Analyst Perspectives

I have been using the term “data pantry” (somewhat tongue in cheek) to describe a curated, governed and readily accessible collection of enterprise data that business users can draw on to support the use of core business software. This includes ERP, CRM and supply chain management software, as well as essential business processes such as analytics, forecasting and planning. This form of data...

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...

Digitally transforming finance operations has been a priority since 2020. Especially with the application of artificial intelligence in all its forms, software can streamline purchasing and the procure-to-pay (P2P) cycle, shorten process times, reduce unnecessary costs, provide greater visibility into cash flows, increase control and improve results. The application of AI will increasingly...

Prophix One is a finance and accounting platform that provides enterprises a single point of entry and common authentication for financial applications. It uses in-memory cube technology to enable enterprises to handle complex planning tasks built on very large datasets to eliminate barriers to integrated business planning. The platform and its dedicated data store support the use of artificial...

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...

The reason why artificial intelligence (AI) has been the focus of endless keynote speeches, product roadmap announcements by ERP providers and development efforts across all categories of business software is that AI-embedded systems promise (and will ultimately deliver) software that more closely molds its operation to the needs of a specific enterprise and individual user. The technology is...

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...

It’s clear that the contact center industry is experiencing a generational technological transformation. The effects of artificial intelligence (AI) and the automation it delivers have been extraordinary. But this isn’t the first time something like this has happened. People with long memories can recall the disruptive effects of CTI, for example, which enabled screen pops and integration of...

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...

Managing corporate income taxes effectively is important since these represent the second or third biggest expense for many enterprises. However attractive minimizing them might be, following all applicable tax laws and regulations is equally as important because failure to pay what the laws require can result in significant monetary fines and, potentially, reputational risk. Since there can be...

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...

There is a shift underway in how enterprises structure their software stacks in relation to contact centers. Multiple factors have coalesced to reduce the importance of the traditional call handling infrastructure (i.e., the ACD) as the foundational element of the tech stack. It no longer has to be the focus of buying or deployment decisions. It’s been superseded by a more extensible (but...

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...

The recent Oracle AI World event did not unveil much that was stunning “new” news for its business applications, because much of what has been evolving was presented already and has been in front of analysts for some months. What was on display, though, was steady progress to its advancing capabilities coming out every quarter. The company has focused on the development of agents and an agentic...

Oracle NetSuite’s presentations and announcements at its recent user group meeting, SuiteWorld, focused on an initiative called NetSuite Next, a set of artificial intelligence (AI), generative AI (GenAI) and agentic AI capabilities that will be an inherent part of the NetSuite suite of applications starting in 2026. The company’s objective is to facilitate customers’ transition to this AI-centric...

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...