The Office of Finance can be compared to a numbers factory where the main raw material, data, is transformed into financial statements, management accounting, analyses, forecasts, budgets, regulatory filings, tax returns and all kinds of reports. Data is the strategic raw material of the finance and accounting department. It is the key ingredient in every sale and purchase as well as every transaction of any description. Quality control is essential to achieving high standards of output in any factory, and finance is no exception. To that end, a great deal of effort goes into managing the department’s processes well. However, too little attention is paid to the quality of the raw material — the data — and how it is handled at every stage of a process. Since the office lockdowns forced by the pandemic of 2020, there has been widespread agreement that the finance and accounting department needs to digitally transform to ensure continuity and resiliency under any circumstances. To improve their performance and that of the entire organization, finance department executives must adopt a total quality management (TQM) approach to managing data in their department.
Data is the Strategic Raw Material of Finance Departments
Topics: Office of Finance, embedded analytics, Analytics, Business Intelligence, Data Management, Business Planning, ERP and Continuous Accounting, AI and Machine Learning, data operations, digital finance, operational data platforms, Analytic Data Platforms, Revenue, Lease and Tax Accounting, Purchasing/Sourcing/Payments, Consolidate/Close/Report
Large Language Models and Generative AI: Beware Misplaced Trust
The data and analytics sector rightly places great importance on data quality: Almost two-thirds (64%) of participants in Ventana Research’s Analytics and Data Benchmark Research cite reviewing data for quality and consistency issues as the most time-consuming task in analyzing data. Data and analytics vendors would not recommend that customers use tools known to have data quality problems. It is somewhat surprising, therefore, that data and analytics vendors are rushing to encourage customers to incorporate large language models into analytics processes, despite LLMs sometimes generating content that is inaccurate and untrustworthy.
Topics: Analytics, Data Governance, Data Management, Data, Digital Technology, natural language processing, AI and Machine Learning, Analytics & Data
Artificial intelligence (AI) has evolved from a highly specialized niche technology to a worldwide phenomenon. Nearly 9 in 10 organizations use or plan to adopt AI technology. Several factors have contributed to this evolution. First, the amount of data they can collect and store has increased dramatically while the cost of analyzing these large amounts of data has decreased dramatically. Data-driven organizations need to process data in real time which requires AI. In addition, analytics vendors have been augmenting business intelligence (BI) products with AI. And recently, ChatGPT has raised awareness of AI and instigated research and experimentation into new ways in which AI can be applied. This perspective, the second in a series on generative AI, introduces some of the concepts behind ChatGPT, including large language models and transformers. Understanding how these models work can help provide a better understanding of how they should be applied and what cautions are necessary.
Topics: Analytics, Digital Technology, natural language processing, AI and Machine Learning, Analytics & Data
The Path to Generative AI to Boost Contact Center Agent Performance
Artificial intelligence (AI) has become ubiquitous in discussions of contact center technology. Vendors are rushing to incorporate it into platforms and applications. And end users have understandably mixed feelings about where it makes sense to use and what its impacts will be. No one should be surprised that AI has arrived, especially for customer support: Software companies have been working on customer experience (CX)- -related AI applications for many years, and the fruits of those efforts have been gradually working their way into real-world tools, particularly chatbots. Solutions that help automate the self-service gateway to customer support are able to boost speed and efficiency in customer interactions, and this can lead to faster response times, reduced wait times, and improved first-contact resolution rates, all of which can have a positive impact on customer satisfaction and loyalty.
Topics: Customer Experience, Contact Center, AI and Machine Learning, agent management, Intelligent Self-Service
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.
Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, AI and Machine Learning, Analytics & Data, operational data platforms, Analytic Data Platforms
OneStream Advances Generative AI to Improve Productivity
OneStream offers a platform designed to serve the needs of accounting and financial planning and analysis organizations. The software handles financial close and consolidation, planning and budgeting, analysis and reporting. The most notable part of the company’s presentations at its annual user group meeting – Splash – was the strategy and roadmap for its two artificial intelligence initiatives, Sensible ML and Sensible GPT. The former, unveiled last year, is a platform approach to applying machine learning to business forecasting, while the latter harnesses the power of large language models such as ChatGPT to streamline the performance of almost any business process.
Topics: Office of Finance, AI and Machine Learning, digital finance
Generative AI Ushers in a New Age of Content and Model Creation
Generative AI is a class of artificial intelligence used to generate new, seemingly real content. Broadly speaking, AI has traditionally been used to identify patterns in data and apply those patterns to categorize and predict behaviors. For instance, it can organize customers into groups (or clusters) with similar characteristics, or predict which customers are most likely to respond to certain offers.
Topics: Analytics, Digital Technology, AI and Machine Learning
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.
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.
Topics: Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Decision Intelligence
More Effective Contingency Planning Improves Agility
We live in a time of uncertainty, not unpredictability. Managing an organization in uncertain times is always hard, but tools are available to improve the odds for success by making it easier and faster to plan for contingencies and scenarios. Software makes it possible to quickly consider the impact of a range of events or assumptions and devise a set of plans to deal with them. Dedicated planning and budgeting software has been around for decades but is about to become all the more useful as vendors increasingly incorporate artificial intelligence using machine learning to assist in scenario planning. Organizations can quickly investigate the impact of different contingencies and the consequences of a range of reactions to them.
Topics: Office of Finance, Data Management, Business Planning, AI and Machine Learning, digital finance
Advanced Analytics Enable More Informed Decision-Making
I’ve previously written about the analytics continuum, which spans a range of capabilities including reporting, visualization, planning, real-time processes, natural language processing, artificial intelligence and machine learning. I’ve also written about the analysis that goes into making intelligent decisions with decision intelligence. In this perspective, I’d like to focus on one end of the analytics continuum, which I’ll label advanced analytics.
Topics: Analytics, Digital Technology, AI and Machine Learning, Analytics & Data
Despite the emphasis on organizations being more data-driven and making an increasing proportion of business decisions based on data and analytics, it remains the case that some of the most fundamental questions about an organization are difficult to answer using data and analytics. Ostensibly simple questions such as, “how many customers does the organization have?” can be fiendishly difficult to answer, especially for organizations with multiple business entities, regions, departments and applications. Increasing volumes and sources of data can hinder, rather than help. Only 1 in 5 participants (20%) in Ventana Research’s Analytics and Data Benchmark research are very confident in their organization’s ability to analyze the overall quantity of data. This is a perennial issue that data and application integration vendors, such as SnapLogic, are aiming to address – increasingly through automation and products for business users as well as data management professionals.
Topics: Cloud Computing, Data Management, Data, AI and Machine Learning, data operations, Analytics and Data
Prevedere Provides Predictive Insights with Analytics and Planning
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.
Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning
The Revolution in Revenue in 2023: Ventana Research Market Agenda
Ventana Research recently announced its 2023 research agenda for the Office of Revenue, continuing the guidance we’ve offered for nearly two decades to help organizations realize their optimal value from applying technology to improve business outcomes. Chief Sales and Revenue Officers face an imperative to manage their sales and revenue organizations, but they don’t always have the guidance they need to embrace technology to achieve the best possible outcomes. As we look forward to 2023, we are focusing on the entire selling and buying journey, and in addition focusing on those activities that ensure renewal and expansion as well as newer digital engagement and selling channels. We are looking at applications that simplify processes and tasks across the customer experience, from beginning to end.
