Analyst Perspectives

As I previously described, context is everything for generative (GenAI) and agentic AI. Enterprises moved in large numbers to adopt foundational GenAI since its emergence into mainstream consciousness during 2023. As they did so, it quickly became clear that establishing trust in GenAI output would require enterprises to augment the realistic content generated by foundation models with real-world...

The mission to enable an autonomous enterprise, as I have articulated, remains aspirational for most organizations. Few have aligned business and technology leadership, strategy and execution to support autonomous operations at any scale. A shared definition is essential. An autonomous enterprise follows an operating model in which intelligent systems can sense conditions, decide actions, execute...

For most enterprises, the problem with artificial intelligence (AI) today is not a lack of ambition. It is confusion. Organizations are overwhelmed by pilots, tools, providers and hype, and they lack a clear path to autonomy and measurable outcomes. The way forward is to cut through the noise and reframe AI not as technology experimentation with generative AI (GenAI) and agentic AI, but as a...

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...

Let’s ground this conversation and cut through the misunderstood dialect about the impact of artificial intelligence (AI) on the software industry. AI is software and has been part of this category for more than several decades, and yes it has evolved into a new era of capability. Despite the noise from financial markets and headline pundits, the software industry is not being replaced by AI; it...

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...

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.

The term SaaS-pocalypse has been popularized lately to summarize the idea that the traditional business software providers’ business model is about to undergo a tectonic structural shift because artificial intelligence will change how work is performed, rendering applications and their subscription pricing models obsolete. In this telling, AI assistants or agents can reduce the need for humans to...

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...

Agentic AI is moving from pilots to production systems that execute work across enterprise applications, data platforms and business processes. As I’ve argued before, the value of AI is realized in action, not just answers, and enterprises are investing accordingly. One of the key questions now is how to coordinate the actions among different agents. My colleague Matt Aslett’s perspective on...

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...