I have previously written about the importance of data democratization as a key element of a data-driven agenda. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed, either internally by employees or externally by partners and customers. This is particularly important for organizations adopting the data mesh approach to data ownership, access and governance. Data mesh is an organizational and cultural approach to data, rather than a technology platform. Nevertheless, multiple vendors are increasingly focused on providing products that facilitate adoption of data mesh and promote data democratization. Amazon Web Services is one such vendor, thanks to the recent launch of Amazon DataZone, one of the figurehead analytics and data announcements made during the company’s recent re:Invent customer event.
Topics: Cloud Computing, Data Governance, Data Management, Data, Digital Technology, data operations, Analytics & Data
Now more than ever, effective data management is crucial to enable decision-makers to better assess information and take calculated actions. It is also important to keep up with the latest trends and technologies to derive higher value from data and analytics and maintain a competitive edge in the market. However, every organization faces challenges with data management and analytics. And as organizations scale, the complexity only increases, creating a need for better data governance, data quality and streamlined and automated processes. DataOps can help solve many of the challenges organizations encounter when trying to unlock the power of data by expanding data use to various parts of an organization. Hitachi Vantara offers DataOps technology that enables organizations to improve data agility and automation. It provides cloud-ready infrastructure, advanced data management software and a broad range of support services.
Topics: Analytics, Data Governance, Data Management, Data, data operations, Analytic Data Platforms
I recently wrote about the potential use cases for distributed SQL databases as well as techniques being employed by vendors to accelerate adoption. Distributed SQL is a term that is used by several vendors to describe operational data platform products that combine the benefits of the relational database model and native support for distributed cloud architecture, including resilience that spans multiple data centers and/or cloud regions. I noted that compatibility with existing database tools and skills was a key factor for these vendors as they lower barriers to developer adoption. A prime example is Cockroach Labs, which highlighted the importance of compatibility and developer efficiency with the recent launch of CockroachDB 22.2.
Topics: Cloud Computing, Data, Digital Technology, Analytics & Data, operational data platforms
Organizations require faster analytics to continuously improve business operations and stay competitive in today’s market. However, many struggle with slow analytics due to a variety of factors such as slow databases, insufficient data storage capacity, poor data quality, lack of proper data cleansing and inadequate IT infrastructure. Challenges such as data silos can also decrease operational efficiency. And as the data grows, performing complex data modelling becomes challenging for users as they spend more time managing data rather than identifying insights.
Topics: Data Management, Data, Analytic Data Platforms
I have previously written about the functional evolution and emerging use cases for NoSQL databases, a category of non-relational databases that first emerged 15 or so years ago and are now well established as potential alternatives to relational databases. NoSQL is a term that is used to describe a variety of databases that fall into four primary functional categories: key-value stores, wide column stores, document-oriented databases and graph databases. Each is worthy of further exploration, which is why I am examining them over a series of analyst perspectives, starting with graph databases.
Topics: Data, operational data platforms
The market for data and analytics products is constantly evolving, with the emergence of new approaches to data persistence, data processing and analytics. This enables organizations to constantly adapt data analytics architecture in response to emerging functional capabilities and business requirements. It can, however, also be a challenge. Investments in data platforms cannot be constantly written-off as organizations adopt new products for new approaches. Too little change can lead to stagnation, but too much change can be chaotic, leading to silos of data and data integration complexity. This is one reason why there is growing interest in the concept of data fabric for managing and governing data across distributed environments. In addition to supporting hybrid and multi-cloud strategies, data fabric enables organizations to manage and generate insight from data spread across a combination of long-standing and new data platforms. Promethium focuses on automating data management and data governance across a distributed architecture with a combination of data fabric and self-service augmented analytics capabilities.
Topics: Data Governance, Data Management, Data, data operations
Data observability was a hot topic in 2022 and looks likely to be a continued area of focus for innovation in 2023 and beyond. As I have previously described, data observability software is designed to automate the monitoring of data platforms and data pipelines, as well as the detection and remediation of data quality and data reliability issues. There has been a Cambrian explosion of data observability software vendors in recent years, and while they have fundamental capabilities in common, there is also room for differentiation. One such vendor is Soda Data, which offers an open-source platform for self-service data observability that is focused on facilitating collaboration between business decision-makers and data teams responsible for generating and managing data to improve trust in data.
Topics: Cloud Computing, Data Management, Data, Digital Technology, data operations, Analytics & Data
I have written about the increased demand for data-intensive operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. I previously described the use of hybrid data processing to enable analytics on application data within operational data platforms. As is often the case in the data platforms sector, however, there is more than one way to peel an orange. Recent years have also seen the emergence of several analytic data platforms that deliver real-time analytic processing suitable for data-intensive operational applications.
Topics: Cloud Computing, Data, Digital Technology, Analytics & Data, operational data platforms, Analytic Data Platforms
Organizations across various industries collect multiple types of data from disparate systems to answer key business questions and deliver personalized experiences for customers. The expanding volume of data increases complexity, and data management becomes a challenge if the process is manual and rules-based. There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results. Organizations must also deal with concurrent errors – from customers to products to suppliers – to create a complete view of the data. Many vendors, including Tamr, have turned to artificial intelligence and machine learning to overcome the challenges associated with maintaining data quality amid the growing volume and variety of data. I assert that by 2026, more than three-quarters of organizations’ data management processes will be enhanced with artificial intelligence and machine learning to increase automation, accuracy, agility and speed.
Topics: Data Governance, Data Management, Data, data operations, Analytic Data Platforms
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
I am happy to share insights from our latest Ventana Research Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 Analytic Data Platforms Value Index is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect real-world criteria incorporated in a request for proposal to data platform vendors supporting the spectrum of analytic use-cases. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience: Adaptability, Capability, Manageability, Reliability and Usability, and two related to the Customer Experience: Total Cost of Ownership/Return on Investment and Validation. This research-based index evaluates the full business and information technology value of analytic data platforms offerings. I encourage you to learn more about our Value Index and its effectiveness as a vendor selection and request for information/requestion for proposal tool.