Topics: Sales, Analytics, Internet of Things, Data, Sales Performance Management, Digital Technology, Digital Commerce, Conversational Computing, AI and Machine Learning, mobile computing, Subscription Management, extended reality, intelligent sales, partner management, Sales Engagement
The 2023 Market Agenda for CX: Analytics Takes Center Stage
Ventana Research recently announced its Market Agenda in the expertise area of Customer Experience. CX has emerged as a way for organizations to demonstrate value and stand out in the marketplace. The technology underlying modern CX is transitioning from tools that are based on communication to those centered on data analysis and process automation. This allows organizations to build great experiences and reap the benefits in customer loyalty and value. It also forces companies to reckon with the complexity and disruption that technologies like artificial intelligence and automation bring to an organization.
Topics: Customer Experience, Voice of the Customer, CEM, Self-service, Analytics, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service
2023 Digital Technology Market Agenda: Innovation for Digital Agility
I’m proud to share Ventana Research’s 2023 Market Agenda for Digital Technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that improve customer, partner and workforce experiences while also increasing organizational effectiveness and agility.
Topics: Analytics, Cloud Computing, Internet of Things, Data, Digital Technology, blockchain, AI and Machine Learning, mobile computing, extended reality, robotic automation, Collaborative & Conversational Computing
Vertical strategies for enterprise resource planning systems are not new. They emerged more than two decades ago as vendors looked for ways to reduce costs and shorten time-to-value in a software category that was notorious for high costs and extended timelines. A vertical-plus strategy – the plus means it’s a platform, not just an application – takes advantage of recently available technology to extend the ease of implementation and maintenance of the system by having deeper integration with complementary applications, available low-code/no-code customization capabilities and a data pantry that enables the amalgamation of data from multiple sources for situational awareness and decision support. Moreover, the ongoing shift from on-premises to cloud-based ERP systems, especially those designed to address specific types of businesses, will accelerate over the next five years as more configurable and customizable systems designed for specific business verticals become available. A cloud-based platform facilitates the creation of a digital ecosystem that can enable a software vendor’s users to enhance customer experiences.
Topics: Office of Finance, Cloud Computing, ERP and Continuous Accounting, AI and Machine Learning, digital finance
Pyramid Analytics Expands Decision Intelligence Across the Organization
In today’s organization, the myriad of analytics and permutations of dashboards challenge workers’ ability to take contextual actions efficiently. Unfortunately, conventional wisdom for investing in analytics does not recognize the benefits of empowering the workforce to understand the situation, examine options and work together to make the best possible decision.
Topics: business intelligence, Analytics, Data, Digital Technology, AI and Machine Learning, Digital Business, Analytics and Data, Analytic Data Platforms
Oracle Links Analytics and Business Intelligence in the Cloud
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.
Topics: embedded analytics, Analytics, Business Intelligence, natural language processing, AI and Machine Learning
Make Intelligent Decisions with Decision Intelligence
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.
Topics: Analytics, Business Intelligence, Digital Technology, AI and Machine Learning, Analytics & Data
InterSystems Transforming Organizations with Cloud Smart Data Fabric
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.
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
The Data Pantry Accelerates Actionable Analytics for Decision-Making
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.
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
Cloud Computing Realities Part 4 — Security and Governance
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.
Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Digital Technology, AI and Machine Learning, Analytics & Data, Governance & Risk
Recognize and Plan for the AI and Machine Learning Skills Gap
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).
Topics: Analytics, Business Intelligence, Digital Technology, AI and Machine Learning, Analytics & Data
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.
Topics: Business Intelligence, Data, AI and Machine Learning, data operations, Analytic Data Platforms
Sisense is Sensible for Embedded Analytics and BI
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.
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.
Topics: embedded analytics, Analytics, Business Intelligence, IBM, IBM Watson, AI and Machine Learning
The Arguments For, and Against, In-Database Machine Learning
Almost all organizations are investing in data science, or planning to, as they seek to encourage experimentation and exploration to identify new business challenges and opportunities as part of the drive toward creating a more data-driven culture. My colleague, David Menninger, has written about how organizations using artificial intelligence and machine learning (AI/ML) report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats, and improving the bottom line with increased sales and lower costs. One-quarter of participants (25%) in Ventana Research’s Analytics and Data Benchmark Research are already using AI/ML, while more than one-third (34%) plan to do so in the next year, and more than one-quarter (28%) plan to do so eventually. As organizations adopt data science and expand their analytics initiatives, they face no shortage of options for AI/ML capabilities. Understanding which is the most appropriate approach to take could be the difference between success and failure. The cloud providers all offer services, including general-purpose ML environments, as well as dedicated services for specific use cases, such as image detection or language translation. Software vendors also provide a range of products, both on-premises and in the cloud, including general-purpose ML platforms and specialist applications. Meanwhile, analytic data platform providers are increasingly adding ML capabilities to their offerings to provide additional value to customers and differentiate themselves from their competitors. There is no simple answer as to which is the best approach, but it is worth weighing the relative benefits and challenges. Looking at the options from the perspective of our analytic data platform expertise, the key choice is between AI/ML capabilities provided on a standalone basis or integrated into a larger data platform.
Topics: Data Governance, Data Management, Data, AI and Machine Learning, data operations, Analytics and Data, Analytic Data Platforms
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.
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 Enables Agility Based on Insight
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.
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
Databricks Lakehouse Platform Maximizes Analytical Value
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.
Topics: Business Intelligence, Data Governance, Data Management, Data, AI and Machine Learning, Streaming Data & Events, Analytic Data Platforms
IBM’s Cloud Pak for Data Builds a Foundation for Data Fabric
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.
Topics: Business Intelligence, Cloud Computing, Data Governance, Data Management, Data, AI and Machine Learning, data operations, operational data platforms
Orchestrating Data Pipelines Facilitates Data-Driven Analytics
I have written a few times in recent months about vendors offering functionality that addresses data orchestration. This is a concept that has been growing in popularity in the past five years amid the rise of Data Operations (DataOps), which describes more agile approaches to data integration and data management. In a nutshell, data orchestration is the process of combining data from multiple operational data sources and preparing and transforming it for analysis. To those unfamiliar with the term, this may sound very much like the tasks that data management practitioners having been undertaking for decades. As such, it is fair to ask what separates data orchestration from traditional approaches to data management. Is it really something new that can deliver innovation and business value, or just the rebranding of existing practices designed to drive demand for products and services?
Topics: Data Management, Data, AI and Machine Learning, data operations, Analytics & Data
MLOps: A Disciplined Approach That Increases Organizational Agility
Artificial intelligence and machine learning are valuable to data and analytics activities. Our research shows that organizations using AI/ML report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats and improving the bottom line with increased sales and lower costs. No wonder nearly 9 in 10 (87%) research participants report using AI/ML or planning to do so.
Topics: Analytics, AI and Machine Learning
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.
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
Celonis Improves Business Processes with Process Mining
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.
Topics: Analytics, Business Intelligence, AI and Machine Learning, Process Mining, Streaming Analytics
Aerospike Has a Data Platform for Real-Time Intelligent Applications
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.
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.
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.
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.
Topics: Business Intelligence, Data Governance, Data Management, AI and Machine Learning, data operations
Kinaxis Extends Platform into Supply Chain Execution with MPO Acquisition
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.
Topics: Business Intelligence, Business Planning, Operations & Supply Chain, Enterprise Resource Planning, AI and Machine Learning, continuous supply chain
Initiatives using AI Improve Marketing and Sales Effectiveness
I have written about vendor efforts to use artificial intelligence (AI) and advanced analytics in their applications targeted at sales and revenue teams to improve focus and prioritize activities, both for pipeline management as well as individual opportunities. Since then, vendors have continued to innovate, and there have been more releases showcasing efforts to aid sales and revenue. And with this continuing innovation, we believe that by 2026, two-thirds of revenue leaders will begin considering a new generation of revenue analytics and data-driven applications designed to improve performance and productivity.
