistent, complete and secure. The difference between a DAD and a finance data mart is that the former is prebuilt for the specific application, and therefore eliminates this source of implementation costs and offers faster time to value.
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Topics: Office of Finance, Analytics, Business Intelligence, Data, Financial Performance Management, Price and Revenue Management, robotic finance, Predictive Planning, AI and Machine Learning
Subscription-based business models have seen exponential growth over the last decade. The growth of this recurring revenue business model, where a subscriber commits to repeatedly pay for a good or device for a fixed or indefinite timeline, has been caused by the shift from the one-time selling of physical products to selling digital services on a subscription basis. The first phase of this transformation was led by “digitally native” organizations, typically B2C, that have only ever offered services via subscription. Although a large market in its own right, it is still dwarfed by businesses selling physical products. But this market is also changing, as more and more traditional organizations transition some or all of their revenue to the subscription economy. Ventana Research asserts that through 2023, fewer than half of organizations will have the correct technology in place to support such a transition. This Analyst Perspective looks at some of the key implications of this transition and what it means for technology choices as companies move toward a subscription management approach to overseeing the subscribers and usage of their products and services.
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Topics: Sales, Customer Experience, Office of Finance, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Contact Center, Product Information Management, Price and Revenue Management, Digital Commerce, Enterprise Resource Planning, ERP and Continuous Accounting, natural language processing, robotic finance, AI and Machine Learning, revenue and lease accounting, Subscription Management, agent management, intelligent sales, sales enablement
I’m very excited to announce to my network as well as the ever-expanding Ventana Research community that I’m now directing Ventana Research’s Office of Sales practice. The focus is to guide and educate sales and business professionals on the selling applications and technology including digital commerce, price and revenue management, product information management, sales enablement, sales performance management and subscription management. While these are the main topics of our Office of Sales practice, my decades of experience in analytics, artificial intelligence (AI) and planning are part of what I bring to the firm to help advance the science of selling.
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Topics: Sales, embedded analytics, Analytics, Business Intelligence, Collaboration, Data, Product Information Management, Sales Performance Management, Price and Revenue Management, Digital Technology, Work and Resource Management, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, intelligent sales, sales enablement
One of the challenges of being a practically minded technology analyst is squaring the importance of “the next big thing” with the reality of what most organizations are doing. For decades it’s been the case that “the next big thing” in the world of information technology is easily several years ahead of where most organizations are in their use of technology. And before most organizations can realize the benefit of some whiz-bang technology, they frequently need to address a range of more mundane issues, such as data availability and accuracy, employee training and corporate culture, among other impediments. Sometimes, though, advanced technology works to uncomplicate things for organizations.
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Topics: Human Capital Management, Marketing, Office of Finance, Analytics, Business Intelligence, Sales Performance Management, Financial Performance Management, Price and Revenue Management, Digital Marketing, Work and Resource Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, robotic finance, Predictive Planning, AI and Machine Learning, revenue and lease accounting, Subscription Management, intelligent sales
Economic dynamics and market pressures during a black-swan event can wreak havoc on efforts to effectively manage revenue operations and pricing for business continuity. For many organizations, environmental changes disrupt the methods by which these essential business processes are managed can be disrupted, damaging the revenue streams that create profitability. The array of pricing strategies and related promotional tactics across channels for configure, price and quote (CPQ), digital commerce and subscription management can challenge the best of organizations. Leadership must examine the agility of pricing management to determine if they have ability to make and manage changes to determine the effectiveness of decisions. This requires visibility into revenue operations and selling channels, which in turn requires programs, processes and technology designed to meet the needs of what is called price and revenue management (PRM).
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Topics: Sales, Customer Experience, Human Capital Management, Marketing, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Product Information Management, Sales Performance Management, Workforce Management, Workforce Planning, Price and Revenue Management, Total Compensation Management, Conversational Computing
Over the past several months, I have discussed a wide range of topics that organizations must consider and appropriately prioritize to maintain business continuity during periods of upheaval. But sometimes it’s important to take a step back and reflect on a critical and recurring theme: experiences. The array of experiences across the workforce and business processes both inside and outside of the organization are an essential part of an organization’s success. Leadership must give these experiences the attention they deserve, and this requires visibility into operations and the tools to measure effectiveness, especially during black-swan events. Fulfilling this objective requires the programs, processes and technology designed to meet the needs of what is called experience management (XM).