Topics: Cloud Computing, Data, Digital Technology, Analytics & Data, Analytic Data Platforms
Ventana Research recently published the 2023 Analytic Data Platforms Value Index. As organizations strive to be more data-driven, increasing reliance on data as a fundamental factor in business decision-making, the importance of the analytic data platform has never been greater. In this post, I’ll share some of my observations about how the analytic data platforms market is evolving.
Topics: Cloud Computing, Data, Digital Technology, Analytics & Data, operational data platforms, Analytic Data Platforms
I am happy to share insights from our latest Ventana Research Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 Operational Data Platforms Value Index is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect real-world criteria incorporated in a request for proposal to data platform vendors supporting the spectrum of operational use cases. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience: Adaptability, Capability, Manageability, Reliability and Usability, and two related to the Customer Experience: Total Cost of Ownership/Return on Investment and Validation.
Topics: Cloud Computing, Data, Digital Technology, Analytics & Data, operational data platforms
Ventana Research recently announced its 2023 Market Agenda for Data, continuing the guidance we have offered for two decades to help organizations derive optimal value and improve business outcomes.
Topics: Cloud Computing, Data Governance, Data Management, Data, Digital Technology, data operations, Analytics & Data, Streaming Data & Events, operational data platforms, Analytic Data Platforms
Ventana Research recently announced its 2023 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.
Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, natural language processing, Process Mining, Analytics and Data, Collaborative & Conversational Computing
Ventana Research recently published the 2023 Operational Data Platforms Value Index. The importance of the operational data platform has never been greater as organizations strive to be more data-driven, incorporating intelligence into operational applications via personalization and recommendations for workers, partners and customers. In this post, I’ll share some of my observations on how the operational data platforms market is evolving.
Topics: Cloud Computing, Data, Analytics and Data, operational data platforms, Analytic Data Platforms
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
I am happy to share insights from our latest Ventana Research Value Index, which assesses how well vendors’ offerings meet buyers’ requirements. The 2023 Data Platforms Value Index is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect real-world criteria incorporated in a request for proposal to data platform vendors that support the spectrum of operational and analytic use cases. Using this methodology, we evaluated vendor submissions in seven categories: five relevant to the Product Experience: Adaptability, Capability, Manageability, Reliability and Usability, and two related to the Customer Experience: Total Cost of Ownership/Return on Investment and Validation.
Topics: Cloud Computing, Data, Digital Technology, Analytics and Data, operational data platforms, Analytic Data Platforms
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
Data observability is a hot topic and trend. I have written about the importance of data observability for ensuring healthy data pipelines, and have covered multiple vendors with data observability capabilities, offered both as standalone and part of a larger data engineering system. Data observability software provides an environment that takes advantage of machine learning and DataOps to automate the monitoring of data quality and reliability. The term has been adopted by multiple vendors across the industry, and while they all have key functionality in common – including collecting and measuring metrics related to data quality and data lineage – there is also room for differentiation. A prime example is Acceldata, which takes a position that data observability requires monitoring not only data and data pipelines but also the underlying data processing compute infrastructure as well as data access and usage.
Topics: Cloud Computing, Data Management, Data, Digital Technology, data operations
Having recently completed the 2023 Data Platforms Value Index, I want to share some of my observations about how the market is evolving. Although this is our inaugural assessment of the market for data platforms, the sector is mature and products from many of the vendors we assess can be used to effectively support operational and analytic use cases.
Topics: Cloud Computing, Data, Digital Technology, Analytics and Data, operational data platforms, Analytic Data Platforms
Ventana Research has announced its market agenda for 2023, continuing a 20-year tradition of credibility and trust in our objective efforts to educate and guide the technology market. Our research and insights are backed by our expertise and independence, as we do not share our Market Agenda or our market research – including analyst and market perspectives – with any external party before it is published. We continuously refine our Market Agenda throughout the year to ensure we offer the expertise and insights organizations rely on to better assess and navigate the direction of the technology industry.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Digital Business, Office of Revenue
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
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
Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics. Our research has shown that creating and managing metrics in a semantic model improves analytics processes.
Topics: Analytics, Business Intelligence, Data Governance, Data, Digital Technology, Analytics & Data
There is always space for innovation in the data platforms sector, and new vendors continue to emerge at regular intervals with new approaches designed to serve specialist data storage and processing requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established vendors, especially for the most demanding operational or analytic data platform requirements. It is never easy, however, for developers of new data platform products to gain significant market traction, given the dominance of the established relational database vendors and cloud providers. Targeting requirements that are not well-served by general purpose data platforms can help new vendors get a toe in the door of customer accounts. The challenge to gaining further market traction is for new vendors to avoid having products become pigeon-holed as only being suitable for a niche set of requirements. This is precisely the problem facing the various distributed SQL database providers.
Topics: Cloud Computing, Data, operational data platforms
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
The technology industry has established itself as a pivotal force in its ability to help organizations become more intelligent and automated. But doing so has required a journey of epic proportions for most organizations that have had to endure a transition of competencies and skills that was, in many places, transitioned to consulting firms who were hired appropriately to manage changes. Unfortunately, this step led, in many cases, to an extended focus on digital transformation rather than the necessary modernization of business processes and technology. Through 2024, after concerted investment into digital transformation, one-half of organizations will require a new digital business and technology agenda for organizational resilience.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Digital Business, Office of Revenue
Earlier this year, I wrote about the increasing importance of data observability, an emerging product category that takes advantage of machine learning (ML) and Data Operations (DataOps) to automate the monitoring of data used for analytics projects to ensure its quality and lineage. Monitoring the quality and lineage of data is nothing new. Manual tools exist to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data observability vendors, including Monte Carlo Data, have emerged in recent years with the goal of increasing the productivity of data teams and improving organizations’ trust in data using automation and artificial intelligence and machine learning (AI/ML).
Topics: Business Intelligence, Cloud Computing, Data Management, Data, data operations
One of the most significant considerations when choosing an analytic data platform is performance. As organizations compete to benefit most from being data-driven, the lower the time to insight the better. As data practitioners have learnt over time, however, lowering time to insight is about more than just high-performance queries. There are opportunities to improve time to insight throughout the analytics life cycle, which starts with data ingestion and integration, includes data preparation and data management, as well as data storage and processing, and ends with data visualization and analysis. Vendors focused on delivering the highest levels of analytic performance, such as SQream, understand that lowering time to insight relies on accelerating every aspect of that life cycle.