Topics: AI and Machine Learning, Revenue Management, Sales Engagement, Office of Revenue
Augmented Intelligence Reduces Dependency on AI/ML Skill Sets
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.
Topics: Analytics, Business Intelligence, natural language processing, AI and Machine Learning, Analytics & Data, Collaborative & Conversational Computing
External Data Supports a More Predictive Finance Department
Organizations do not live in a vacuum and things happening outside their walls have a direct impact on how they perform. So, it is essential for them to incorporate external data in their forecasting, planning and budgeting, especially for predictive analytics and machine learning (ML) to support artificial intelligence (AI). I use the term external data to include any information about the world outside an organization (including economic and market statistics), competitors (such as pricing and locations), and customers. Until recently, it was adequate for organizations to regard external data is a “nice to have” item, but that is no longer the case. External data is necessary for many functions, including useful and accurate competitive intelligence used by sales and marketing groups. It is also essential for the effective applications of AI using ML for business-focused planning and budgeting and predictive analytics.
Topics: Office of Finance, Business Planning, Financial Performance Management, AI and Machine Learning, digital finance
Zoho presented analysts with a deep look at its strategy and roadmap at its July analyst conference, describing how it intends to meld its many business applications together through integration at the level of the platform. The company, which is privately owned and funded, has generally sought to build its own tools rather than buy or partner. This approach has allowed the firm to create a suite of tightly linked tools that share a common interface.
Topics: Customer Experience, Voice of the Customer, Data, AI and Machine Learning, Digital Business, Customer Experience Management, customer service and support
Palantir Operationalizes Analytics and Data for Actions and Decisions
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.
Topics: embedded analytics, Business Intelligence, Internet of Things, AI and Machine Learning, Streaming Analytics
Qlik Advances Self-Service Analytics and Business Intelligence
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.
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.
Topics: Office of Finance, Continuous Planning, Business Intelligence, Business Planning, Financial Performance Management, AI and Machine Learning, continuous supply chain, digital finance, profitability management
Neo4j Expands Data Science Focus with New Managed Service
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.
Topics: Business Intelligence, Data, AI and Machine Learning, operational data platforms, Analytic Data Platforms
Expanding the Analytics Continuum: From Analysis to Action
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.
Topics: Business Intelligence, natural language processing, AI and Machine Learning, Streaming Analytics, Analytics & Data
Tableau Brings Business Intelligence to Business Users
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.
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.
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
OneStream’s Sensible ML Tasks AI for Business Planning
OneStream offers a platform designed to serve the needs of accounting and financial planning and analysis organizations. The software handles financial close and consolidation, planning and budgeting, analysis and reporting. For me, the most significant announcement at the company’s recent user conference was the unveiling of its Sensible ML (Machine Learning) offering, which is in limited general release. I’ve commented on the importance of artificial intelligence in business applications, and Sensible ML is a promising and important step in that direction.
Topics: Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance, profitability management
TigerGraph Promotes Graph Database for Data Science with ML Workbench
I recently wrote about the growing range of use cases for which NoSQL databases can be considered, given increased breadth and depth of functionality available from providers of the various non-relational data platforms. As I noted, one category of NoSQL databases — graph databases — are inherently suitable for use cases that rely on relationships, such as social media, fraud detection and recommendation engines, since the graph data model represents the entities and values and also the relationships between them. The native representation of relationships can also be significant in surfacing “features” for use in machine learning modeling. There has been a concerted effort in recent years by graph database providers, including TigerGraph, to encourage and facilitate the use of graph databases by data scientists to support the development, testing and deployment of machine learning models.
Topics: business intelligence, Analytics, Cloud Computing, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data
A Data Pantry Speeds Development of Machine Learning Models
A few years ago – somewhat tongue in cheek – I began using the term “data pantry” to describe a type of data store that’s part of a business application platform, created for a specific set of users and use cases. It’s a data pantry because, unlike a general-purpose data store such as a data warehouse, everything the user needs is readily available and easily accessible, with labels that are immediately recognized and understood.
Topics: Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance
Organizations are continuously increasing the use of analytics and business intelligence to turn data into meaningful and actionable insights. Our Analytics and Data Benchmark Research shows some of the benefits of using analytics: Improved efficiency in business processes, improved communication and gaining a competitive edge in the market top the list. With a unified BI system, organizations can have a comprehensive view of all organizational data to better manage processes and identify opportunities.
Topics: business intelligence, embedded analytics, Data Governance, Data Management, natural language processing, AI and Machine Learning, data operations, Streaming Analytics, operational data platforms
Ahana Offers Managed-Services Approach to Simplify Presto Adoption
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.
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.
Topics: Business Intelligence, Data Management, AI and Machine Learning, data operations, Analytics & Data, semantic model
AI Will Create Strategic Advantage for the Office of Finance
Artificial intelligence using machine learning has passed through the bright, shiny object stage and software vendors are well into the process of making the concept a reality in their offerings. Ventana Research defines AI as the use of technology to process information in much the way humans do, including improving accuracy in recommendations, actions and conclusions as more data is received. I like 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 eliminating the need to perform repetitive work.
Topics: Planning, Machine Learning, Budgeting, Business Planning, Financial Performance Management, forecasting, AI and Machine Learning, digital finance, profitability management
Disentangling and Demystifying Data Mesh and Data Fabric
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.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
SingleStore Positions Hybrid Data Processing for Data Intensity
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.
Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data
Data and Analytics Processes: Can We Get Personal?
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.
Topics: Business Intelligence, Data Management, natural language processing, AI and Machine Learning, data operations, Analytics & Data
Oracle Positions to Address Any and All Data Platform Needs
I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.
Topics: business intelligence, Analytics, Data Integration, Data, AI and Machine Learning, data platforms
Denodo Advancing Data Virtualization in the Cloud
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%).
Topics: embedded analytics, Analytics, Business Intelligence, AI and Machine Learning, Streaming Analytics
Real-Time Data Processing Requires More Agile Data Pipelines
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.
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
Don’t Rely on Dashboards for Real-Time Analytics
I have written previously that the world of data and analytics will become more and more centered around real-time, streaming data. Data is created constantly and increasingly is being collected simultaneously. Technology advances now enable organizations to process and analyze information as it is being collected to respond in real time to opportunities and threats. Not all use cases require real-time analysis and response, but many do, including multiple use cases that can improve customer experiences. For example, best-in-class e-commerce interactions should provide real-time updates on inventory status to avoid stock-out or back-order situations. Customer service interactions should provide real-time recommendations that minimize the time to resolution. Location-based offers should be targeted at the customer’s current location, not their location several minutes ago. Another domain where real-time analyses are critical is internet of things (IoT) applications. Additionally, use cases like predictive maintenance require timely information to prevent equipment failures that help avoid additional costs and damage.
Topics: business intelligence, Analytics, Internet of Things, Data, Digital Technology, AI and Machine Learning, Streaming Analytics, Analytics & Data, Streaming Data & Events
Working Across the Aisle in Analytics: Involving IT and LOB
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.
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.
