Analyst Perspectives

Increased enterprise focus on artificial intelligence (AI) and generative AI (GenAI) has served to sharpen the focus on the need for trusted data and reliable analytics and data operations. The ISG State of Generative AI Market Report highlighted that elevated expectations and demands associated with AI are a forcing function for enterprises to take long-overdue steps to improve data and analytics processes to ensure that data that is clean, well-organized and compliant with regulatory...

Late 2024 saw the publication of the 2024 ISG Buyers Guides for DataOps, providing an assessment of 49 software providers offering products used by data engineers, data scientists, and data and AI professionals to facilitate the use of data for analytics and AI needs. The DataOps Buyers Guide research includes five reports which are focused on overall DataOps, Data Observability, Data Orchestration, Data Pipelines and Data Products. This is the first time in the industry when all software...

It is now more than two years since the launch of ChatGPT introduced the world to generative AI (GenAI) and large language models (LLMs). GenAI-based assistants and co-pilots are now widely adopted, with individuals and enterprises adopting GenAI models to automate the generation of text, digital images, audio, video and code, amongst other things.

I recently wrote about the need for enterprises to harness events to process and act upon data at the speed of business. The core technologiesthat enable enterprises to process and analyze data in real time have been in existence for many years and are widely adopted. However, streaming and events technologies are also commonly seen as a niche requirement, separate from an enterprise’s primary focus on batch processing of data at rest. One of the reasons for this is an entrenched reliance on...

Databricks recently announced its Series J funding round, successfully raising $10 billion at a valuation of $62 billion. Led by Thrive Capital alongside high-profile investors such as Andreessen Horowitz and Insight Partners, the company intends to invest this capital towards new artificial intelligence (AI) products, acquisitions and significant expansion of its international operations. In the announcement, Databricks reported that it expects to achieve an annual revenue run rate of $3...

Metadata management has played a role in data governance and analytics for many years. It wasn’t until the emergence of the data catalog as a product category just over a decade ago that enterprises had a platform for metadata-driven data management that could span multiple departments and use cases across an entire enterprise.

The degree to which data platforms are critical to efficient business operations cannot be overstated. Without data platforms, enterprises would be reliant on a combination of paper records, time-consuming manual processes and huge libraries of physical files to record, process and store business information. The extent to which that is unthinkable highlights the level at which today’s enterprises and society as a whole rely on data platforms. The core persistence, management, processing and...

Despite all the interest in artificial intelligence (AI) and generative AI (GenAI), ISG’s Buyers Guide for Data Platforms serves as a reminder of the ongoing importance of product experience functionality to address adaptability, manageability, reliability and usability. While new and emerging capabilities might catch the eye, features that address data platform security, performance and availability remain some of the most significant deal-breakers when enterprises are considering potential...

Too often, enterprises find that data is distributed across multiple silos on-premises and in the cloud. More than two-thirds of participants in ISG’s Market Lens Cloud Study are using a hybrid architecture involving both on-premises and cloud infrastructure for analytics and artificial intelligence deployments. Unifying data to achieve operational and analytic objectives requires complex data integration and management processes. Fulfilling these processes requires a smorgasbord of tools aimed...

I previously explained that data observability software has become a critical component of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data on a continual basis. Maintaining quality and trust is a perennial data management challenge, the importance of which has come into sharper focus in recent years thanks to the rise of artificial...

The adoption of cloud environments for analytic workloads has been a key feature of the data platforms sector in recent years. For two-thirds (66%) of participants in ISG’s Data Lake Dynamic Insights Research, the primary data platform used for analytics is cloud based. Many enterprises adopted cloud-based analytic data platforms with a view to improving operational efficiencies by reducing the need for upfront investment in physical infrastructure as well as the ability to scale cloud services...

I previously wrote about the importance of open table formats to the evolution of data lakes into data lakehouses. The concept of the data lake was initially proposed as a single environment where data could be combined from multiple sources to be stored and processed to enable analysis by multiple users for multiple purposes.

Although the terms data fabric and data mesh are often used interchangeably, I previously explained that they are distinct but complementary. Data fabric refers to technology products that can be used to integrate, manage and govern data across distributed environments, supporting the cultural and organizational data ownership and access goals of data mesh. Data fabric and data mesh are also both related to logical data management, which is the approach of providing virtualized access to data...