Topics: Business Intelligence, Data, AI and Machine Learning, data operations, Analytic Data Platforms
Organizations are increasingly utilizing cloud object storage as the foundation for analytic initiatives. There are multiple advantages to this approach, not least of which is enabling organizations to keep higher volumes of data relatively inexpensively, increasing the amount of data queried in analytics initiatives. I assert that by 2024, 6 in ten organizations will use cloud-based technology as the primary analytics data platform, making it easier to adopt and scale operations as necessary.
Topics: Teradata, Data Governance, Data Management, Data, data operations, operational data platforms, Analytic Data Platforms, Object storage, Vantage platform
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
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
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
In their pursuit to be data-driven, organizations are collecting and managing more data than ever before as they attempt to gain competitive advantage and respond faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. As data is increasingly spread across multiple data centers, clouds and regions, organizations need to manage data on multiple systems in different locations and bring it together for analysis. As the data volumes increase and more data sources and data types are introduced in the organization, it creates challenges to storing, managing, connecting and analyzing the huge set of information that is spread across multiple locations. Having a strong foundation and scalable data management architecture in place can help alleviate many of the challenges organizations face when they are scaling and adding more infrastructure. We have written about the potential for hybrid and multi-cloud platforms to safeguard data across heterogenous environments, which plays to the strengths of companies, such as Actian, that provide a single environment with the ability to integrate, manage and process data across multiple locations.
Topics: Data Management, Data, data operations, Analytic Data Platforms
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
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
I have written before about the continued use of specialist operational and analytic data platforms. Most database products can be used for operational or analytic workloads, and the number of use cases for hybrid data processing is growing. However, a general-purpose database is unlikely to meet the most demanding operational or analytic data platform requirements. Factors including performance, reliability, security and scalability necessitate the use of specialist data platforms. I assert that through 2026, and despite increased demand for hybrid operational and analytic processing, more than three-quarters of data platform use cases will have functional requirements that encourage the use of specialized analytic or operational data platforms. It is for that reason that specialist database providers, including Ocient, continue to emerge with new and innovative approaches targeted at specific data-processing requirements.
Topics: business intelligence, Cloud Computing, Data Management, Data, Analytics and Data, Analytic Data Platforms
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
I have recently written about the organizational and cultural aspects of being data-driven, and the potential advantages data-driven organizations stand to gain by responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. I have also explained that data-driven processes require more agile, continuous data processing, with an increased focus on extract, load and transform processes — as well as change data capture and automation and orchestration — as part of a DataOps approach to data management. Safeguarding the health of data pipelines is fundamental to ensuring data is integrated and processed in the sequence required to generate business intelligence. The significance of these data pipelines to delivering data-driven business strategies has led to the emergence of vendors, such as Astronomer, focused on enabling organizations to orchestrate data engineering pipelines and workflows.
Topics: Cloud Computing, Data Management, Data, data operations, Analytics & Data
The data catalog has become an integral component of organizational data strategies over the past decade, serving as a conduit for good data governance and facilitating self-service analytics initiatives. The data catalog has become so important, in fact, that it is easy to forget that just 10 years ago it did not exist in terms of a standalone product category. Metadata-based data management functionality has had a role to play within products for data governance and business intelligence for much longer than that, of course, but the emergence of the data catalog as a product category provided a platform for metadata-based data inventory and discovery that could span an entire organization, serving multiple departments, use cases and initiatives.
Topics: business intelligence, Data Governance, Data Management, Data, data operations, Analytics and Data
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
I have written recently about increased demand for data-intensive applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations. Almost one-quarter of respondents (22%) to Ventana Research’s Analytics and Data Benchmark Research are currently analyzing data in real time, with an additional 10% analyzing data every hour. There are multiple data platform approaches to delivering real-time data processing and analytics and more agile data pipelines. These include the use of streaming and event data processing, as well as the use of hybrid data processing to enable analytics to be performed on application data within operational data platforms. Another approach, favored by a group of emerging vendors such as Rockset, is to develop these data-intensive applications on a specialist, real-time analytic data platform specifically designed to meet the performance and agility requirements of data-intensive applications.
Topics: Cloud Computing, Data, Streaming Analytics, Analytics & Data, Streaming Data & Events, operational data platforms, Analytic Data Platforms
I recently noted that as demand for real-time interactive applications becomes more pervasive, the use of streaming data is becoming more mainstream. Streaming data and event processing has been part of the data landscape for many decades, but for much of that time, data streaming was a niche activity. Although adopted in industry segments with high-performance, real-time data processing and analytics requirements such as financial services and telecommunications, data streaming was far less common elsewhere. That has changed significantly in recent years, fueled by the proliferation of open-source and cloud-based streaming data and event technologies that have lowered the cost and technical barriers to developing new applications able to take advantage of data in-motion. This is a trend we expect to continue, to the extent that streaming data and event processing becomes an integral part of mainstream data-processing architectures.
Topics: Big Data, Data, Streaming Analytics, Analytics & Data, Streaming Data & Events
I have recently written about the importance of healthy data pipelines to ensure data is integrated and processed in the sequence required to generate business intelligence, and the need for data pipelines to be agile in the context of real-time data processing requirements. Data engineers, who are responsible for monitoring, managing and maintaining data pipelines, are under increasing pressure to deliver high-performance and flexible data integration and processing pipelines that are capable of handling the rising volume and frequency of data. Automation is a potential solution to this challenge, and several vendors, such as Ascend.io, have emerged in recent years to reduce the manual effort involved in data engineering.
Topics: Big Data, Cloud Computing, Data Management, Data, data operations
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
Streaming data has been part of the industry landscape for decades but has largely been focused on niche applications in segments with the highest real-time data processing and analytics performance requirements, such as financial services and telecommunications. As demand for real-time interactive applications becomes more pervasive, streaming data is becoming a more mainstream pursuit, aided by the proliferation of open-source streaming data and event technologies, which have lowered the cost and technical barriers to developing new applications that take advantage of data in motion. Ventana Research’s Streaming Data Dynamic Insights enables an organization to assess its relative maturity in achieving value from streaming data. I assert that by 2024, more than one-half of all organizations’ standard information architectures will include streaming data and event processing, allowing organizations to be more responsive and provide better customer experiences.
