Analyst Perspectives

By now it should be obvious that artificial Intelligence (AI) and agents in all of their forms are on the brink of changing how finance and accounting departments operate. The basic outlines are already in place, but it’s not clear how or how rapidly day-to-day operations will evolve, as well as (by definition) what surprises are in store.

The software industry has entered one of its most consequential pricing and value inflection points since the shift to cloud computing. AI is not an incremental capability layered onto existing software; it is fundamentally redefining how software should create value and, in turn, how that value must be priced. The traditional model of seat-based subscription pricing was designed for systems that...

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 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...

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...

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...

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...

Tokenization is an emerging topic for business software providers. Tokenization is the process of representing something valuable or complex with a simple computer-readable substitute to enhance security or improve the efficiency and usability of a system. Tokens are already widely used in commerce. It’s now common for sensitive data used in a transaction, such as a bank account number or credit...

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...

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...

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...