As I noted in the 2024 Buyers Guide for Operational Data Platforms, intelligent applications powered by artificial intelligence have impacted the requirements for operational data platforms. These applications, infused with contextually relevant recommendations, predictions and forecasting, are driven by machine learning and generative AI.

In today's rapidly evolving technological landscape, artificial intelligence (AI) governance has emerged as a critical ingredient for successful AI deployments. It helps build trust in the results of AI models, it helps ensure compliance with regulations and it is necessary to meet internal governance requirements. Effective AI governance must encompass various dimensions, including data privacy, model drift, hallucinations, toxicity and perhaps most importantly, bias. Unfortunately, we expect...

As I recently noted, the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption. I assert that through 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to increase trust in their data by leveraging metadata to understand how, when and where data is used in...

I previously wrote about data mesh as a cultural and organizational approach to distributed data processing. Data mesh has four key principles—domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance—each of which is being widely adopted. I assert that by 2027, more than 6 in 10 enterprises will adopt technologies to facilitate the delivery of data as a product as they adapt their cultural and organizational approaches to data ownership in the...

As I’ve written recently, artificial intelligence governance is a concern for many enterprises. In our recent ISG Market Lens study on generative AI, 39% of participants cited data privacy and security among the biggest inhibitors to adopting AI. Nearly a third (32%) identified performance and quality (e.g., erroneous results), and an equal amount (32%) mentioned legal risk.

The final of the men’s 100 meters at the Paris Olympics this summer was a reminder that being successful requires not just being fast but performing at the right time. Being fast is obviously a prerequisite for participating in an Olympic 100-meter final, and all the competitors finished the race in under 10 seconds, with just 0.12 seconds separating the first man from the last. While all the athletes were fast, what separated the winner of the gold medal—USA’s Noah Lyles—was execution. He was ...

Enterprises face a bewildering level of choice in relation to data platforms, as evidenced by the number of software providers and products assessed in our recent Data Platforms Buyers Guide. There are numerous data platform providers and products to choose from, but also a diverse array of functional and architectural options. Is the workload primarily operational or analytic? Will it be deployed on-premises or in the cloud? Should it be distributed or centralized? Data warehouse or data...

I have written on multiple occasions about the increasing proportion of enterprises embracing the processing of streaming data and events alongside traditional batch-based data processing. I assert that, by 2026, more than three-quarters of enterprises’ standard information architectures will include streaming data and event processing, allowing enterprises to be more responsive and provide better customer experiences.

Having just completed our AI Platforms Buyers Guide assessment of 25 different software providers, I was surprised to see how few provided robust AI governance capabilities. As I’ve written previously, data governance has changed dramatically over the last decade, with nearly twice as many enterprises (71% v. 38%) implementing data governance policies during that time. With all this attention on data governance, I had expected AI platform software providers would recognize the needs of...

The artificial intelligence and machine learning landscape was profoundly altered by the emergence of generative AI into the mainstream consciousness during 2023. The widespread availability of GenAI models and cloud services has lowered the barriers to individuals and enterprises engaging with AI for various use cases, including generating content, querying data, writing code, preparing data for analysis, documenting data pipelines and using software products more effectively. The impact that...

I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The Data Platforms Ventana Research Buyers Guide is the distillation of a year of market and product research by ISG and Ventana Research.

I previously wrote about the ongoing importance of event brokers and event management in enabling enterprises to adopt event-driven architecture and event stream processing. Many enterprises adopt EDA as the design pattern for maximizing events to deliver real-time business processes. There are many advantages to using EDA, including a cultural shift away from batch processing towards real-time analysis and decision-making.

I previously wrote about the potential for generative artificial intelligence technology to enhance the integration sector by facilitating outcome-driven approaches for automatically generating integration pipelines in response to declared business requirements. The use of GenAI in data and application integration remains nascent, but multiple software providers are embracing the potential for GenAI to improve the productivity of integration experts and facilitate self-service integration by...

I recently wrote about the role data observability plays in generating value from data by providing an environment for monitoring its quality and reliability. Data observability is a critical functional aspect of Data Operations, alongside the development, testing and deployment of data pipelines and data orchestration, as I explained in our Data Observability Buyers Guide. Maintaining data quality and trust is a perennial data management challenge, often preventing organizations from operating...

Enterprises are embracing the potential for artificial intelligence (AI) to deliver improvements in productivity and efficiency. As they move from initial pilots and trial projects to deployment into production at scale, many are realizing the importance of agile and responsive data processes, as well as tools and platforms that facilitate data management, with the goal of improving trust in the data used to fuel analytics and AI. This has led to increased attention on the role of data...

