Analyst Perspectives

I recently completed the latest edition of our Business Planning Buyers Guide, which reviews and assesses the offerings of 14 providers of this software. One of the points that I look at is whether and to what extent the software provider offers out-of-the-box external data useful for forecasting, planning, analysis and evaluation. What I discovered is that the availability of this type of vital information is exceedingly slim.

When considered at all, unintended consequences are expected to be negative. As enterprises and institutions rush to adopt artificial intelligence and generative AI, the focus is on the potentially unforeseen—and unforecastable—unfavorable outcomes. However, one very likely positive impact of AI investments in business computing is the near-effortless availability of consistently reliable data for whatever task is at hand. This is coming about because of the need to have large, relevant data...

Artificial Intelligence and generative AI are beginning to change how enterprises do many things, especially planning and budgeting. This technology has the potential to significantly redefine the mission of the financial planning and analysis group. It will do so by substantially reducing the time spent on the purely mechanical aspects of day-to-day tasks. AI is also making it easier for executives and managers to rapidly forecast, plan and analyze to promote deeper situational awareness and...

As I explained in our recent Buyers Guide for Data Platforms, the popularization of generative artificial intelligence (GenAI) has had a significant impact on the requirements for data platforms in the last 18 months. While there is an ongoing need for data platforms to support data warehousing workloads involving analytic reports and dashboards, there is increasing demand for analytic data platform providers to add dedicated functionality for data engineering, including the development,...

The emergence of generative artificial intelligence (GenAI) has significant implications at all levels of the technology stack, not least analytics and data products, which serve to support the development, training and deployment of GenAI models, and also stand to benefit from the advances in automation enabled by GenAI. The intersection of analytics and data and GenAI was a significant focus of the recent Google Cloud Next ’24 event. My colleague David Menninger has already outlined the key...

I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies as well as the importance of data orchestration to accelerate analytics and artificial intelligence. As I explained in the recent Data Observability Buyers Guide, data observability software is also a critical aspect of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an...

Oracle held an industry analyst summit recently where the focus was on artificial intelligence (AI) and embedded AI. At the event, Oracle demonstrated progress in adding useful AI-enabled capabilities to its business applications, especially in finance and accounting, supply chain, HR and revenue management. To put this into context, across the software industry, AI is already at work in many finance-focused applications that are currently available, albeit often in limited release. We are in...

I previously wrote about the potential for rapid adoption of the data lakehouse concept as enterprises combined the benefits of data lakes based on low-cost cloud object storage with the structured data processing functionality normally associated with data warehousing. By layering support for table formats, metadata management and transactional updates and deletes as well as query engine and data orchestration functionality on top of low-cost storage of both structured and unstructured data,...

We’re quickly approaching the moment when it becomes clear that artificial intelligence (AI) and generative AI (GenAI) will not be free. As that happens, we will discover who’s willing to pay how much and for what. After nearly 18 months of unlimited use-case fantasizing, it should be obvious that not all the potential applications of AI can be realized over the next three to five years because they fail a cost/benefit test. In theory, AI’s potential is almost limitless, but so far, little...

Many organizations have adopted DataOps to apply agile development, DevOps and lean manufacturing processes to the development, testing, deployment and orchestration of data integration and processing pipelines. The most likely ultimate outcome of these pipelines is the analytics reports and dashboards enterprises rely on to make business decisions.