Topics: Analytics, Business Intelligence, Data Governance, Data, AI and Machine Learning, data operations, data platforms
Evolving NoSQL Database Functionality Fuels Adoption
The various NoSQL databases have become a staple of the data platforms landscape since the term entered the IT industry lexicon in 2009 to describe a new generation of non-relational databases. While NoSQL began as a ragtag collection of loosely affiliated, open-source database projects, several commercial NoSQL database providers are now established as credible alternatives to the various relational database providers, while all the major cloud providers and relational database giants now also have NoSQL database offerings. Almost one-quarter (22%) of respondents to Ventana Research’s Analytics and Data Benchmark Research are using NoSQL databases in production today, and adoption is likely to continue to grow. More than one-third (34%) of respondents are planning to adopt NoSQL databases within two years (21%) or are evaluating (14%) their potential use. Adoption has been accelerated by the evolving functionality offered by NoSQL products and services, the growing maturity of specialist NoSQL vendors, and new commercial offerings from cloud providers and established database providers alike. This evolution is exemplified by the changing meaning of the term NoSQL itself. While it was initially associated with a rejection of the relational database hegemony, it has retroactively been reinterpreted to mean “Not Only SQL,” reflecting the potential for these new databases to coexist with and complement established approaches.
Topics: Analytics, Data, AI and Machine Learning, data platforms
Data Observability is Key to Ensuring Healthy Data Pipelines
I recently described the emergence of hydroanalytic data platforms, outlining how the processes involved in generating energy from a lake or reservoir were analogous to those required to generate intelligence from a data lake. I explained how structured data processing and analytics acceleration capabilities are the equivalent of turbines, generators and transformers in a hydroelectric power station. While these capabilities are more typically associated with data warehousing, they are now being applied to data lake environments as well. Structured data processing and analytics acceleration capabilities are not the only things required to generate insights from data, however, and the hydroelectric power station analogy further illustrates this. For example, generating hydroelectric power also relies on pipelines to ensure that the water is transported from the lake or reservoir at the appropriate volume to drive the turbines. Ensuring that a hydroelectric power station is operating efficiently also requires the collection, monitoring and analysis of telemetry data to confirm that the turbines, generators, transformers and pipelines are functioning correctly. Similarly, generating intelligence from data relies on data pipelines that ensure the data is integrated and processed in the correct sequence to generate the required intelligence, while the need to monitor the pipelines and processes in data-processing and analytics environments has driven the emergence of a new category of software: data observability.
Topics: Analytics, Data Governance, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms, Streaming Data & Events
The use of artificial intelligence (AI) using machine learning (ML) will be the single most important trend in business software this decade because it can multiply the investment value of such applications and provide vendors an important source of differentiation to achieve a competitive advantage in what are today very mature software categories. I assert that by 2025, almost all Office of Finance software vendors will have incorporated some AI capabilities to reduce workloads and improve performance. However, software vendors will be challenged to apply innovations in this area quickly while ensuring that the AI capabilities function well enough in the real world to foster rapid adoption while avoiding user frustration. The failures of the Apple Newton and Microsoft’s Clippy office assistant stand out as examples of too-ambitious-too-soon attempts at infusing intelligent automation.
Topics: Office of Finance, embedded analytics, Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning, digital finance
Incorta Unifies Data Processing to Accelerate Analytics & BI
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.
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.
Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data, Streaming Data & Events
AtScale Universal Semantic Layer Democratizes and Scales Analytics
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.
Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, AI and Machine Learning, data operations, Streaming Analytics
The Office of Finance Market Agenda for 2022: Effectiveness and Profits
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.
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
The 2022 Market Agenda for Office of Revenue: New Performance Priority
Ventana Research recently announced its 2022 Market Agenda for the Office of Revenue, continuing the guidance we have offered for nearly two decades to help organizations realize optimal value from applying technology to improve business outcomes. Chief sales and revenue officers and their associated operations teams are experts in their respective fields but may not have the guidance needed to employ technology effectively. As we look to 2022, we are focusing on the entire selling and buying life cycle and the applications that simplify and improve interactions throughout the customer experience.
Topics: Sales, Analytics, Internet of Things, Data, Sales Performance Management, Digital Technology, Digital Commerce, Conversational Computing, AI and Machine Learning, mobile computing, Subscription Management, extended reality, intelligent sales, partner management, Sales Engagement
ThoughtSpot Enables Simpler Analytics with AI and NLP
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.
Topics: embedded analytics, Analytics, Business Intelligence, Data Integration, Data, natural language processing, data lakes, AI and Machine Learning, data operations, data platforms
The internet is a rich source of information and is used by buyers to research new applications and offerings well before ever engaging a vendor and salesperson. Along with massive growth in offerings, this is a major reason why sales teams are facing increasing challenges to successfully sell and attain targets.
Topics: Sales, AI and Machine Learning, Revenue Management, Sales Engagement
Hydroanalytic Data Platforms Power Data Lakes’ Strategic Value
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.
Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms
Data Platforms Landscape Divided Between Analytic and Operational
As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting analytic workloads for almost as long as there has been a database market.
Topics: business intelligence, Analytics, Data, data lakes, AI and Machine Learning, data operations, data platforms
Why Revenue Planning Should be Continuous and Year Round
With the emergence of multiple selling channels and the rise of the subscription model, the need for a unified approach to revenue planning and execution should be a priority for every organization. As I have written about in my analyst perspective Revenue Management: The Opportunity for Innovation and Optimization, this need to unify the approach and focus on alignment across all revenue supporting teams in furtherance of an organization’s objectives and targets is of key importance to ensure that teams handle different aspects of a customer’s journey and experience. And, as I will further discuss, this alignment between groups is rarely a happy accident but rather the result of forward-looking, continuous planning.
Topics: Sales, Customer Experience, Sales Performance Management, AI and Machine Learning, Subscription Management, Revenue Management, Sales Engagement
Automating Workflows for a Better Customer Experience
Any organization that relies heavily on a large labor force looks to automation to reduce costs, and contact centers are no exception. They handle interactions at such large scale that almost any effort to automate some part of the process can deliver measurable efficiencies. Two factors have ratcheted up attention on automating customer experience workflows: the dramatic expansion of digital interaction channels, and the development of artificial intelligence and machine learning tools to facilitate workflow deployment.
Topics: Customer Experience, Voice of the Customer, Analytics, Data Integration, Contact Center, Data, AI and Machine Learning, agent management, data operations, Digital Business, Experience Management, Customer Experience Management, Field Service
TIBCO Broadens Portfolio for Improved Analytics Efficiency
TIBCO is a large, independent cloud-computing and data analytics software company that offers integration, analytics, business intelligence and events processing software. It enables organizations to analyze streaming data in real time and provides the capability to automate analytics processes. It offers more than 200 connectors, more than 200 enterprise cloud computing and application adapters, and more than 30 non-relational structured query language databases, relational database management systems and data warehouses.
Topics: embedded analytics, Analytics, Collaboration, Data Governance, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
Talend Data Fabric Simplifies Data Life Cycle Management
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.
Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
Vonage Buys Into Conversational Commerce With Jumper.ai
When migrating their communications stacks to the cloud, many organizations come face to face with a quandary: do they emphasize the business phone system and gravitate toward a unified communications vendor? Or should they focus on the specific applications needed for running their contact centers and seek out a CCaaS vendor?
Topics: Customer Experience, Voice of the Customer, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service
Enterprises looking to adopt cloud-based data processing and analytics face a disorienting array of data storage, data processing, data management and analytics offerings. Departmental autonomy, shadow IT, mergers and acquisitions, and strategic choices mean that most enterprises now have the need to manage data across multiple locations, while each of the major cloud providers and data and analytics vendors has a portfolio of offerings that may or may not be available in any given location. As such, the ability to manage and process data across multiple clouds and data centers is a growing concern for large and small enterprises alike. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research study are using cloud computing for analytics and data, of which 42% are currently using more than one cloud provider.
Topics: Analytics, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, AI and Machine Learning, data operations, data platforms
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?