I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies as well as the importance of data orchestration to accelerate analytics and artificial intelligence. As I explained in the recent Data Observability Buyers Guide, data observability software is also a critical aspect of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an...

I previously wrote about the potential for rapid adoption of the data lakehouse concept as enterprises combined the benefits of data lakes based on low-cost cloud object storage with the structured data processing functionality normally associated with data warehousing. By layering support for table formats, metadata management and transactional updates and deletes as well as query engine and data orchestration functionality on top of low-cost storage of both structured and unstructured data,...

The artificial intelligence (AI) market is exploding with activity, which is part of the reason we recently announced that we have dedicated an entire practice at Ventana Research to the topic. Large language models (LLMs) and generative AI (GenAI) have taken the AI world by storm. In fact, we assert that through 2026, one-half of all AI investments will be based on generative rather than predictive AI. My colleague Rob Kugel has written about how AI can improve productivity and benefit the...

Many organizations have adopted DataOps to apply agile development, DevOps and lean manufacturing processes to the development, testing, deployment and orchestration of data integration and processing pipelines. The most likely ultimate outcome of these pipelines is the analytics reports and dashboards enterprises rely on to make business decisions.

Analytics software is used by business analysts and decision-makers to facilitate the generation of insights from data. It encompasses business intelligence and decision intelligence software, including reports and dashboards as well as embedded analytics and the development of intelligent applications infused with the results of analytic processes. Analytics software enables enterprises to improve business outcomes by operating more efficiently, accelerating product development and enhancing...

I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies. As I explained in the 2023 Data Orchestration Buyers Guide, today’s analytics environments require agile data pipelines that can traverse multiple data-processing locations and evolve with business needs.

I previously explained how master data management helps provide trust in data, making it one of the most significant aspects of an enterprise’s strategic approach to data management. More recently, I discussed how it has a role to play in accelerating data democratization as part of data intelligence initiatives. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives. While it is an established and mature...

I wrote recently about the role that data intelligence has in enabling enterprises to facilitate data democratization and the delivery of data as a product. Data intelligence provides a holistic view of how, when, and why data is produced and consumed across an enterprise, and by whom. This information can be used by data teams toensure business users and data analysts are provided with self-service access to data that is pertinent to their roles and requirements. Delivering data as a product...

Data and analytics have become increasingly important to all aspects of business. The modern data and analytics stack includes many components, which creates challenges for enterprises and software providers alike. As my colleague Matt Aslett points out, a better term might be modern data and analytics smorgasbord. There are arguments for and against using an assortment of tools versus a consolidated platform. For example, purchasing, integrating and deploying a variety of tools can be complex....

The development, testing and deployment of data pipelines is a fundamental accelerator of data-driven strategies, enabling enterprises to extract data from the operational applications and data platforms designed to run the business and load, integrate and transform it into the analytic data platforms and tools used to analyze the business. As I explained in our recent Data Pipelines Buyers Guide, data pipelines are essential to generating intelligence from data. Healthy data pipelines are...

As enterprises seek to increase data-driven decision-making, many are investing in strategic data democratization initiatives to provide business users and data analysts with self-service access to data across the enterprise. Such access has long been a goal of many enterprises, but few have achieved it. Only 15% of participants in Ventana Research’s Analytics and Data Benchmark Research say their organization is very comfortable allowing business users to work with data that has not been...

In the technology industry, 2023 will be remembered as the year of generative artificial intelligence. Yes, the world was made aware of GenAI when ChatGPT was publicly launched in November of 2022, but few knew the impact it would have at that point in time. Since then, GenAI has taken the world by storm, with vendors applying the technology to make it easier to ask questions about data, write code (including SQL), prepare data for analyses, document data pipelines and use software products...

Interest in artificial intelligence (AI) is exploding driven in large part by the widespread interest in generative AI. ISG’s AI Buyer Behavior Survey reported that more than 6 in 10 participants have at least one AI application in production. However, despite the ease with which individuals can use AI as a result of natural language processing, creating and managing AI models is still a challenge. First, there is a shortage of skills. Second, the process itself involves many parts, each of...

Cloud computing has had an enormous impact on the analytics and data industry in recent decades, with the on-demand provisioning of computational resources providing new opportunities for enterprises to lower costs and increase efficiency. Two-thirds of participants in Ventana Research’s Data Lakes Dynamic Insightsresearch are using a cloud-based environment as the primary data platform for analytics. 

