Analyst Perspectives

I have previously explained the critical importance of data to successful artificial intelligence (AI) initiatives, including generative and agentic AI. While enterprises have demonstrated the value of AI through small-scale initiatives, scaling these efforts has highlighted the need to coordinate AI and data programs more effectively. Providers that can address the full combination of AI and...

I have previously written about the impact that artificial intelligence (AI) and agentic AI are having on the requirements for data platforms. AI and data platforms are intrinsically linked, with the latter providing the underlying data persistence and data processing capabilities that support model development and training, as well as the inferencing capabilities of intelligent operational...

I previously wrote about the potential for generative artificial intelligence (GenAI) to change the face of analytics and facilitate data literacy and data democratization by enabling business users without specialist analytic skills to discover and analyze data. At the time of writing, GenAI-based interfaces were already being adopted by business intelligence (BI) software providers to...

The term “sovereign AI and data” became increasingly prevalent in recent years, initially driven by cloud infrastructure providers responding to the need to support regional regulations with sovereign cloud offerings. However, the use of sovereign cloud infrastructure is not required to deliver compliance with data sovereignty regulations. In fact, our research indicates that having the autonomy...

The concept of software components orchestrating across boundaries is not new—it has appeared in mainframe program libraries, Service-Oriented Architectures (SOA) and Enterprise Service Buses (ESBs). APIs became the next wave of this idea, powering cloud, mobile and software-as-a-service (SaaS) ecosystems.

I previously stated that too many enterprises allow the IT department to be wholly responsible for data and analytics, with the risk that strategies become divorced from business objectives and KPIs. I also stated that a pragmatic approach to organizing and operating data, analytics and artificial intelligence (AI) initiatives is essential to treating data as a business discipline. There are...

Time is a critical element in business decision-making. To make decisions at the speed of business, it is fundamentally important that enterprises have access to relevant data in a timely manner. It is also essential, however, that data is processed and analyzed in the correct time sequence. In order to decide when to buy or sell, a trader needs to be sure that the price data they are analyzing...

I previously described data intelligence as fundamental to providing data analysts and business users with governed self-service access to data across an enterprise by delivering information about how data is produced and consumed across the organization. Data intelligence relies on a combination of technical and business metadata and functionality for knowledge graph, data inventory, data...

I recently wrote about the evolving requirements for operational data platforms to support artificial intelligence (AI) workloads. Operational data platforms providers are rapidly updating their products, driven by the development of intelligent applications infused with contextually relevant recommendations, predictions and forecasting that are in turn driven by machine learning (ML), generative...

A little over two years ago, I observed that the pendulum of fashion in the data management sector was swinging away from multiple best-of-breed tools and toward consolidated platforms. Enterprise software portfolios take time to evolve, but the trend toward consolidation has clearly been evident in the product strategies of large data management providers since then. Many providers have combined...

Cloudera recently hosted its EVOLVE25 event in New York, introducing updates that reinforce its commitment to open data architectures and hybrid data management. The announcements centered on a unified platform across cloud and on-premises deployments. Cloudera also announced the Iceberg REST Catalog and Cloudera Lakehouse Optimizer, both of which extend the provider’s ability to manage and share...

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...

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 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...

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...

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...

I have previously described how data as a product was initially closely aligned with data mesh, a cultural and organizational approach to distributed data processing. As a result of data mesh’s association with distributed data, many assumed that the concept was diametrically opposed to the data lake, which offered a platform for combining large volumes of data from multiple data sources. That...