Topics: Analytics, Business Intelligence, Internet of Things, Data, Digital Technology, AI and Machine Learning, Streaming Analytics
NICE CXi Is a Pivot to the Post-Contact Center World
When NICE acquired inContact in 2016, it began a transformation that saw it broaden its product offering and positioned itself to play a larger role in the contact center and customer experience industries. It was a prescient move, creating a firm that could supply end-to-end contact center functionality in the cloud. And it anticipated today’s market dynamic, in which NICE and its competitors are racing to define (and capitalize on) the post-contact center future.
Topics: Customer Experience, Voice of the Customer, Business Continuity, Analytics, Contact Center, Data, Digital transformation, AI and Machine Learning, agent management, Digital Business, Experience Management, Customer Experience Management, Field Service, customer service and support
AI’s Value to Contact Centers: What Are the Use Cases
In part one of this Analyst Perspective on the use of artificial intelligence within contact center applications, we focused on the evolution — and resulting benefits — of tools embedded with AI, including ease-of-use for non-data-scientists.
Topics: Customer Experience, Voice of the Customer, Analytics, AI and Machine Learning, agent management, Customer Experience Management, Field Service, customer service and support
Databricks Lakehouse Platform Streamlines Big Data Processing
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, Data, data lakes, AI and Machine Learning
The Digital Awakening of Business Process Intelligence
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.
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
AI’s Value to Contact Centers: Improved Customer and Agent Experiences
When artificial intelligence emerged from the labs and vendors started offering it as a component of their software, many contact-center buyers shied away from it. From their point of view, AI and machine learning tools were new, expensive, relatively untested and had an uncertain use case. This stance was understandable, as contact center professionals are traditionally expected to be risk-averse when deploying technology into their operations. Contact centers are, by design, supposed to be hardened, mission-critical sites of high reliability. There has historically been a bias towards avoiding new technology, deploying only when it has been thoroughly vetted across the industry.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, AI and Machine Learning, agent management, Customer Experience Management, Field Service, customer service and support
Use External Data Platform to Improve 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.
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, AI and Machine Learning
Collibra Brings Effective Data Governance to Line-of-Business
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.
Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
Sisu Optimizes Analytics with Machine Learning for Actions & Decisions
Sisu Data is an analytics platform for structured data that uses machine learning and statistical analysis to automatically monitor changes in data sets and surface explanations. It can prioritize facts based on their impact and provide a detailed, interpretable context to refine and support conclusions. The product features fact boards, annotations and the ability to share facts and analysis across teams. Data teams and analysts start by creating common definitions of key performance indicators, which Sisu then utilizes to automatically test thousands of hypotheses to identify differences between groups.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data, AI and Machine Learning
BillingPlatform Bolsters the Rise of Subscription Services
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.
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
Customer support operations increasingly rely on automation and complex workflow processes to reduce costs and improve experiences. Automation also allows organizations to make their service processes richer, incorporating information and staff from back offices, for example, or embedding conversational tools into contact center processes.
Topics: Customer Experience, embedded analytics, Analytics, Contact Center, natural language processing, AI and Machine Learning, agent management, Customer Experience Management, Field Service, Process Mining, Streaming Analytics, customer service and support
Rapidminer Platform Supports Entire Data Science Lifecycle
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Preparation, Data, data lakes, AI and Machine Learning
Confluent Helps Organizations Tackle Streaming Data
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data, AI and Machine Learning
Expert.ai Earns 2021 Digital Innovation Award for Digital Technology
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.
Topics: Customer Experience, Analytics, Internet of Things, Digital Technology, blockchain, natural language processing, Awards, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
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.
Topics: Continuous Planning, Analytics, Product Information Management, Price and Revenue Management, Digital Technology, Operations & Supply Chain, Enterprise Resource Planning, Conversational Computing, AI and Machine Learning, collaborative computing, continuous supply chain, work experience management
Informatica Earns Data Digital Innovation Award for 2021
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.
Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, blockchain, data lakes, AI and Machine Learning
Dialpad provides contact center and business phone services, a market that is in transition due to a convergence of technologies and business conditions.
Topics: Customer Experience, Voice of the Customer, Analytics, Collaboration, Contact Center, natural language processing, AI and Machine Learning, agent management
Sisense Earns Analytics Digital Innovation Award for 2021
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Digital Technology, natural language processing, AI and Machine Learning
CommerceIQ Earns Office of Sales Digital Innovation Award for 2021
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.
Topics: Sales, Analytics, Product Information Management, Digital Commerce, 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.
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
Palantir Earns Overall Digital Innovation Award for 2021
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.
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
Varicent Advances SPM and New Revenue Intelligence
As mentioned in my Analyst Perspective, Revenue Performance Management: Leadership and Operations for Optimal Outcomes, there is continuing pressure on sales leaders to deliver against sales targets in increasingly competitive markets. Among the various levers that sales leadership can use to support these efforts, are applications and processes that best position sales teams to achieve targets, such as planning and allocating territories, establishing quotas and devising incentive compensation plans supportive of organizational revenue goals. Once in place, continuous monitoring of lead-to-opportunity progress and pipeline health can aid in identifying areas for improvement as well as solidifying sales forecasts to better indicate gap-to-target issues and necessary adjustments to territories and compensation incentives.
Topics: Sales, Analytics, Sales Performance Management, Price and Revenue Management, AI and Machine Learning, sales enablement
ServiceNow Brings Customer Workflows with Automation and Intelligence
Customer Service & Support (CSS) is a software segment that provides tools for tracking and resolving customer problems, primarily through contact centers. The segment has been mature for decades but today is reinvigorated by a new emphasis on workflows and automation. Vendors, like ServiceNow, have been innovative in developing new technologies for managing self-service and field service, and providing agents with contextually relevant information during interactions. The new technologies brought to bear on this include artificial intelligence (AI) for knowledge search and delivery; agent assist and guidance tools; and SMS-centric customer messaging.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, Product Information Management, Digital Commerce, AI and Machine Learning, Subscription Management, agent management
Amazon is a Vendor of Merit in 2021 Value Index for Analytics and Data
We are happy to share some insights about Amazon QuickSight drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Google is a Vendor of Merit in 2021 Value Index for Analytics and Data
We are happy to share some insights about Google Looker drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
ThoughtSpot Earns Merit Ranking in 2021 Value Index
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
TIBCO Rated with Merit in 2021 Value Index in Analytics and Data
We are happy to share some insights about TIBCO Spotfire drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
Sisense is Vendor of Merit for Analytics and Data Value Index
We are happy to share some insights about Sisense drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Infor Gets Assurance Rating for Analytics and Data Value Index
We are happy to share some insights about Infor Birst drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Preparation, Data, Information Management (IM), natural language processing, AI and Machine Learning
2021 Value Index for Analytics Rates Microsoft with Assurance
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Salesforce Tableau has Assurance in 2021 Value Index on Analytics
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
SAS Rated with Assurance in Analytics and Data Value Index
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Teradata Expands Vantage Enterprise Data Platform
Teradata introduced some enhancements to its Vantage platform last year in which they expanded its analytics functions and language support, and strengthened tools to improve collaboration between data scientists, business analysts, data engineers and business personnel. Some of the key enhancements included expanding the native support for R and Python, extending the ability to execute a wide range of open-source analytics algorithms, and automatic generation of SQL from R and Python code. These updates are included to reduce data silos, enabling a wide range of data and analytics personas to collaboratively run complex analytics in a self-service manner.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Preparation, Information Management, Data, AI and Machine Learning
SAP Excels in Value Index for Analytics with its Customer Experience
We are happy to share some insights about SAP drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, natural language processing, AI and Machine Learning
Sales Forecasting: Have the Process and Technology for a True Revenue Forecast?