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Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Sales Performance Management, Workforce Management, Workforce Planning, Operations & Supply Chain, Total Compensation Management, Conversational Computing
Business planning is an essential part of an organization’s focus on its future performance and overall potential because it ensures continuous operations, even in black-swan events. Planning across the entire organization needs to be a critical priority and leadership should give it the attention it deserves. In challenging times, a focus on execution tends to take hold — this is not unreasonable but in focusing on satisfying immediate customer and workforce needs and putting out fires, business leaders too often forget that forward-looking continuous planning is essential to achieving desired outcomes. Fulfilling this objective requires technology designed to meet these needs for every business process in the organization.
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Topics: Sales, Human Capital Management, Office of Finance, Continuous Planning, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, Total Compensation Management, Predictive Planning, Conversational Computing
Analytics and data provide visibility into an organization’s past, present and potential performance. However, not all organizations are using analytics that provide timely insights — insights that not just reflect what happen but direct a successful course for the future. Demand for personalized and relevant insight only intensifies in a black-swan event. To maintain business continuity in times of pressure, it is critical that organizations not waste any time or resources when using analytics and data to optimize operations and decision-making. Just having an analytics and data-first mentality and operating in the cloud is insufficient for success, as those are just part of an effective data and analytics effort. Organizations also should include data science and machine learning that can provide an excellent digital experience; unfortunately, this is no simple task.
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Topics: business intelligence, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject. He also provides an assessment of the risks organizations face in working with data lakes and offers recommendations for maximizing the potential of data.
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Topics: Big Data, Data Warehousing, Analytics, Business Analytics, Business Intelligence, Data Governance, Data Management, Data Preparation, data lakes
Effectively managing data privacy and security is a high-stakes matter. When an organization doesn’t get it right, it often becomes front-page news and occasionally becomes a subject of litigation. Yet organizations face an equally challenging imperative to ensure that business users have easy access to the data they need. Depending on how they are implemented, data governance policies can inhibit access to data, making it harder to find and utilize the data assets of an organization.
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Topics: Big Data, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things
Artificial intelligence (AI) and machine learning (ML) are all the rage right now. Our Machine Learning Dynamic Insights research shows that organizations are using these techniques to achieve a competitive advantage and improve both customer experiences and their bottom line. One type of analysis an organization can perform using AI and ML is predictive analytics. Organizations also need to plan their operations to predict the amount of cash they will need, inventory levels and staffing requirements. Unfortunately, while planning begins with predictions, organizations can’t plan with AI and ML. Let me explain what I mean.
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Topics: Office of Finance, Analytics, Business Intelligence, Financial Performance Management, Digital Technology, Predictive Planning, AI and Machine Learning
I was recently asked to identify key modern data architecture trends. Data architectures have changed significantly to accommodate larger volumes of data as well as new types of data such as streaming and unstructured data. Here are some of the trends I see continuing to impact data architectures.
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Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Data, Information Management (IM), Digital Technology, data lakes, AI and Machine Learning
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Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data, Digital Technology, Conversational Computing
Ventana Research recently announced its 2020 research agenda for analytics, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value from their technology investments and improve business outcomes.
It’s been exciting to follow the emergence of innovative capabilities in the analytics market, but for businesses it can be challenging to stay on top of all these changes. To help, we craft our research agenda using our firm’s knowledge of technology vendors and products and our experience with and expertise on business requirements.
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Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, natural language processing, AI and Machine Learning
For years I’ve viewed with skepticism the claim that one technology or another will reduce audit costs. For one, there’s rarely a silver bullet. An array of moving parts drive audit fees. For example, the complexity of the corporation, accounting data management and the audit staff’s familiarity with the industry and the company all affect the time auditors must spend. Also, most of the time I’ve found that achieving significant savings was not the result of going from good to great, but from fixing deep-seated issues. If a company’s books and accounting practices are a mess, it can achieve considerable savings simply by cleaning up its act. In this circumstance, technology can play a part of a broader initiative that addresses the people, process and data management elements that are behind the mess.
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Topics: Office of Finance, Analytics, Business Intelligence, Financial Performance Management, ERP and Continuous Accounting, robotic finance, AI and Machine Learning
Using customer analytics effectively involves several challenges. Organizations must make it a business priority, cultivate leadership and set a course for ensuring data and analytics are being processed and governed effectively. But effectiveness also requires technology that will assist in the effective operations and management of customers and help an organization achieve its goals.
