Analyst Perspectives

I have previously described the critical importance of context for enterprise adoption of generative artificial intelligence (GenAI) and agentic AI. Establishing trust in the content generated by GenAI is facilitated by grounding the models with real-world context from enterprise content and data, while AI agents designed to make context-aware decisions and take automated actions based on...

Since Alteryx was acquired by Clearlake Capital and Insight Partners in March 2024, the company has taken the opportunity to revamp its product portfolio and refresh its executive team. Alteryx has always offered a combination of functionality that spans the data and analytics lifecycle, including data integration, data preparation and data quality, as well as the development of analytics...

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...

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...

As I previously stated, although most enterprises are reliant on batch data processing, it is an artificial construct driven by the historical limitations of computing capabilities to generate and process data at the same time without impacting performance. While real-time data processing has previously been seen as a niche requirement for low-latency applications, it is increasingly being...

ISG recently published the 2025 ISG Buyers Guides for DataOps, providing an assessment of 51 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 generated three reports and five quadrants assessing providers in relation to overall DataOps, 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...

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...