Topics: Data, Streaming Analytics, Streaming Data & Events, operational data platforms
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
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
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 previously explained how the data lakehouse is one of two primary approaches being adopted to deliver what I have called a hydroanalytic data platform. Hydroanalytics involves the combination of data warehouse and data lake functionality to enable and accelerate analysis of data in cloud storage services. The term data lakehouse has been rapidly adopted by several vendors in recent years to describe an environment in which data warehousing functionality is integrated into the data lake environment, rather than coexisting alongside. One of the vendors that has embraced the data lakehouse concept and terminology is Dremio, which recently launched the general availability of its Dremio Cloud data lakehouse platform.
Topics: business intelligence, Analytics, Data, data lakes, data platforms
As I recently described, it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads, albeit with growing demand for hybrid data processing use-cases and functionality. Specialist operational and analytic data platforms have historically been the since preferred option, but there have always been general-purpose databases that could be used for both analytic and operational workloads, with tuning and extensions to meet the specific requirements of each.
Topics: Analytics, Business Intelligence, Cloud Computing, Data, Digital Technology, data platforms, Analytics & Data
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
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
The server is a key component of enterprise computing, providing the functional compute resources required to support software applications. Historically, the server was so fundamentally important that it – along with the processor, or processor core – was also a definitional unit by which software was measured, priced and sold. That changed with the advent of cloud-based service delivery and consumption models.
Topics: Business Continuity, Cloud Computing, Data, Digital Technology, Digital Business, data platforms, Analytics & Data
Over a decade ago, I coined the term NewSQL to describe the new breed of horizontally scalable, relational database products. The term was adopted by a variety of vendors that sought to combine the transactional consistency of the relational database model with elastic, cloud-native scalability. Many of the early NewSQL vendors struggled to gain traction, however, and were either acquired or ceased operations before they could make an impact in the crowded operational data platforms market. Nonetheless, the potential benefits of data platforms that span both on-premises and cloud resources remain. As I recently noted, many of the new operational database vendors have now adopted the term “distributed SQL” to describe their offerings. In addition to new terminology, a key trend that separates distributed SQL vendors from the NewSQL providers that preceded them is a greater focus on developers, laying the foundation for the next generation of applications that will depend on horizontally scalable, relational-database functionality. Yugabyte is a case in point.
Topics: Business Continuity, Cloud Computing, Data, Digital Technology, Digital Business, data platforms, Analytics & Data
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
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
Data governance is an issue that impacts all organizations large and small, new and old, in every industry, and every region of the world. Data governance ensures that an organization’s data can be cataloged, trusted and protected, improving business processes to accelerate analytics initiatives and support compliance with regulatory requirements. Not all data governance initiatives will be driven by regulatory compliance; however, the risk of falling foul of privacy (and human rights) laws ensures that regulatory compliance influences data-processing requirements and all data governance projects. Multinational organizations must be cognizant of the wide variety of regional data security and privacy requirements, not least the European Union’s General Data Protection Regulation (GDPR). The GDPR became enforceable in 2018, protects the privacy of personal or professional data, and carries with it the threat of fines of up to 20 million euros ($22 million) or 4% of a company’s global revenue. Europe is not alone in regulating against the use of personally identifiable information (other similar regulations include The California Consumer Privacy Act) but Ventana Research’s Data Governance Benchmark Research illustrates that there are differing attitudes and approaches to data governance on either side of the Atlantic.
Topics: Analytics, Data Governance, Data
I recently described the growing level of interest in data mesh which provides an organizational and cultural approach to data ownership, access and governance that facilitates distributed data processing. As I stated in my Analyst Perspective, data mesh is not a product that can be acquired or even a technical architecture that can be built. Adopting the data mesh approach is dependent on people and process change to overcome traditional reliance on centralized ownership of data and infrastructure and adapt to its principles of domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance. Many organizations will need to make technological changes to facilitate adoption of data mesh, however. Starburst Data is associated with accelerating analysis of data in data lakes but is also one of several vendors aligning their products with data mesh.
Topics: Business Continuity, Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, data lakes, Digital Business, data platforms, Analytics & Data
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
Data mesh is the latest trend to grip the data and analytics sector. The term has been rapidly adopted by numerous vendors — as well as a growing number of organizations —as a means of embracing distributed data processing. Understanding and adopting data mesh remains a challenge, however. Data mesh is not a product that can be acquired, or even a technical architecture that can be built. It is an organizational and cultural approach to data ownership, access and governance. Adopting data mesh requires cultural and organizational change. Data mesh promises multiple benefits to organizations that embrace this change, but doing so may be far from easy.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, data operations, Digital Business, data platforms, Analytics & Data, Streaming Data & Events
For years, maybe decades, we have heard about the struggles between IT and line-of-business functions. In this perspective, we will look at some of the data from our Analytics and Data Benchmark Research about the roles of IT and line-of-business teams in analytics and data processes. We will also look at some of the disconnects between these two groups. And, by looking at how organizations are operating today and the results they are achieving, we can discern some of the best practices for improving the outcomes of analytics and data processes.
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
I recently examined how evolving functionality had fueled the adoption of NoSQL databases, recommending that organizations evaluate NoSQL databases when assessing options for data transformation and modernization efforts. This recommendation was based on the breadth and depth of functionality offered by NoSQL database providers today, which has expanded the range of use cases for which NoSQL databases are potentially viable. There remain a significant number of organizations that have not explored NoSQL databases as well as several workloads for which it is assumed NoSQL databases are inherently unsuitable. Given the advances in functionality, organizations would be well-advised to maintain up-to-date knowledge of available products and services and an understanding of the range of use cases for which NoSQL databases are a valid option.
Topics: NoSQL, Data, data platforms, Use Cases
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
As businesses become more data-driven, they are increasingly dependent on the quality of their data and the reliability of their data pipelines. Making decisions based on data does not guarantee success, especially if the business cannot ensure that the data is accurate and trustworthy. While there is potential value in capturing all data — good or bad — making decisions based on low-quality data may do more harm than good.
Topics: Data Governance, Data Integration, Data, Digital Technology, data lakes, data operations, Analytics & Data
Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce.