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Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, Subscription Management, agent management, extended reality
Customer analytics have never been more important, but effectively creating and managing them is not easy. The data that’s required to achieve visibility into all customer activity involves many applications and systems and it’s a challenge to ensure the data used is accurate and consistent. Even once data is assembled, organizations often struggle to apply analytics to create the metrics that best represent an understanding of the past and, more importantly, the path to the future.
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Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Information Management, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, Intelligent CX, Conversational Computing, AI and Machine Learning, collaborative computing, Subscription Management, agent management, extended reality
By itself, data isn’t useful for business; the application of analytics is necessary to transform data into actionable information. Data analysis of one sort or another has long been a core competence of finance departments, applied to balance sheets, income statements or cash flow statements. Today, however, Finance must go beyond these basics by expanding the scope of the data being examined to include all financial and operational information that can yield actionable insights. Analysis thus should include, for example, data from the systems that manage sales operations, human resources and field service and that data must be available to all departments and applications that need it.
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Topics: Customer Experience, Human Capital Management, Voice of the Customer, embedded analytics, Learning Management, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Product Information Management, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Digital Technology, Digital Marketing, Digital Commerce, ERP and Continuous Accounting, blockchain, natural language processing, robotic finance, Predictive Planning, candidate engagement, Intelligent CX, Conversational Computing, Continuous Payroll, AI and Machine Learning, revenue and lease accounting, collaborative computing, mobile computing, Subscription Management, total rewards management, intelligent marketing, intelligent sales
We are happy to offer some insights on Qlik drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on Microsoft drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on Looker drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on Tableau drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts by Ventana Research. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on Oracle drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on Salesforce drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts by Ventana Research. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on SAS drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts by Ventana Research. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Digital Technology
We are happy to offer some insights on Yellowfin drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts by Ventana Research. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to share some insight on Domo drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on SAP drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to share some insight on BOARD drawn from our Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to share some insights on Infor based on our latest market Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on MicroStrategy drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on Mobile, Embedded and Collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
We are happy to offer some insights on Information Builders drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. Earlier this year we published the Ventana Research Value Index: Analytics and Business Intelligence 2019, the distillation of a year of market and product research efforts. We then developed three additional Value Indexes on Analytics and BI focusing on mobile, embedded and collaborative capabilities. Because each is a critical aspect of modern business intelligence, we developed specific criteria for each in order to provide an in-depth look at features geared specifically to mobile, embedded and collaborative use.
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Topics: Data Science, Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
Organizations now must store, process and use data of significantly greater volume and variety than in the past. These factors plus the velocity of data today — the unrelentingly rapid rate at which it is generated, both in enterprise systems and on the internet — add to the challenge of getting the data into a form that can be used for business tasks.
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Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Information Management, Internet of Things, Data, Digital Technology, blockchain, data lakes, AI and Machine Learning
I am happy to share some insights gleaned from our latest Value Index research, which provides our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Collaborative Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. Drawing on our benchmark research and expertise, we apply a structured research methodology built on evaluation categories that are designed to reflect the real-world criteria incorporated in a request for proposal to vendors in analytics and business intelligence. Using this methodology, we evaluated vendor submissions in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). This research-based index is the first such evaluation to assess the full business value of collaborative analytics and business intelligence software. You can learn more about our Value Index as an effective vendor selection and RFI/RFP tool at https://www.ventanaresearch.com/value-indexes.
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Topics: Analytics, Business Intelligence, Collaboration, Value Index
Domopalooza 2019 marked the first annual user conference after Domo went public, but the energy, excitement and new feature announcements have not slowed. With thousands in attendance and growing fast, this year's conference focused on five key areas: digitization, real time connectivity, driving insight based actions, applying AI & machine learning, and building applications. All of these announcements are aimed at broadening the workloads supported by Domo.
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Topics: Analytics, Business Intelligence, Collaboration, Data Integration, Data Management, Data Preparation, Domo
About 10 years ago, social media tools like Facebook, Twitter and LinkedIn introduced a wave of collaborative analytics and BI capabilities. We saw chat streams associated with specific analyses that users could like or endorse. The number of contributions a user made to the community was part of his or her profile so others could accordingly weigh the importance of the input.