There has been a lot of market activity around vendors offering sales-forecasting products (or functionality to address sales forecasting) as part of a wider technology offering for sales and revenue management. As I have discussed in my Analyst Perspective: The Art and Science of Sales from the Inside Out, the pandemic accelerated the prior trends that are now forcing sales leaders and sales teams to reexamine traditional notions of how B2B sales are conducted. In addition, with the rise of the subscription business model and digital e-commerce, a more holistic approach to identify where revenue is coming from and how to manage and optimize a predictable revenue stream is becoming a pressing need. I cover the basic premise of this management of revenue streams in my Analyst Perspective: Revenue Management: The Opportunity for Innovation and Optimization.
Topics: Sales, Office of Finance, Analytics, Business Planning, Sales Performance Management, Price and Revenue Management, AI and Machine Learning
Board International Gains Exemplary Vendor Ranking in Value Index
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Yellowfin is Innovative Leader in Mobile and Collaborative Analytics
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Revenue Performance Management: Leadership and Operations for Optimal Outcomes
As laid out in my recent Analyst Perspective, Revenue Management: The Opportunity for Innovation and Optimization, revenue management is a new way look at generating and managing the top line. It unifies multiple sources: the traditional focus on new customers to existing customers as well as all types of revenue from new, additional channels. This could include customer retention, upsell and cross sell, in addition to other selling channels such as through partners or digital sales channels like e-commerce and subscriptions.
Topics: Sales, Analytics, Sales Performance Management (SPM), Price and Revenue Management, Digital Commerce, AI and Machine Learning, Subscription Management
Domo Rated Exemplary and Collaborative Leader in 2021 Value Index on Analytics and Data
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Oracle Earns Innovative Vendor Rating in 2021 Analytics and Data Value Index
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
Qlik is Exemplary and Value Index Leader in Customer Experience for 2021 Value Index for Analytics and Data
We are happy to share some insights about Qlik drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), natural language processing, AI and Machine Learning
TIBCO Information Builders is Named an Innovative Vendor in 2021 Value Index
We are happy to share some insights about Information Builders’ WebFOCUS Business Intelligence and Analytics Platform drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
MicroStrategy Earns Value Index Rating of Exemplary in Analytics and Data
We are happy to share some insights about MicroStrategy drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
IBM is Exemplary and Leader in Analytics and Data Value Index
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, natural language processing, AI and Machine Learning
Customer Service and Support: Expanded Role and Need for Software
Customer service and support (CSS) is a term with two meanings. Most generally, it refers to the functions of a contact center in handling post-sales customer inquiries that require some effort or action on the part of the business. More specifically, it refers to the elements of the software stack that facilitate those operations, primarily case tracking and trouble ticketing.
Topics: Customer Experience, Analytics, Contact Center, AI and Machine Learning, agent management
Alation Helps Organizations Get More Value From Data
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.
Topics: Analytics, Business Intelligence, Collaboration, Data Preparation, Data, Information Management (IM), AI and Machine Learning
Unit4 Democratizes Agile, Data-Driven Decision-Making
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.
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
The Science of Sales Professional Effectiveness
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.
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
SugarCRM Brings the Sweetness in AI Driven Sales
As I have discussed in my Analyst Perspective, The Art of Sales, from the Inside Out, the challenges facing direct sales leaders are not going away. Declining quota attainment, lack of visibility into deal health and difficulty in forecasting quarterly sales remain a challenge for sales leaders, resulting in a continuing reduction in duration of tenure.
Topics: Sales, Analytics, Sales Performance Management, Price and Revenue Management, AI and Machine Learning, sales enablement
The Voice of the Customer Is Really a Chorus of Voices
Voice of the Customer (VoC) is a catch-all term that refers to the collection of customer feedback in various formats. Sometimes this feedback is in the form of quick surveys or reactions to questions like, "Did I resolve your issue today?" or "Would you recommend our service to a friend?" Alternatively, it can be derived from less specific but more numerous data signals that span multiple interactions or across a customer base. Most businesses make an effort to capture some customer feedback.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, AI and Machine Learning, agent management
Organizations are becoming more and more data-driven and are looking for ways to accelerate the usage of artificial intelligence and machine learning (AI/ML). Developing and deploying AI/ML models can be complicated in many ways, often involving different tools and services to manage these solutions from end to end. Accessing and preparing data is the most common challenge organizations face in this process, and consequently, AI/ML vendors typically incorporate tools to address this part of the process. But there are many other steps in the process as well, such as coordinating the handoff between data scientists and IT or software engineers for deployment to production. This can potentially slow down the entire data-to-insights process. End-to-end platforms for AI offer the promise of simplifying these processes, allowing teams that work with data to improve organizational results.
Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
IBM Planning Analytics Makes Planning Easier for Business Unit Leaders
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.
Topics: Office of Finance, embedded analytics, Analytics, Business Intelligence, Collaboration, Business Planning, ERP and Continuous Accounting, Predictive Planning, AI and Machine Learning
Process Mining: Improve Execution and Operations with Analytics
Process-mining software isn’t exactly new, but it’s also not widely known in the software technology market. The discipline has been around for at least a decade, but is generating more interest these days with both specialist vendors and major enterprise software vendors offering process-mining products and services. We assert that through 2022, 1 in 4 organizations will look to streamline their operations by exploring process mining.
Topics: business intelligence, Analytics, Digital Technology, 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.
Topics: Sales, Customer Experience, Analytics, Business Intelligence, Product Information Management, Price and Revenue Management, Digital Commerce, AI and Machine Learning
Informatica Continues to Evolve Data Management Platform
Organizations are accelerating their digital transformation and looking for innovative ways to engage with customers in this new digital era of data management. The goal is to understand how to manage the growing volume of data in real time, across all sources and platforms, and use it to inform, streamline and transform internal operations. Over the years, the adoption of cloud computing has gained momentum with more and more organizations trying to make use of applications, data, analytics and self-service business intelligence (BI) tools running on top of cloud-computing infrastructure in order to improve efficiency. However, cloud adoption means living with a mix of on-premises and multiple cloud-based systems in a hybrid computing environment. The challenge is to ensure that processes, applications and data can still be integrated across cloud and on-premises systems. Our research shows that organizations still have a significant requirement for on-premises data management but also have a growing requirement for cloud-based capabilities.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Information Management, Internet of Things, Data, natural language processing, AI and Machine Learning
2021 Analytics and Data Value Index: Market Observations and Perspective
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.
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
The pandemic accelerated several trends in the contact-center industry that were already underway, chiefly: moving infrastructure and software applications to the cloud, and rethinking the process of managing agents. One byproduct of these trends is a renewed look at the similarities between business-phone systems (also known as unified communications, or UC) and contact center systems (CC).
Topics: Customer Experience, Analytics, Collaboration, Contact Center, AI and Machine Learning, agent management
Salesforce Sales Cloud: Evolving to Meet the 360 Needs of Sales
There is no doubt that the pandemic has accelerated the existing need for new technology that can help sales professionals do their jobs well in this quickly evolving market. In addition, market trends are driving the need for functionality that is aimed at the front-line sales professional and the manager, highlighting the demand for tools that can help arrest the decline in quota attainment, as well as helping salespeople supplement their traditional focus on sales quotas with activities such as prospecting.