It is well known that data integration, transformation and preparation represent a significant proportion of the time and effort required in any analytics project. Traditionally, operational data platforms are designed to store, manage, and process data to support worker-, customer- and partner-facing operational applications, and data is then extracted, transformed, and loaded (or “ETLed”) into a separate analytic data platform, which is designed to store, manage, process, and analyze data....

I have previously written about the impact of intelligent operational applications on the requirements for data platforms. Intelligent applications are used to run the business but also deliver personalization, recommendations and other features generated by machine learning and artificial intelligence. As such, they require a combination of operational and analytic processing functionality. The emergence of these intelligent applications does not eradicate the need for separate analysis of...

Unstructured data has been a significant factor in data lakes and analytics for some time. Twelve years ago, nearly a third of enterprises were working with large amounts of unstructured data. As I’ve pointed out previously, unstructured data is really a misnomer. The data is structured; it's just not structured into rows and columns that fit neatly into a relational table like much of the other information enterprises process. Consequently, it requires different skills, different technology...

The increasing importance of intelligent operational applications driven by artificial intelligence (AI) is blurring the lines that have traditionally divided the requirements between operational and analytic data platforms. Operational data platforms have traditionally been deployed to support applications targeted at business users and decision-makers to run the business, with analytic data platforms typically supporting applications used by data and business analysts to analyze the business.

We’ve been saying for years that natural language processing (NLP) and natural language analytics would greatly expand access to analytics. However, prior to the explosion of generative AI (GenAI), software providers had struggled to bring robust natural language capabilities to market. It required considerable manual effort. Many analytics providers had introduced natural language capabilities, but they didn’t really resonate with enterprise requirements. They required significant effort to...

In recent years, many enterprises have migrated data platform workloads from on-premises infrastructure to cloud environments, attracted by the promised benefits of greater agility and lower costs. The scale of cloud data platform adoption is illustrated by Ventana Research’s Data Lakes Dynamic Insights research: For two-thirds (66%) of participants, the primary data platform used for analytics is cloud based. As the quantity and importance of the data platform workloads deployed in the cloud...

Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes.

Ventana Research has announced its market agenda for 2024, continuing a 20-year tradition of credibility and trust in our objective efforts to educate and guide the technology industry. Our research and insights are backed by our expertise and independence, and 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...

Discussion about potential deployment locations for analytics and data workloads is often based on the assumption that, for enterprise workloads, there is a binary choice between on-premises data centers and public cloud. However, the low-latency performance or sovereignty characteristics of a significant and growing proportion of workloads make them better suited to data and analytics processing where data is generated rather than a centralized on-premises or public cloud environment. ...

As articulated in Ventana Research’s Data Platforms Buyer’s Guide and DataOps Buyer’s Guide research, the combination of cloud computing and advanced analytics has lowered the cost of storing and processing large volumes of data, accelerating the emergence of new data platform and data operations products that enable organizations to gain operational efficiency and competitive advantage. The right combination of data platform and data management products is essential to ensure that the right...

The phrase ‘big data’ may have largely gone out of fashion, but the concept of storing and processing all relevant data continues to be important for enterprises seeking to be more data-driven. Doing so requires analytic data platforms capable of storing and processing data in multiple formats and data models. This will be an important focus for the forthcoming Data Platforms Buyer’s Guide 2024. 

I recently discussed how fashion has a surprisingly significant role to play in the data market as various architectural approaches to data storage and processing take turns enjoying a phase in the limelight. Pendulum swing is a theory of fashion that describes the periodic movement of trends between two extremes, such as short and long hemlines or skinny and baggy/flared trousers. Pendulum swing theory is similarly a factor in data technology trends, with an example being the oscillation...

I previously described how Oracle had positioned its database portfolio to address any and all data platform requirements. The caveat to that statement at the time was that any organization wanting to take advantage of the company’s flagship Oracle Autonomous Database could only do so using Oracle Cloud Infrastructure (OCI) cloud computing service, their own datacenter or a hybrid cloud environment. The widespread popularity of Oracle Database and the advanced automation capabilities delivered...

I recently articulated some of the reasons why IT teams can fail to deliver on the business requirements for data and analytics projects. This is an age-old and multifaceted problem that is not easily solved. Organizations have a role to play in alleviating the issue by ensuring that their business processes and project planning support a collaborative approach in which business and IT professionals work together. Data and analytics product vendors can also help by delivering products that are...