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Topics: Analytics, Business Intelligence, Collaboration, Digital Technology
I am happy to offer some insights on Qlik drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Qlik and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to offer some insights on Microsoft drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Microsoft and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to offer some insights on Looker drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Looker and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology, looker
I am happy to offer some insights on Tableau drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Tableau and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Tableau, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to offer some insights on Oracle drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Oracle and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to share some insights gleaned from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Embedded Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. Drawing on our benchmark research and expertise, we apply a structured research methodology built on evaluation categories that are designed to reflect the real-world criteria incorporated in a request for proposal to vendors in analytics and business intelligence. Using this methodology, we evaluated vendor submissions in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). This research-based index is the first such evaluation to assess the full business value of embedded analytics and business intelligence software. You can learn more about our Value Index as an effective vendor selection and RFI/RFP tool at https://www.ventanaresearch.com/value-indexes.
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Topics: Mobile, embedded analytics, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to offer some insights on Salesforce.com drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Salesforce.com and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
Analytics and business intelligence (BI) play an instrumental role in enabling an organization’s business units and IT to utilize its data in both tactical and strategic ways to perform optimally. To accomplish this, organizations must not only access the data, generate and apply insights from analytics, and communicate the results, they also must ensure that the analytics are presented in a way that leads to action. One of the most effective ways to do this is to embed analytics into business processes and applications.
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Topics: embedded analytics, Analytics, Business Intelligence, Digital Technology
I am happy to share some insights gleaned from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Mobile Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. Drawing on our benchmark research and expertise, we apply a structured research methodology built on evaluation categories that are designed to reflect the real-world criteria incorporated in a request for proposal to vendors in analytics and business intelligence. Using this methodology, we evaluated vendor submissions in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). This research-based index is the first such evaluation to assess the full business value of analytics and business intelligence software. You can learn more about our Value Index as an effective vendor selection and RFI/RFP tool at https://www.ventanaresearch.com/value-indexes.
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Topics: Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
For analytics to be effective, they need to be available to line-of-business personnel as needed in their normal course of conducting business, which today means providing rich mobile access to analytics through phones and tablets to support a mobile workforce seeking to conduct business in any location at any time. Workers today expect these mobile capabilities, which means organizations must make choices to provide analytics and BI platforms that can deliver them.
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Topics: Mobile, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to offer some insights on SAS drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated SAS and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, SAS, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to share some insight on Domo drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Domo and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Domo, Digital Technology
MicroStrategy recently held their annual user conference, MicroStrategy World 2019. This year's conference brought 2,100 customer attendees plus partners to the Phoenix Convention Center in Phoenix, AZ. The big news of the event was the introduction of MicroStrategy HyperIntelligence™, a platform tool designed to directly inject analytics into business applications.
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Topics: MicroStrategy, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, AI
I am happy to offer some insights on SAP drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated SAP and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, SAP, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to share some insight on BOARD drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated BOARD and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology, BOARD International
I am happy to share some insights on Infor based on our latest market Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Infor and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Infor, Digital Technology, Infor Birst
I am happy to share some insights about IBM drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated IBM and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
I am happy to offer some insights on MicroStrategy drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated MicroStrategy and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Digital Technology
IBM's Analytics University (held in both Miami and Stockholm) brought about some large changes. Big announcements this year included a consolidation of IBM's Watson Analytics into Cognos 11.1, helping provide some clarity to their analytics offerings, along with new visualizations and better data preparation. This also includes a new conversational assistant to help generate narrative explanations of displays and interactive queries. For the full breakdown of IBM's Analytics University 2018, and my analysis of all the largest announcements, watch my latest hot take.
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Topics: Big Data, Analytics, Business Intelligence, Data Preparation, AI, natural language processing
In 2017 Strata + Hadoop World was changed to the Strata Data Conference. As I pointed out in my coverage of last year’s event, the focus was largely on machine learning and artificial intelligence (AI). That theme continued this year, but my impression of the event was of a community looking to get value out of data regardless of the technology being used to manage that data. The change was subtle: The location was the same; the exhibitors were largely the same; attendance was similar this year and last. But there was no particular vendor or technology dominating the event.