Topics: Sales, embedded analytics, Analytics, Internet of Things, Sales Performance Management, natural language processing, AI and Machine Learning, sales enablement
Microsoft Azure: Cloud Computing for Data and Analytics
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Lake, Data Preparation, Data, AI and Machine Learning, Microsoft Azure
The current pandemic has disrupted many of the traditional sales methods used by field-sales organizations to engage, and sell to, buyers. In an effort to provide help, many vendors have recently announced new features that focus less on the management of sales organizations and more on tools to help salespeople sell. This has been coupled with a renewed interest in using data to help with the science, alongside the art, of selling, as referenced in my AP: The Art and Science of Sales from the “Inside Out". Oracle has called this new emphasis “Responsive Selling,” with an aim to harness data and machine learning (ML) to aid sellers in this new, challenging environment.
Topics: Sales, Analytics, Data, Product Information Management, Sales Performance Management (SPM), Digital Technology, AI and Machine Learning, sales enablement, Sales Engagement
The Office of Finance Market Agenda for 2021: Accelerating Adoption of Digital Technology
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.
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
The 2021 Market Agenda for Office of Sales: The Revolution for Revenue
Ventana Research recently announced its 2021 research agenda for the Office of Sales, continuing the guidance we’ve offered for nearly two decades to help organizations realize optimal value from applying technology to improve business outcomes. Chief sales and revenue officers are experts in their respective fields but may not have the guidance needed to employ technology effectively. As we look to 2021, we are focusing on the entire selling and buying journey and the applications that simplify interactions throughout the customer experience.
Topics: Sales, Analytics, Financial Performance, Internet of Things, Data, Sales Performance Management, Digital Technology, Digital Commerce, AI and Machine Learning, mobile computing, Subscription Management, extended reality, intelligent sales, partner management, Office of Sales, Machine Conversational Computing, Sales Engagement
The Digital Technology Market Agenda for 2021: Predictability in Unpredictable Times
I’m proud to share Ventana Research’s 2021 market agenda for digital technology. Our focus in this agenda is to deliver expertise to help organizations prioritize technology investments that increase workforce effectiveness and organizational agility, ensuring ongoing operation during any type of disruption.
Topics: Big Data, Analytics, Cloud Computing, Internet of Things, Digital Technology, Robotic Process Automation, blockchain, Conversational Computing, AI and Machine Learning, mobile computing, extended reality
The 2021 Market Agenda for Customer Experience: Achieving Excellence in Engagement
Ventana Research recently announced its 2021 market agenda in the expertise area of Customer Experience. Most organizations have some degree of focus on managing how they interact with their customers, but it is often a disjointed and constrained process. Developing an effective customer experience has become an investment priority in recent years as organizations increasingly recognize the importance of good experiences to profitability, customer longevity and advocacy on behalf of brands.
Topics: Sales, Customer Experience, Marketing, Voice of the Customer, Analytics, Customer Service, Contact Center, Workforce Management, Digital Marketing, Digital Commerce, AI and Machine Learning, agent management, Customer Experience Management, Field Service
The 2021 Market Agenda for Analytics: Converting Data Into Insights
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.
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.
Topics: Sales, Customer Experience, Marketing, Analytics, Business Intelligence, Data Preparation, Digital Technology, AI and Machine Learning
BlackLine Matches Receivables for Continuous Accounting
BlackLine recently held its first virtual user conference, Beyond the Black, where it detailed numerous additions and enhancements to its applications. Of note was the launch of BlackLine Cash Application, an accounts receivable (AR) processing software based on software originally developed by recently acquired Rimilia. The new application fits the company's product strategy of providing accounting departments with software that automates time-consuming repetitive tasks and substantially reduces the amount of detail that individuals must handle in performing core processes.
Topics: Office of Finance, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning
Continuous Planning is Optimized with AI and External Data
In the context of planning, budgeting and benchmarking, external data includes information about the world outside an organization such as economic and market statistics, competitors and customers. Today, a comprehensive set of external data is a “nice to have” item in most organizations, but that’s likely to change. External data is necessary for useful and accurate business-focused planning and budgeting, and for performance benchmarking. It is also essential for the effective applications of artificial intelligence (AI) to these functions.
Topics: Information Management, Business Planning, Financial Performance Management, Predictive Planning, AI and Machine Learning, digital finance
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.
Topics: PROS Pricing, embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, data lakes, AI and Machine Learning
Tableau and Salesforce bring New Look to Business Analytics
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.
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Data, data lakes, AI and Machine Learning
Organizations are always looking to improve their ability to use data and AI to gain meaningful and actionable insights into their operations, services and customer needs. But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. Organizations still struggle with limited data visibility and insufficient insights, which are often caused by a multitude of reasons such as analytic workloads running independently, data spread across multiple data centers, data governance, etc. In our ongoing benchmark research project, we are researching the ways in which organizations work with big data and the challenges they face.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes, AI and Machine Learning
Intelligent Virtual Agents Are an Imperative for Digital Self-Service
The pandemic has raised the stakes for self-service in every part of the customer journey. In 2020, the customer service industry underwent a shock to its collective system by pulling up stakes and moving agents to remote work. At the same time, consumers moved away from in-person interactions in stores and branches. This systemic disruption has led to longer call wait times and tougher interactions because collaborating and accessing company data systems from outside the office is difficult.
Topics: Customer Experience, Contact Center, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning, agent management
Can you imagine a more arcane and boring topic than accounts receivable? Unless you are the CFO, controller, chief accounting officer or treasurer of an organization, maybe not. Anecdotally, as it’s part of the trend to the digital transformation of all things in the department, there appears to be greater interest in this area of the Office of Finance. With populations locked down and the accounting staff unable to work in an office, the need to operate virtually has accelerated the application of technology to finance and accounting departments, which has been long overdue.
Topics: Office of Finance, Financial Performance Management, ERP and Continuous Accounting, robotic finance, AI and Machine Learning
Teradata Vantage CX: The Real Customer Data Platform
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.
Topics: Customer Experience, Marketing, Analytics, Business Intelligence, Contact Center, Data, Digital Marketing, Digital Commerce, AI and Machine Learning, intelligent marketing
The Art and Science of Sales from the “Inside Out"
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.
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
Molecula Earns Ventana Research 13th Digital Innovation Award for Data
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.
Topics: Analytics, Collaboration, Data Governance, Data Lake, Data Preparation, IOT, Data, Information Management (IM), Digital Technology, blockchain, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
Creating Mutual and Lasting Value with Total Compensation Management
Determining and providing the appropriate compensation for each person — whether it involves base pay, variable pay such as commissions or bonuses or longer-term incentives in the form of cash or equity or other rewards — is critical to being able to attract and retain productive members of the workforce, whether full- or part-time employees, contingent workers or contractors. The complexities of compensation often prove to be a core challenge for human resources departments as they strive to keep the organization productive, satisfied and motivated while ensuring equitable and defensible pay practices across the entire workforce. In today’s age where workers can get an online crowdsourced compensation benchmark for their role, managers and HR must be prepared to respond to comparison questions.
Topics: Analytics, AI and Machine Learning, collaborative computing, total rewards management
Enterprise resource planning (ERP) systems are central to nearly every organization’s management of operational and financial business processes. They are essential to the smooth functioning of an organization’s record keeping, accounting and finance tasks. In manufacturing and distribution, ERP manages inventory and logistics. Some ERP software vendors incorporate an extended set of capabilities that include managing human resources as well as supply chains and logistics. In the 2020s, technology will drive fundamental change in how ERP systems operate and how companies use the software.