Topics: Sales, business intelligence, embedded analytics, Analytics, Data, Sales Performance Management, Digital Technology, Digital Commerce, natural language processing, Subscription Management, partner management, Revenue Management, Sales Engagement, Collaborative & Conversational Computing
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
Many organizations invest in data governance out of concern over misuse of data or potential data breaches. These are important considerations and valid aspects of data governance programs. However, good data governance also has positive impacts on organizations. For example, I have previously written about the valuable connection between the use of data catalogs and satisfaction with an organization’s data lake. Our most recent Analytics and Data Benchmark Research demonstrates some of the beneficial links between data governance and analytics. In this Perspective, I’ll share some of the correlations identified in our research.
Topics: embedded analytics, Analytics, Data Governance, Data, Digital Technology
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
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
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
Ventana Research recently announced its 2022 Market Agenda for Data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
Topics: Data Governance, Data Integration, Data, data lakes, data operations, data platforms, Streaming Data & Events
I’m proud to share Ventana Research’s 2022 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 operations during any type of disruption.
Topics: Analytics, Cloud Computing, Internet of Things, Data, Digital Technology
Ventana Research has announced its market agenda for 2022, continuing the tradition of reliability in our efforts to educate and guide the technology market. Our assessments are backed by our expertise and independence, as we do not share our market agenda or our research – including analyst and market perspectives or our Value Index – with any external party until it is published. We review and refine our market agenda throughout the year to ensure we offer the expertise and insights organizations rely on to better assess and navigate the direction of the technology industry.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Digital Business, Office of Revenue, Market Agenda
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
Few trends have had a bigger impact on the data platforms landscape than the emergence of cloud computing. The adoption of cloud computing infrastructure as an alternative to on-premises datacenters has resulted in significant workloads being migrated to the cloud, displacing traditional server and storage vendors. Almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research currently use cloud computing products for analytics and data, and a further one-quarter plan to do so. In addition to deploying data workloads on cloud infrastructure, many organizations have also adopted cloud data and analytics services offered by the same cloud providers, displacing traditional data platform vendors. Organizations now have greater choice in relation to potential products and providers for data and analytics workloads, but also need to think about integrating services offered by cloud providers with established technology and processes. Having pioneered the concept, Amazon Web Services has arguably benefitted more than most from adoption of cloud computing, and is also in the process of expanding and adjusting its portfolio to alleviate challenges and encourage even greater adoption.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data
Organizations today are working with multiple applications and systems, including enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) and other systems, where data can easily become fragmented and siloed. And as the organization increases its data sources and adds more systems and custom applications, it becomes challenging to manage the data consistently and keep data definitions up to date. This increases the need to use master data management (MDM) software that can provide a single source of truth to drive accurate analytics and business operations.
Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Analytics & Data
The term NoSQL has been a misnomer ever since it appeared in 2009 to describe a group of emerging databases. It was true that a lack of support for Structured Query Language (SQL) was common to the various databases referred to as NoSQL. However, it was always one of a number of common characteristics, including flexible schema, distributed data processing, open source licensing, and the use of non-relational data models (key value, document, graph) rather than relational tables. As the various NoSQL databases have matured and evolved, many of them have added support for SQL terms and concepts, as well as the ability to support SQL format queries. Couchbase has been at the forefront of this effort, recognizing that to drive greater adoption of NoSQL databases in general (and its distributed document database in particular) it was wise to increase compatibility with the concepts, tools and skills that have dominated the database market for the past 50 years.
Topics: Business Continuity, Analytics, Data, Digital Technology, Digital Business, data platforms
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
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
Breaking into the database market as a new vendor is easier said than done given the dominance of the sector by established database and data management giants, as well as the cloud computing providers. We recently described the emergence of a new breed of distributed SQL database providers with products designed to address hybrid and multi-cloud data processing. These databases are architecturally and functionally differentiated from both the traditional relational incumbents (in terms of global scalability) and the NoSQL providers (in terms of the relational model and transactional consistency). Having differentiated functionality is the bare minimum a new database vendor needs to make itself known in a such a crowded market, however.
Topics: Cloud Computing, Data, Digital Technology, Digital transformation, Digital Business, data platforms, Analytics and Data
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 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
The need for a COVID-19 vaccination “passport” has prompted some to suggest using blockchain technology as a means of reliably verifying an individual’s status at an international level. There are precedents: for example, until smallpox was eradicated, all international travelers were obliged to carry an immunization record for that disease on a standard paper form to gain entrance to a country. With the likelihood that COVID-19 will remain endemic for many years, a reliable digital record with universal accessibility would be a boon to everyone, especially to international travelers. Vaccination records are just one part of the broader topic of using blockchain technology for medical identity management.
Topics: Data Governance, Information Management, Data, blockchain
It has been clear for some time that future enterprise IT architecture will span multiple cloud providers as well as on-premises data centers. As Ventana Research noted in the market perspective on data architectures, the rapid adoption of cloud computing has fragmented where data is accessed or consolidated. We are already seeing that almost one-half (49%) of respondents to Ventana Research’s Analytics and Data Benchmark Research are using cloud computing for analytics and data, of which 42% are currently using more than one cloud provider.
Topics: Data, data lakes, data operations, data platforms
Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps. The same desire for agility suggests that organizations need to adopt AnalyticOps.
Topics: business intelligence, Analytics, Data Governance, Data, Digital Technology, data operations, data platforms
Talend is a data integration and management software company that offers applications for cloud computing, big data integration, application integration, data quality and master data management. The platform enables personnel to work with relational databases, Apache Hadoop, Spark and NoSQL databases for cloud or on-premises jobs. Talend data integration software offers an open and scalable architecture and can be integrated with multiple data warehouses, systems and applications to provide a unified view of all data. Its code generation architecture uses a visual interface to create Java or SQL code.
Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, data lakes, AI and Machine Learning
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
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
Our research shows that nearly all financial service organizations (97%) consider it important to accelerate the flow of information and improve responsiveness. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real time, financial service organizations can quickly turn events into valuable business outcomes in the form of new products and services or revenue.