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Topics: Big Data, Data Science, Machine Learning, Analytics, Business Intelligence, Data Governance, Data Integration, Data Preparation, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing
Employee engagement has been a dominant theme in both human capital management (HCM) and the systems to manage it in recent years; lately (though not necessarily appropriately) it is a topic often equated with the notion of the employee experience. On a related point, Gallup’s annual employee engagement survey has consistently found the majority of today’s workforce to be disengaged, defined as “not enthusiastic or passionate about their work.” Interest in the degree to which HCM technology can improve employee engagement (or mitigate disengagement) now rivals the attention given to such perennial chief human resources officer (CHRO) concerns as attracting and retaining top talent and retooling the workforce.
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Topics: Big Data, Data Science, Human Capital Management, Machine Learning, Learning Management, Analytics, Business Intelligence, Cloud Computing, Collaboration, HRMS, Workforce Management, Digital Technology, Workforce Optimization
We now are well beyond the year depicted in 2001: A Space Odyssey, a cinematic perspective on the future of artificial intelligence in which HAL 9000, a computer, is able to simulate human behavior and control machines. Anyone reviewing the past two years of marketing around AI in the business technology industry can be forgiven for believing that we have arrived at the futuristic state Stanley Kubrick imagined. We have not.
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Topics: Big Data, Data Science, Mobile, Customer Analytics, Customer Engagement, Customer Experience, Machine Learning, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Customer Service, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing, Cybersecurity, Billing and Recurring Revenue, Workforce Optimization, collaboration for business
We are have arrived at the May 25, 2018 date when the European Union’s General Data Privacy Regulations (GDPR) become enforceable, following what has been a two-year transition period. Companies were given this time to put in place reasonable measures and the systems necessary to support the legislation’s wide-ranging personal data privacy requirements, which apply to any organization with more than 250 employees that serves EU citizens. While this regulation will apply in the EU, it has implications for any organization in the world that provides services involving the personal data of any EU citizen.
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Topics: Big Data, Data Science, Mobile, Sales, Customer Analytics, Customer Engagement, Customer Experience, Marketing, Analytics, Business Intelligence, Cloud Computing, Collaboration, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Digital Technology, Digital Marketing, Digital Commerce, Cybersecurity, Billing and Recurring Revenue, collaboration for business, mobile marketing
Advancing the potential of any business requires continuous improvement in the processes and technology that support it. Many companies have embraced attempts at a digital transformation, and it’s become a goal to which organizational resources and budgets have been dedicated around the globe.
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Topics: Big Data, Data Science, Mobile, Sales, Customer Analytics, Customer Engagement, Customer Experience, Human Capital Management, Machine Learning, Marketing, Marketing Performance Management, Mobile Technology, Office of Finance, Wearable Computing, Analytics, Business Intelligence, Cloud Computing, Collaboration, Customer Service, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Information Optimization, Product Information Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Machine Learning and Cognitive Computing, Pricing and Promotion Management, Cybersecurity, Billing and Recurring Revenue, Workforce Optimization, collaboration for business, mobile marketing
We at Ventana Research recently published our research agendas for 2018. Analytics and business intelligence are evolving and so is our research on their use across practice areas. Earlier research has shown that analytics can deliver significant value to organizations; for example, our predictive analytics research shows that 57 percent of organizations reported achieving a competitive advantage and half created new revenue opportunities with predictive analytics. Waves of investment in self-service analytics have propelled the market for analytics tools, significantly empowering line-of-business organizations to create their own analytics and set their own analytic priorities. But organizations are also beginning to recognize some of the limitations of current analytics implementations – for self-service, for example. Our Data Preparation Benchmark Research reveals that fewer than half (42%) of organizations are comfortable allowing business users to work with data not prepared by IT. Our research this year will continue to explore both the successes and challenges organizations face as they continue to use analytics and BI.
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Topics: Machine Learning, Analytics, Business Intelligence, Collaboration, Internet of Things, IOT, Artificial intelligence, natural language processing, Natural Language Generation
Prophix is an established provider of financial performance management (FPM) software for planning and budgeting, forecasting, analysis and reporting, and managing the financial close and consolidation process. Its eponymous software is designed specifically for midsize companies or midsize divisions of larger corporations. These organizations are a distinctive segment of the market in that they have almost all the functional requirements of large enterprises but have fewer resources to apply to these critical tasks. Fortunately, the evolution of information technology over the past decade has been especially beneficial to midsize customers, bringing them expanded capabilities, substantially better performance and greater automation of routine tasks at an affordable total cost of ownership.