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 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...

I previously wrote about the challenge facing distributed SQL database providers to avoid becoming pigeonholed as only being suitable for a niche set of requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established providers and get a foot in the door with customer accounts. Expanding and retaining those accounts is not necessarily easy, however, especially as general-purpose data platform providers...

Alteryx was founded in 1997 and initially focused on analyzing demographic and geographically organized data. In 2006, the company released its eponymous product that established its direction for what the product is today. In 2017, it went public in an IPO on the NYSE. At the time of the IPO, Alteryx was focusing much of its marketing efforts on the data preparation market, particularly to support Tableau. Throughout this time though, Alteryx offered much more than data preparation. As a...

I previously discussed the trust and accuracy limitations of large language models, suggesting that data and analytics vendors provide guidance about potentially inaccurate results and the risks of creating a misplaced level of trust. In the months that have followed, we are seeing some clarity from these vendors about the approaches organizations can take to increase trust and accuracy when developing applications that incorporate generative AI, including fine-tuning and prompt engineering. It...

In my past perspectives, I’ve written about the evolution from data at rest to data in motion and the fact that you can’t rely on dashboards for real-time analytics. Organizations are becoming more and more event-driven and operating based on streaming data. As well, analytics are becoming more and more intertwined with operations. More than one-fifth of organizations (22%) describe their analytics workloads as real time in our Data and Analytics Benchmark Research and nearly half (47%) of...

I previously described how Databricks had positioned its Lakehouse Platform as the basis for data engineering, data science and data warehousing. The lakehouse design pattern provides a flexible environment for storing and processing data from multiple enterprise applications and workloads for multiple use cases. I assert that by 2025, 8 in 10 current data lake adopters will invest in data lakehouse architecture to improve the business value generated from the accumulated data.

I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Mobile Analytics Buyers Guide 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 the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum...

The 2023 Ventana Research Buyers Guide for Mobile Analytics research enables me to provide observations about how the market has advanced. 

I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Collaborative Analytics Buyers Guide 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 the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the...

The 2023 Ventana Research Buyers Guide for Collaborative Analytics research enables me to provide observations about how the market has advanced. 

I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Analytics and Data Buyers Guide 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 the real-world criteria incorporated in a request for proposal to Analytics vendors supporting the spectrum...

The 2023 Ventana Research Buyers Guide for Analytics and Data research enables me to provide observations about how the market has advanced.

The data platforms market may appear to have little or nothing to do with haute couture, but it is one of the data sectors most strongly influenced by the fickle finger of fashion. In recent years, various architectural approaches to data storage and processing have enjoyed a phase in the limelight, including data warehouse, data mart, data hub, data lake, cloud data warehouse, object storage, data lakehouse, data fabric and data mesh. These approaches are often heralded as the next big thing,...

I have written before about the rising popularity of the data fabric approach for managing and governing data spread across distributed environments comprised of multiple data centers, systems and applications. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data across multiple data platforms and cloud environments. The data fabric approach is also proving attractive to vendors, including Microsoft, as a...

At one point, analytics and business intelligence were considered non-mission critical activities. One of the primary concerns in designing analytics systems was to ensure they didn’t interfere with or draw computing resources away from operational systems. But today, analytical systems are integral to many aspects of operations. More than 9 in 10 participants in our Analytics and Data Benchmark Research reported analytics had improved activities and processes. However, most analytics and BI...

If I had a magic wand, I would want to add scenario evaluation to all business intelligence tools on the market. I have previously written about the need to make intelligent decisions with decision intelligence. The data and analytics markets have evolved so that organizations have far greater capabilities to utilize data in decision-making processes. While there is some convergence around the concept of decision intelligence, there are still several “islands” of decision-making. Analyzing...

It is a mark of the rapid, current pace of development in artificial intelligence (AI) that machine learning (ML) models, until recently considered state of the art, are now routinely being referred to by developers and vendors as “traditional.” Generative AI, and large language models (LLMs) in particular, have taken the AI world by storm in the past year, automating and accelerating the development of content, including text, digital images, audio and video, as well as computer programs and...

As I have previously explained, we expect an increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. These systems rely on the analysis of data in the operational data platform to accelerate worker decision-making or improve customer experience.