Topics: Analytics, Internet of Things, Data, Digital Technology, Streaming Analytics
Databricks is a data engineering and analytics cloud platform built on top of Apache Spark that processes and transforms huge volumes of data and offers data exploration capabilities through machine learning models. It can enable data engineers, data scientists, analysts and other workers to process big data and unify analytics through a single interface. The platform supports streaming data, SQL queries, graph processing and machine learning. It also offers a collaborative user interface — workspace — where workers can create data pipelines in multiple languages — including Python, R, Scala, and SQL — and train and prototype machine learning models.
Topics: embedded analytics, Analytics, Business Intelligence, Collaboration, Data Governance, Information Management, Data, data lakes, AI and Machine Learning
The work environment today demands that your organization advances the efficiency to execute business processes for continuous operations to have a positive impact on business performance. The capability to be responsive to any range of minor to disruptive business events is required to support business continuity and level of organizational readiness to meet the needs of digital business. Ventana Research asserts that in 2025, one-quarter of organizations will remain digitally ineffective in achieving the business priorities for customer-, product- and people-related processes. It is essential to eliminate bottlenecks and become an organization that places action and decision-making at is center to optimize the execution of business processes.
Topics: Customer Experience, Voice of the Customer, embedded analytics, Analytics, Business Intelligence, Cloud Computing, Contact Center, Data, Digital Technology, Operations & Supply Chain, Enterprise Resource Planning, Digital transformation, natural language processing, AI and Machine Learning, continuous supply chain, agent management, Digital Business, Experience Management, Field Service, Process Mining, Streaming Analytics
Access to external data can provide a competitive advantage. Our research shows that more than three-quarters (77%) of participants consider external data to be an important part of their machine learning (ML) efforts. The most important external data source identified is social media, followed by demographic data from data brokers. Organizations also identified government data, market data, environmental data and location data as important external data sources. External data is not just part of ML analyses though. Our research shows that external data sources are also a routine part of data preparation processes, with 80% of organizations incorporating one or more external data sources. And a similar proportion of participants in our research (84%) include external data in their data lakes.
Topics: Analytics, Business Intelligence, Internet of Things, Data, Digital Technology, Lease Management, AI and Machine Learning, Streaming Data, Streaming Analytics
The technology industry throws around a lot of similar terms with different meanings as well as entirely different terms with similar meanings. In this post, I don’t want to debate the meanings and origins of different terms; rather, I’d like to highlight a technology weapon that you should have in your data management arsenal. We currently refer to this technology as data virtualization. Other similar terms you may have heard include data fabric, data mesh and [data] federation. I’ll briefly discuss these terms and how I see them being used, but ultimately, I’d like to share with you some research that shows why data virtualization can be valuable, regardless of what you call it.
Topics: Analytics, Data Governance, Data Integration, Data, Digital Technology, data lakes
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
Data governance is a hot topic these days. In fact, we are conducting benchmark research on the subject here. With increasing regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations face more external oversight of their data governance practices. The risk of significant fines associated with these and other regulations, coupled with organizations’ internal compliance requirements, has brought more attention to data governance practices. We anticipate through 2023, three-quarters of Chief Data Officers’ primary concerns will be governing the privacy and security of their organization’s data.
Topics: Analytics, Data Governance, Data, Digital Technology
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 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
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 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
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
Environmental, social and governance reporting by public corporations has become a top-of-mind issue for senior executives and boards of directors as countries increasingly consider or mandate its implementation in some form. The fundamental rationale for ESG reporting is rooted in the inability of purely financial measures to capture externalities (such as greenhouse gas emissions) or provide metrics that enable an objective assessment of management’s ability to properly determine trade-offs between short-term results and long-term sustainability. And, while in the United States the Sarbanes-Oxley Act mandates that auditors assess governance, the focus of this assessment is on preventing financial fraud as opposed to broader objectives that may be important to the functioning of the company as a sustainable entity.
Topics: Human Capital Management, Office of Finance, Business Intelligence, Data Governance, Data Preparation, Data, Financial Performance Management, ERP and Continuous Accounting
We are happy to share some insights about Innovit MDM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales
We are happy to share some insights about Viamedici EPIM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
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: Voice of the Customer, Collaboration, Data Governance, Data Preparation, Information Management, Data, Product Information Management, Digital Marketing, Digital Commerce, blockchain, data lakes, intelligent marketing
We are happy to share some insights about Contentserv CS drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about Perfion drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
We are happy to share some insights about Winshuttle EnterWorks drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
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
We are happy to share some insights about inRiver PIM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
Event data can be used to enhance existing processes, but it can also be used to dramatically impact operations, revenue models and the bottom line for manufacturers. Our Benchmark Research shows 95% of manufacturers consider it important to speed the flow of information and improve responsiveness within business processes. In this perspective I’ll share how manufacturers are working with event data to transform their organizations.
Topics: Customer Experience, Information Management, Internet of Things, Data, Digital Technology, Operations & Supply Chain
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2021 Ventana Research Value Index: Collaborative Analytics and Data is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and included vendors.
Topics: business intelligence, Analytics, Collaboration, Data Preparation, Information Management, Data, Digital Technology, natural language processing, mobile computing
We are happy to share some insights about Riversand PX 360 drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2021 Ventana Research Value Index: Embedded Analytics and Data is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and included vendors.
Topics: Analytics, Business Intelligence, Data Preparation, Information Management, Data, natural language processing
We are happy to share some insights about Magnitude Agility PIM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Mobile Analytics and Data 2021 is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and which vendors are included.
Topics: business intelligence, Analytics, Collaboration, Data Governance, Information Management, Data, Digital Technology, natural language processing, mobile computing
We are happy to share some insights about Salsify ProductXM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
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
We are happy to share some insights about Pimcore Platform drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
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
We are happy to share some insights about Akeneo Serenity drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
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
We are happy to share some insights about Informatica's Product 360 drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
Topics: Marketing, Data Governance, Data, Product Information Management, Price and Revenue Management, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, Office of Sales, data operations
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
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
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
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
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
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 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
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
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
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
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
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
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
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
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
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
Everyone talks about data quality, as they should. Our research shows that improving the quality of information is the top benefit of data preparation activities. Data quality efforts are focused on clean data. Yes, clean data is important. but so is bad data. To be more accurate, the original data as recorded by an organization’s various devices and systems is important.