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Topics: Planning, Office of Finance, Reporting, Budgeting, Consolidation, Continuous Planning, Analytics, Business Intelligence, Collaboration, Financial Performance Management, Integrated Business Planning, accounting close, Price and Revenue Management, Work and Resource Management, Sales Planning and Analytics
In 2016 Unit4 acquired Prevero, a financial performance management software company. The acquisition reflects a trend toward the convergence of transactional and analytical business applications. ERP and financial management software vendors increasingly are adding analytic capabilities – especially in financial performance management (FPM) – to the core functions of transaction processing and accounting in order to broaden the scope of their offerings. The integration of transaction processing and analytical software is especially valuable to Unit4’s core customer base of midsize organizations, which we define as those with 100 to 1,000 employees. Midsize entities have almost the same systems requirements as larger ones but lack the resources the latter enjoy.
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Topics: Marketing, Office of Finance, Continuous Planning, Analytics, Business Intelligence, Cloud Computing, Collaboration, Workforce Management, Financial Performance Management, FPM, Work and Resource Management, Operations & Supply Chain, Sales Planning and Analytics
Natural language generation (NLG), the process of generating text or narratives based on a set of data values, can reach a broader audience. NLG narratives can be used for a variety of purposes, but in this perspective I focus on how NLG can be used to enhance business intelligence (BI) processes. In the case of BI, NLG can be used to explain what has happened and why it is happening, and even what actions to take. The NLG narratives can be understood by a broader range of business users than the tables and charts of data that are the typical output of most BI applications or analytics tools.
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Topics: Machine Learning, Natural Language, Analytics, Business Intelligence
Longview recently completed the acquisition of Tidemark Systems, a planning software vendor. Longview Plan powered by Tidemark is a suite of cloud-based applications that enable corporations to plan, assess performance and communicate results more effectively. The software facilitates what Ventana Research calls “continuous planning.” This is a highly collaborative, action-oriented approach to planning that relies on frequent, short cycles to rapidly create and update integrated company-wide operational and financial plans. This structural approach makes it easy for individual business functions to create their own plans while enabling headquarters to connect those plans to create a unified view. Viewed in the long term, this acquisition provides Longview with a platform that will enable it to apply its existing on-premises intellectual property to a broader suite of web-based performance management and tax applications.
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Topics: Mobile, Office of Finance, Recurring Revenue, Continuous Planning, Analytics, Business Intelligence, Financial Performance Management, Price and Revenue Management, ERP and Continuous Accounting, Sales Planning and Analytics
This is my second analyst perspective based on our IoT Benchmark Research. In the first, I discussed the business focus of IoT applications and some of the challenges organizations are facing. Now I’ll share some of the findings about technologies used in IoT applications and the impact those technologies appear to have on the success of users’ projects.
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Topics: Big Data, Analytics, Business Intelligence, IOT, NoSQL
If we look at the focus of technology vendors for analytics and business intelligence or business applications providers deploying these capabilities in the last five years, we see that they have elevated the importance on the value of visualization and dashboards. These promotions might be understandable, but will they make business and the people using them more intelligent?
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Topics: Big Data, Data Science, Mobile, Machine Learning, Analytics, Business Intelligence, Cloud Computing, Collaboration, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing
This year various types of organizations are embracing machine learning like it is going out of style – or maybe it would be better to say coming into style. And now with a little investigation on LinkedIn finds over half million professionals with machine learning in their job title. Machine learning is the application of specific data science algorithms that become more accurate as the system records more outcomes and processes more data. This improvement is referred to as “learning,” hence the name. There are good reasons machine learning is growing so rapidly, but there are pitfalls to avoid as well.
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Topics: Big Data, Data Science, Analytics, Business Intelligence, Cloud Computing, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
Ventana Research defines financial performance management (FPM) as the process of addressing the often overlapping people, process, information and technology issues that affect how well finance departments operate and support the activities of the rest of their organization. FPM deals with the full cycle of finance department activities, which include planning and budgeting, analysis, assessment and review, closing and consolidation, internal financial reporting and external financial reporting, as well as the underlying information technology systems that support them.
