Analyst Perspectives

I have previously written about the critical importance of data management to the development of artificial intelligence (AI) applications and agentic AI. The importance of data management is nothing new, but automation of business processes and decision-making raises the stakes in terms of the expectations and the risks. The need for enterprises to have trust in their data governance and data...

I have written several times this year about Model Context Protocol (MCP) and its importance in enabling agentic artificial intelligence (AI) use cases. Numerous data platform, data management, data operations and real-time data software providers have added support for MCP to their products in recent months. MCP has become so ubiquitous, in fact, that it is easy to forget the protocol was only...

I previously wrote about the importance of knowledge graphs to data intelligence and artificial intelligence (AI). A knowledge graph can surface information about the relationships and dependencies between virtual and physical objects by providing a structured representation of data that identifies the connections between entities and attributes. This representation of enterprise knowledge can be...

As I recently explained, treating data as a business discipline—rather than a technical one—is a critical component of delivering competitive advantage through investment in data processing, analytics and artificial intelligence. As enterprises embrace data as a business discipline, it is increasingly important that the products used for data processing and management enable collaboration between...

I recently explained the significance of data management as an enabler of strategic adoption of artificial intelligence. Data management enables enterprises to ensure that data is valid, consistent and trusted for operational use cases and analytic decision-making. Large volumes of data are required to train models, making data management and data governance critical to AI. Data quality and data...

I previously wrote about the role that data intelligence catalogs play in enabling business leaders to understand the use of data across an enterprise. I also recently wrote about the importance of treating data as a business discipline to ensure that data projects are aligned with business strategy objectives. As I noted, although many data catalog products provide enterprises with information...

The emergence of natural language analytics interfaces driven by generative artificial intelligence (GenAI) models has accelerated enterprise initiatives to enable data democratization—making data available to business decision-makers without the need to train them to use business intelligence (BI) tools. It has also heightened the need for agreed semantic models and business metrics, as well as...

I previously described data mesh as a cultural and organizational approach to distributed data ownership, access and governance, rather than a product that could be acquired or even a technical architecture that could be built. While that remains true, many data management software providers have adapted their products in recent years to address the four key principles of data mesh:...

I previously wrote about IBM’s strategy of consolidating analytics, data and artificial intelligence (AI) functionality from various products under its watsonx brand, which was launched in 2023 to address the AI development life cycle, as well as data storage processing and AI governance. The company has added two more offerings to its watsonx portfolio in recent months, combining established...

The IT department of any enterprise is integral to implementing and managing the execution of its data objectives, just as the finance department is integral to implementing and managing financial objectives. Few enterprises would allow the finance department complete autonomy to define financial strategies; however, too many enterprises allow the IT department to define data strategies. Treating...

Fragmentation is a pervasive problem for enterprises seeking to take advantage of data generated by various applications to drive business decision-making. For almost as long as the IT industry has existed enterprises have struggled to combine and integrate information from multiple data siloes created by a variety of applications and business units. Creating a “single version of the truth” that...

I have written several times recently about the importance of data in supporting artificial intelligence (AI), including generative AI (GenAI) and agentic AI. From a data platforms perspective, this is most evident in the role that analytic data platforms play in supporting the training and fine-tuning of AI models. Operational data platforms also have a role to play in supporting enterprise AI...

Organizational change management is the discipline and process of guiding individuals, teams and organizations to a desired future structure. This is frequently an issue when implementing new technology designed to enable new strategies or business models. It is often a challenge because of human resistance to change, which is driven by a desire for stability, fear of personal consequences and...

ISG Software Research recently published the 2025 Buyers Guide for Real-Time Data, providing an assessment of 43 software providers offering products used by analytics and data professionals to facilitate the use of real-time data. The Real-Time Data Buyers Guide research includes five reports which are focused on overall Real-Time Data, Application Integration, Messaging and Event Processing,...

ISG Software Research’s expertise examines the software provider landscape through two lenses: business applications (including office of finance, human capital management (HCM) and customer experience) and IT and technology (including digital business, digital technology, artificial intelligence (AI) and analytics and data). Most software providers fall into one of these two high-level expertise...

Databricks recently hosted its Data+AI Summit in San Francisco, an event that attracted 22,000 attendees. That’s a far cry from the Spark Summit I attended in 2016. As pointed out in my coverage of Databricks massive funding round earlier this year, the company was originally founded as a provider of cloud-based Apache Spark services. Since its inception, Spark has been associated with processing...

I have been saying for several years that success with streaming data requires enterprises to manage data in motion alongside data at rest, rather than treating streaming as a niche activity. Software providers have also been moving in this direction. Many established data management providers have added the ability to manage, store and process streaming data alongside their existing batch data...

One of the key questions that will need to be solved if agentic artificial intelligence is to fulfill its potential is which technologies and providers will serve the role of orchestrating communication and integration between the various models, applications and data repositories involved. ISG Research defines agentic AI as software designed to execute business processes through autonomous...

If a single phrase could sum up the big data craze of a dozen or so years ago, it would be “more data beats better algorithms.” Attributed to Google research director Peter Norvig, the quote effectively summarized a research paper Norvig jointly authored called The Unreasonable Effectiveness of Data and was embraced by big data enthusiasts as articulating the prevalent thinking that enterprises...

My colleagues have recently described how agentic artificial intelligence (AI) has the potential to revolutionize enterprise computing by automating the handling of static and dynamic complexity to enable software to take action without the need for human intervention. Put simply, agentic AI is the orchestration of the execution of discreet business tasks by a combination of software components...