Topics: Data Governance, Data Preparation, Data, Information Management (IM), data lakes
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
The amount of data flowing into organizations is growing exponentially, creating a need to process more data more quickly than ever before. Our Data Preparation Benchmark Research shows that accessing and preparing data continues to be the most time-consuming part of making data available for analysis. This can potentially slow down the organizational functions which depend on the analysis results. Trying to get ahead of the backlog with incremental improvements to existing approaches and traditional technologies alone can be frustrating.
Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes
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
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
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
Since customer data platforms (CDP) emerged in the marketplace about five years ago, there has been debate about what roles they fill, especially within customer service organizations. They were originally developed by small software firms to provide marketing teams with a comprehensive view of customer records. Those records could be scattered throughout an organization, siloed by system and department. CDPs were an attempt to shortcut integration processes that are long, expensive and often custom-designed.
Topics: Customer Experience, Marketing, data artisan, Data Governance, Data Lake, Data Preparation, Data, Information Management (IM), intelligent marketing
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
Ventana Research recently announced its 2021 Market Agenda for data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
Topics: Data Governance, Data Preparation, Information Management, Data, data lakes, Streaming Data, data operations, Event Data, Data catalog, Event Streams, Event Stream Processing
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
Ventana Research has announced its market agenda for 2021, continuing the tradition of transparency in our efforts to educate and guide the technology market but also our independence as we do not share our market agenda or analyst perspectives with any external party. Each year, we proudly formulate our market agenda that is not biased by clients or the technology industry, focusing on education rather than the prospect of consulting or software revenue. We review and refine our plan throughout the year to ensure we offer the expertise and insights organizations rely on to better understand – and navigate – the direction of the technology industry.
Topics: Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Data, Digital Technology, Operations & Supply Chain, Office of Sales, Digital Business
Data is becoming more valuable and more important to organizations. At the same time, organizations have become more disciplined about the data on which they rely to ensure it is robust, accurate and governed properly. Without data integrity, organizations cannot trust the information produced by their data processes, and will be discouraged from using that data, resulting in inefficiencies and reduced effectiveness.
Topics: business intelligence, Analytics, Data Governance, Data Preparation, Information Management, Data, data lakes
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
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
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 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
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: Continuous Planning, Data, Digital Technology, Operations & Supply Chain, Enterprise Resource Planning, blockchain, continuous supply chain
A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called Data Lakes: Safe Way to Swim in Big Data? for background. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings.
Topics: Data Governance, Data Lake, Information Management, Data, Digital Technology
Every organization performing analytics with multiple employees needs to collaborate. They should be collaborating in the analytics process and in communicating the results of those analyses. As I continue my evaluation of analytics and data vendors, I have to admit some disappointment at the level of collaborative capabilities some analytics vendors provide. To be fair, the level of capabilities vary widely, but I expected collaborative capabilities to be more uniformly available as a standard feature in analytics technologies by now. I had anticipated that three-quarters of analytics vendors would include collaboration capabilities. More than half the vendors I have evaluated support some comments and discussion in their products, only a few have incorporated social recognition and wall posting as part of their collaborative capabilities. So, what impact does a lack of analytics collaboration have on organizations undergoing digital transformation?
Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, collaborative computing
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
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
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
Economic dynamics and market pressures during a black-swan event can wreak havoc on efforts to effectively manage revenue operations and pricing for business continuity. For many organizations, environmental changes disrupt the methods by which these essential business processes are managed can be disrupted, damaging the revenue streams that create profitability. The array of pricing strategies and related promotional tactics across channels for configure, price and quote (CPQ), digital commerce and subscription management can challenge the best of organizations. Leadership must examine the agility of pricing management to determine if they have ability to make and manage changes to determine the effectiveness of decisions. This requires visibility into revenue operations and selling channels, which in turn requires programs, processes and technology designed to meet the needs of what is called price and revenue management (PRM).
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Product Information Management, Sales Performance Management, Workforce Management, Workforce Planning, Price and Revenue Management, Total Compensation Management, Conversational Computing
Over the past several months, I have discussed a wide range of topics that organizations must consider and appropriately prioritize to maintain business continuity during periods of upheaval. But sometimes it’s important to take a step back and reflect on a critical and recurring theme: experiences. The array of experiences across the workforce and business processes both inside and outside of the organization are an essential part of an organization’s success. Leadership must give these experiences the attention they deserve, and this requires visibility into operations and the tools to measure effectiveness, especially during black-swan events. Fulfilling this objective requires the programs, processes and technology designed to meet the needs of what is called experience management (XM).
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Sales Performance Management, Workforce Management, Workforce Planning, Operations & Supply Chain, Total Compensation Management, Conversational Computing
Business planning is an essential part of an organization’s focus on its future performance and overall potential because it ensures continuous operations, even in black-swan events. Planning across the entire organization needs to be a critical priority and leadership should give it the attention it deserves. In challenging times, a focus on execution tends to take hold — this is not unreasonable but in focusing on satisfying immediate customer and workforce needs and putting out fires, business leaders too often forget that forward-looking continuous planning is essential to achieving desired outcomes. Fulfilling this objective requires technology designed to meet these needs for every business process in the organization.
Topics: Sales, Human Capital Management, Office of Finance, Continuous Planning, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting, Total Compensation Management, Predictive Planning, Conversational Computing
Analytics and data provide visibility into an organization’s past, present and potential performance. However, not all organizations are using analytics that provide timely insights — insights that not just reflect what happen but direct a successful course for the future. Demand for personalized and relevant insight only intensifies in a black-swan event. To maintain business continuity in times of pressure, it is critical that organizations not waste any time or resources when using analytics and data to optimize operations and decision-making. Just having an analytics and data-first mentality and operating in the cloud is insufficient for success, as those are just part of an effective data and analytics effort. Organizations also should include data science and machine learning that can provide an excellent digital experience; unfortunately, this is no simple task.