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Topics: Mobile, Human Capital Management, Office of Finance, Recurring Revenue, Continuous Planning, Analytics, Business Intelligence, Financial Performance Management, Price and Revenue Management, ERP and Continuous Accounting, Sales Planning and Analytics
Informatica reintroduced itself to the world at its recent customer conference, Informatica World, in San Francisco. The company took advantage of the event to showcase its new branding in an effort to change the way customers think about the company. Informatica has been providing information services in the cloud for more than a decade. Even though cloud revenue comprises a minority of Informatica’s business, in absolute terms, the revenue is significant, and company executives want the public to recognize Informatica as a leader in cloud-based data management services for enterprises. Presenters also made notable product announcements, discussed below, including the application of machine learning to the data management process.
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Topics: Big Data, Data Science, Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data Preparation, Information Optimization, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
I recently attended the MicroStrategy World conference, which was held in Washington, D.C. and it celebrate its 20th anniversary, which is why MicroStrategy hosted the event near its headquarters. Over the past 20 years, the market and technology for business intelligence and analytics have significantly changed, and in several changes, MicroStrategy has been at the forefront. Now is a good time to examine the company’s position in the market and its latest offerings in context of the analytics market direction that I recently presented.
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Topics: Big Data, Mobile Technology, Analytics, Business Intelligence, Digital Technology
Anaplan recently held Anaplan Hub, its annual user group meeting. The company offers a cloud-based business planning platform that incorporates a modeling and calculation engine. The tool makes it relatively easy to add or expand the scope of plans that can be connected and monitored as a central source. Companies typically use Anaplan software for financial planning or budgeting, sales, workforce, marketing and IT planning. These are the types of plans in which companies often need to create models that incorporate their specific requirements, their strategy and their business systems.
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Topics: Customer Analytics, Human Capital Management, Marketing, Marketing Performance Management, Office of Finance, Recurring Revenue, Continuous Planning, Analytics, Business Intelligence, Cloud Computing, Collaboration, HRMS, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Work and Resource Management, Operations & Supply Chain, Sales Enablement and Execution, ERP and Continuous Accounting, Sales Planning and Analytics
Some 3,000 people attended Domo’s recent customer event, called Domopalooza. That’s nearly double the attendance of the previous event, which my colleague Mark Smith covered. Formerly a bit “stealthy,” Domo has started sharing more information, some of which I’ll pass along, as well as observations about product announcements made at the event.
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Topics: Data Science, Mobile, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
Business process reengineering was a consulting fashion in the early 1990s that spurred many companies to purchase their first ERP systems. BPR proposes a fundamental redesign of core business processes to achieve substantial improvements in market and customer responsiveness, productivity, cycle times and quality. ERP systems support business process reengineering by guiding the step-by-step execution of the redesigned process to ensure that it is performed consistently. They also automate the handoffs between individuals and departments to accelerate completion of that process.
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Topics: Big Data, Data Science, Mobile, Customer Analytics, Customer Experience, Machine Learning, Office of Finance, Wearable Computing, Continuous Planning, Analytics, Business Intelligence, Cloud Computing, Data Integration, Internet of Things, Financial Performance Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, Machine Learning and Cognitive Computing, ERP and Continuous Accounting, Sales Planning and Analytics
Until recently most organizations deployed systems on their own premises to build communications and contact center infrastructures, which often required them to integrate products from several vendors. In the past few years many vendors have moved their systems to the cloud, and others have begun as cloud-based suppliers. This trend has opened up the opportunity for more organizations to take advantage of modern communication systems and contact centers. Using the cloud for either, or both can save money and resources, reduce risk, and make available more integrated, multi-channel systems. While the adoption of such systems has undoubtedly increased and is likely to continue to do so, our benchmark research into next-generation contact centers in the cloud finds that many organizations still prefer to remain on premises, and adoption of cloud-based systems occurs on a case-by-case basis. In addition, many organizations look for vendors that support multiple models so they have the option of starting out using one model but transitioning later to another, including to a hybrid model in which some systems are on-premises and others are cloud-based..
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Topics: Big Data, Mobile, Customer Analytics, Customer Engagement, Customer Experience, Machine Learning, Wearable Computing, Analytics, Business Intelligence, Cloud Computing, Collaboration, Internet of Things, Contact Center, Digital Commerce, Subscription Billing