Topics: Office of Finance, Financial Performance Management, ERP and Continuous Accounting, robotic finance, Predictive Planning, AI and Machine Learning
Oracle Day by Day earns our 13th Digital Innovation Award for Analytics
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing
Trends in Contact Center Market for 2020 and Beyond
This has been a dramatic year for contact centers. The underlying technology has been changing for some time, but that change is now accelerating because of the urgent operational shifts forced by the pandemic. When you can’t gather dozens or hundreds of people into a single, open-plan site, you must look at alternative models for staffing and interaction handling. You must also work harder to create positive customer experiences across multiple contact channels.
Topics: Customer Experience, Voice of the Customer, Analytics, Contact Center, Product Information Management, Digital Commerce, AI and Machine Learning, Subscription Management, agent management
Varicent: Symon.AI earns our 13th Digital Innovation Award for Office of Sales
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.
Topics: Sales, Marketing, embedded analytics, Analytics, Sales Performance Management, Digital Technology, AI and Machine Learning, intelligent marketing, sales enablement
8x8: Open Communications Platform earns our 13th Digital Innovation Award for Digital Technology
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 performance of an organization. Our goal is to recognize technology and vendors that have introduced noteworthy digital innovations that advance business and IT.
Topics: Customer Experience, Human Capital Management, Marketing, Analytics, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, Operations & Supply Chain, blockchain, employee experience, candidate engagement, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, agent management, extended reality, business digital commerce, work experience management
Evaluating Analytics and BI Software Vendors’ Capabilities
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing
Zuora and Subscription Management: Suite and Platform to Address Digital Business
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.
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.
Topics: Office of Finance, Analytics, Business Intelligence, Data, Financial Performance Management, Price and Revenue Management, robotic finance, Predictive Planning, AI and Machine Learning
Subscription and Usage Management Technology Needs for the Modern Economy
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.
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
Why I Joined Ventana Research to Lead Office of Sales
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.
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
OneStream Wins Our Innovation Award in Office of Finance with Analytic Blend
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.
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
The Business Continuity Imperative: The Workforce Experience and Human Capital Management in 2020 and Beyond
The workforce is an essential part of an organization’s overall business potential because it ensures continuous operations, even in black-swan events. The workforce is the core of the organization and should get the attention it deserves. In challenging times, a “customer-first” mentality tends to take hold — this is not unreasonable but in focusing on satisfying customers and opportunities, business leaders too often forget that the workforce experience is essential to achieving desired results. Fulfilling this objective requires technology designed to meet these human capital management (HCM) objectives. An organization’s agility and ability to invest adequate time and resources into the workforce experience is essential to an organization’s sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Human Capital Management, Office of Finance, Voice of the Customer, Continuous Planning, Business Continuity, Analytics, Business Planning, Workforce Analytics, Workforce Management, Digital Technology, Operations & Supply Chain, Robotic Process Automation, employee experience, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, agent management, People Analytics
The Business Continuity Imperative: Analytics and Data for Engaging Digital Experiences in 2020 and Beyond
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.
Topics: business intelligence, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
The Business Continuity Imperative: The Work Experience and Workforce Engagement Agenda
The workforce is the center of any organization, no matter if the workforce consists of employees, contractors or what we call gig workers. It stands to reason that a black-swan event has an immediate impact on a workforce and thus an organization’s overall business health. In challenging times, a “family-first” mentality tends to take hold — a reality that, far too often, business leaders and HR organizations underestimate. But organizational readiness is essential for sustainability and operational effectiveness.
Topics: Sales, Human Capital Management, Learning, Office of Finance, Voice of the Customer, Analytics, Digital Technology, Digital Marketing, Operations & Supply Chain, Workforce Optimization, AI and Machine Learning
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.
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.
Topics: Analytics, Business Intelligence, Data Governance, Data Preparation, Data, Information Management (IM), Digital Technology, data lakes, AI and Machine Learning
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.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, natural language processing, AI and Machine Learning
The Ventana Research Digital Technology Agenda in 2020
Ventana Research recently announced its 2020 research agenda for digital technology, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value and improve business outcomes. While we have seen more than four decades of digital transformation in the systems and tools businesses rely on, recent years have yielded transformative approaches to technology that can finally actually change the way people and processes work, rather than just make traditional processes more efficient.
Topics: Analytics, Internet of Things, Data, Digital Technology, blockchain, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, extended reality
Harnessing Cognitive Assets: A Hallmark of Agile Organizations
Organizations universally desire the business outcome of improved organizational agility — in other words, the ability to quickly and effectively identify and respond to business risks and opportunities, typically through workforce-related actions. Being agile requires that an organization be adept at two things: harnessing cognitive assets, the knowledge and ideas that comprise the intellectual capital of a workforce, and deploying and using technologies that channel those assets where they will have the greatest impact.
Topics: Human Capital Management, Learning Management, Collaboration, Workforce Management, Knowledge Management, AI and Machine Learning
Kinaxis recently held its annual user conference, Kinexions, which focuses on helping the company’s customers improve their execution of supply chain and sales and operations planning (S&OP). Its RapidResponse software handles S&OP, demand, supply, inventory and capacity planning. S&OP is a function sorely in need of improvement: Our research finds that only 22 percent of companies perform it well or very well.
Topics: Continuous Planning, Analytics, Enterprise Resource Planning, AI and Machine Learning, continuous supply chain, business digital commerce
Sage Intacct recently hosted its annual user group meeting, Advantage, and earlier this year met with industry analysts. Both meetings shed light on how the company is addressing two key opportunities. One is building a robust offering to address rapidly evolving technology requirements for the Office of Finance. The other is broadening the scope of its offering to address the financial management and administration needs of its customers.
Topics: Office of Finance, business intelligence, Financial Performance Management, ERP and Continuous Accounting, robotic finance, Predictive Planning, AI and Machine Learning, revenue and lease accounting
Incentive Solutions Shows Potential in Sales Performance Management
Here are some insights on Incentive Solutions drawn from our latest Value Index research, which provides an analytic assessment of how well vendor offerings address buyers’ requirements. The Ventana Research Value Index on Sales Performance Management 2019 is the distillation of a year of market and product research efforts by Ventana Research. We evaluated Incentive Solutions and eight other vendors in seven categories, five product-related adaptability, capability, manageability, reliability and usability) and two concerning the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each of the seven categories to reflect its relative importance in an RFP process, with the weightings based on data derived from our benchmark research on sales performance management.
Topics: Sales, Customer Experience, Office of Finance, Analytics, Contact Center, Data, Sales Performance Management, Financial Performance Management, Digital Technology, Digital Commerce, Predictive Planning, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, Subscription Management, agent management, intelligent sales
NICE is a Leader in Reliability for Sales Performance Management
Here are some insights on NICE drawn from our latest Value Index research, which provides an analytic assessment of how well vendor offerings address buyers’ requirements. The Ventana Research Value Index on Sales Performance Management 2019 is the distillation of a year of market and product research efforts by Ventana Research. We evaluated NICE and eight other vendors in seven categories, five product-related adaptability, capability, manageability, reliability and usability) and two concerning the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each of the seven categories to reflect its relative importance in an RFP process, with the weightings based on data derived from our benchmark research on sales performance management.
Topics: Sales, Customer Experience, Mobile Technology, Office of Finance, Analytics, Contact Center, Data, Sales Performance Management, Financial Performance Management, Digital Technology, Digital Commerce, Predictive Planning, Conversational Computing, AI and Machine Learning, collaborative computing, Subscription Management, agent management, intelligent sales
beqom is a Value Index Leader for Manageability in Sales Performance Management
Here are some insights on beqom drawn from our latest Value Index research, which provides an analytic assessment of how well vendor offerings address buyers’ requirements. The Ventana Research Value Index on Sales Performance Management 2019 is the distillation of a year of market and product research efforts by Ventana Research. We evaluate