The publication of Ventana Research’s 2023 Operational Data Platforms Value Index earlier this year highlighted the importance of incorporating analytic processing into operational applications to deliver personalization and recommendations for workers, partners and customers. This importance is being accelerated by interest in generative AI, especially large language models. The emergence of intelligent applications has impacted the requirements for operational data platforms with the need to...

Despite best intentions, many organizations still struggle with some fundamental aspects of data processing and analytics. Taking data from operational applications and making it available for analysis is a first step, but data management remains a perennial challenge. Data movement and transformation difficulties can lead to delays and data quality problems that prevent organizations from generating value from data. The inability to govern and integrate data from multiple data sources prevents...

I previously explained the arguments in favor of adoption of distributed SQL databases, the new generation of operational data platforms designed to combine the benefits of the relational database model and native support for distributed cloud architecture. It is critical for distributed SQL vendors to engage with developers to ensure they are considering the importance of resilience that spans multiple data centers and/or cloud regions as they choose the databases that will underpin...

Data fabric has grown in popularity as organizations struggle to manage data spread across multiple data centers, systems and applications. By providing a technology-driven approach to automating data management and governance across distributed environments, data fabric is attractive to organizations seeking to simplify and standardize data management. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data...

The data and analytics sector rightly places great importance on data quality: Almost two-thirds (64%) of participants in Ventana Research’s Analytics and Data Benchmark Research cite reviewing data for quality and consistency issues as the most time-consuming task in analyzing data. Data and analytics vendors would not recommend that customers use tools known to have data quality problems. It is somewhat surprising, therefore, that data and analytics vendors are rushing to encourage customers...

MicroStrategy is a long-standing business intelligence and analytics vendor that operates worldwide. Founded in 1989, this publicly traded company with hundreds of millions of dollars in revenue recently held its first in-person conference since prior to the pandemic. Similar to previous in-person events, the event was well attended by about 2,000 attendees and exhibitors. The theme, “MicroStrategy ONE,” is a way to explain the breadth of capabilities the company offers. The breadth of the...

Artificial intelligence (AI) has evolved from a highly specialized niche technology to a worldwide phenomenon. Nearly 9 in 10 organizations use or plan to adopt AI technology. Several factors have contributed to this evolution. First, the amount of data they can collect and store has increased dramatically while the cost of analyzing these large amounts of data has decreased dramatically. Data-driven organizations need to process data in real time which requires AI. In addition, analytics...

The data platforms market has traditionally been divided between products specifically designed to support operational or analytic workloads, with other market segments having emerged in recent years for data platforms targeted specifically at data science and machine learning (ML), as well as real-time analytics. More recently, we have seen vendor strategies evolving to provide a more consolidated approach, with data platforms designed to address a combination of analytics and data science, as...

The recent publication of our Value Index research highlights the impact of intelligent applications on the operational data platforms sector. While we continue to believe that, for most use cases, there is a clear, functional requirement for either analytic or operational data platforms, recent growth in the development of intelligent applications infused with the results of analytic processes, such as personalization and artificial intelligence (AI)-driven recommendations, has increasing...

In my perspective on decision intelligence, I lamented the fact that business intelligence technologies have left the rest of the exercise to the reader for too long. Making a decision is a process that involves many steps and many people. Decision-making is so complicated and divorced from day-to-day business processes that organizations have had to create entirely separate teams to focus on the analytics and data to support it. One aspect of the decision-making process that can be enhanced by...

As engagement with customers, suppliers and partners is increasingly conducted through digital channels, ensuring that infrastructure and applications are performing as expected is not just important but mission critical. My colleague, David Menninger, recently explained the increasing importance of observability to enable organizations to ensure that their systems and applications are operating efficiently. Observability has previously been the domain of the IT department but is increasingly...

Success with streaming data and events requires a more holistic approach to managing and governing data in motion and data at rest. The use of streaming data and event processing has been part of the data landscape for many decades. For much of that time, data streaming was a niche activity, however, with standalone data streaming and event-processing projects run in parallel with existing batch-processing initiatives, utilizing operational and analytic data platforms. I noted that there has...

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...

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...

I’ve previously written about the analytics continuum, which spans a range of capabilities including reporting, visualization, planning, real-time processes, natural language processing, artificial intelligence and machine learning. I’ve also written about the analysis that goes into making intelligent decisions with decision intelligence. In this perspective, I’d like to focus on one end of the analytics continuum, which I’ll label advanced analytics.

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,...

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...

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...

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...

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.

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...

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.

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.

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.

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...

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.