Topics: business intelligence, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
Marketing is inextricably linked to business success, and digital technology is essential to an organization’s overall marketing potential because it generates interest and brand awareness. In a black-swan event, the marketing department often is overwhelmed by short-term demands, so in these situations it’s of the essence that digital transformation gets the attention it deserves. In challenging times, a “putting-out-fires” mentality tends to take hold — this is not unreasonable but in focusing on satisfying the interest of the moment, business leaders too often forget that a consistent digital experience is essential to engaging consumers, the public and customers in a way that contributes to long-term success. Fulfilling this objective requires technology designed to deliver for marketing to meet this essential imperative. An organization’s agility and ability to invest adequate time and resources into marketing technology that enables a superior digital experience is essential for its sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Conversational Computing
Contact centers play a substantial role in an organization’s success. The customer journey is engaged here, at each moment of interaction. Agents, whether human or machine-driven, are intrinsic to the customer experience and the value of the contact center. Customers are essential to an organizations’ overall business potential because they generate revenue. In a black-swan event, demand for customer service may spike or dip, so in these situations it’s of the essence that agents get the attention they deserve. In challenging times, a “customer-first” mentality tends to take hold — this is natural, but in focusing on satisfying customers, business leaders too often forget that the agent experience is essential to effective customer engagement. Fulfilling this objective requires contact center technology designed for this purpose. An organization’s agility and ability to invest adequate time and resources into agents is essential for its sustainability and contact center effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Conversational Computing
Subscriptions are the future of business. Subscribers are essential to an organization’s overall business potential because they generate recurring revenue. In a black-swan event, demand for a subscription may spike or dip, so in these situations it’s of the essence that subscriptions get the attention they deserve. In challenging times, a “subscriber-first” mentality tends to take hold — this is not unreasonable but in focusing on satisfying subscribers, business leaders too often forget that the subscription experience is essential to retaining them. Fulfilling this objective requires technology designed for subscriptions. An organization’s agility and ability to invest adequate time and resources into subscriptions is essential for its sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Subscription Billing, Conversational Computing, Subscription Management, Subscriber Experience
Partners play a key role in the revenue and growth of every organization. Whether channel selling is in assistance to internal sales or independent, what happens in partnering has ramifications that are simply too important to underestimate. The imperative to maintain business continuity with channel partners becomes painfully clear in a global pandemic, and that imperative demands that organizations cultivate partner excellence and channel performance. This effort should start with partner leadership and operations, with the objective of building channel relationships that can survive the test of time. Effective sales channel partnerships are built on mutual trust and a shared belief in the market opportunity, and recruiting and managing partners must be supported by effective processes and technology. The health of these relationships and the resulting revenue from the channel hinges on an effective partner experience, and this requires technology investments that enable leaders to not just manage channel performance, but help inspire it every single day.
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Analytics, Internet of Things, Data, Digital Technology, Operations & Supply Chain, Conversational Computing, partner management, Partner Experience
Products and services are the foundation of every organization, regardless of its industry or size. Products are essential to an organization’s overall business health because they generate revenue and engage buyers and customers. In a black-swan event, demand for a product may spike or dip, so in these situations it’s of the essence that products get the attention they deserve as they are marketed, sold, serviced and enhanced with innovations. In challenging times, a “customer-first” mentality tends to take hold — this is not unreasonable but in a rush to satisfy customers, business leaders too often forget that the product experience is essential to fulfilling on the customer experience and satisfying customers and buyers. An organization’s agility and ability to invest adequate time and resources into products is essential for its sustainability and operational effectiveness.
Topics: Sales, Customer Experience, Marketing, Office of Finance, Voice of the Customer, Analytics, Data, Product Information Management, Digital Technology, Operations & Supply Chain, Conversational Computing, product experience management
Suppliers play a critical role in supporting the operations and processes of every organization. Whether direct or indirect, an organization’s relationship with its suppliers has ramifications that are perilous to underestimate. The imperative to maintain business continuity becomes painfully clear in a global pandemic, and that imperative demands that organizations cultivate excellent supplier relationships.
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Analytics, Internet of Things, Data, Digital Technology, Operations & Supply Chain, Conversational Computing
Supercharging the customer experience (CX) is more than just an opportunity. It’s essential for every organization that looks to optimize engagement at every moment of the customer journey. In times such as these, when business continuity is a top priority, organizations must address the customer experience, especially if it has not been a focal point of the executive team.
Topics: Sales, Customer Experience, Human Capital Management, Marketing, Office of Finance, Voice of the Customer, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, Operations & Supply Chain, Intelligent CX, Conversational Computing
The imperative to infuse digital technology into your organization is not new, but it’s more essential than ever that organizations embrace digital transformation and business continuity to improve processes. I have recently written about the need for digital innovation in business continuity, outlining the steps every organization needs to take. These steps involve a close examination of the digital technology they can apply effectively for business continuity during a pandemic, natural disaster, cyber event or geopolitical situation.
Topics: Customer Experience, Human Capital Management, Office of Finance, Analytics, Data, Digital Technology
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
Topics: Analytics, Business Intelligence, Collaboration, Data Governance, Data, Digital Technology, Conversational Computing
Ventana Research recently announced its 2020 research agenda for data, continuing the guidance we’ve offered for nearly two decades to help organizations derive optimal value and improve business outcomes. Data volumes continue to grow while data latency requirements continue to shrink. Meanwhile, virtually every organization is confronting a need for good data governance.
Topics: Collaboration, Data Governance, Data Preparation, Information Management, Data, blockchain, data lakes
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
I’ve written before about blockchain’s significant potential. A lot of the current discussion on the topic centers on cryptocurrencies and financial trading platforms, both of which are already in operation. However, my focus is on its applicability to business generally, especially in B2B commerce, where I believe there is significant potential for it to serve as a universal data connector. There’s also a great deal of potential for blockchain to provide individuals with greater power in managing their identity and greasing the wheels of trade. That noted, those designing and planning to implement commerce-related blockchains must address fundamental issues if blockchain technology is to achieve its potential.
Topics: Sales, Human Capital Management, business intelligence, Collaboration, Internet of Things, Data, Product Information Management, Digital Commerce, Enterprise Resource Planning, blockchain, candidate engagement, collaborative computing, continuous supply chain
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
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
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 evaluated beqom 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, Predictive Planning, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, Subscription Management, agent management, intelligent sales
Here are some insights on SAP 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 SAP 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, Financial Performance Management (FPM), Sales Performance Management, Digital Technology, Digital Commerce, Predictive Planning, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, Subscription Management, agent management, intelligent sales