Analyst Perspectives

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...

A little under a year ago, I explained how Google was positioning its BigQuery product as a unified data platform for processing data in multiple formats, across multiple locations, for multiple use cases—including business intelligence (BI) and artificial intelligence (AI)—using a combination of multiple data engines, including SQL, Spark and Python. The evolution of BigQuery as the focus of...

Founded as Software Development Laboratories in 1977, Oracle is a behemoth in the software industry, generating more than $50 billion in revenue in its fiscal year 2024. Originally focused solely on the relational database market, the software provider operated as Relational Systems, Inc. for several years before adopting the name Oracle in 1982. The company went public in 1986 and became one of...

As enterprises embrace the potential opportunities presented by artificial intelligence (AI), they are quickly finding that good data management is a prerequisite. As was explained in ISG’s State of Generative AI Market Report, AI requires data that is clean, well-organized and compliant with regulatory standards. There are multiple challenges to delivering AI-ready data, including combining...