MicroStrategy is a long-standing business intelligence and analytics vendor that operates worldwide. Founded in 1989, this publicly traded company with hundreds of millions of dollars in revenue recently held its first in-person conference since prior to the pandemic. Similar to previous in-person events, the event was well attended by about 2,000 attendees and exhibitors. The theme, “MicroStrategy One,” is a way to explain the breadth of capabilities the company offers. The breadth of the product offering is one of the company’s greatest strengths, but also one of its biggest challenges.
Topics: embedded analytics, Analytics, Business Intelligence, Digital Technology, natural language processing, Analytics & Data
Artificial intelligence (AI) has evolved from a highly specialized niche technology to a worldwide phenomenon. Nearly 9 in 10 organizations use or plan to adopt AI technology. Several factors have contributed to this evolution. First, the amount of data they can collect and store has increased dramatically while the cost of analyzing these large amounts of data has decreased dramatically. Data-driven organizations need to process data in real time which requires AI. In addition, analytics vendors have been augmenting business intelligence (BI) products with AI. And recently, ChatGPT has raised awareness of AI and instigated research and experimentation into new ways in which AI can be applied. This perspective, the second in a series on generative AI, introduces some of the concepts behind ChatGPT, including large language models and transformers. Understanding how these models work can help provide a better understanding of how they should be applied and what cautions are necessary.
Topics: Analytics, Digital Technology, natural language processing, AI & Machine Learning, Analytics & Data
In my perspective on decision intelligence, I lamented the fact that business intelligence technologies have left the rest of the exercise to the reader for too long. Making a decision is a process that involves many steps and many people. Decision-making is so complicated and divorced from day-to-day business processes that organizations have had to create entirely separate teams to focus on the analytics and data to support it. One aspect of the decision-making process that can be enhanced by technology is collaboration.
Topics: Analytics, Business Intelligence, Digital Technology, Analytics & Data, Collaborative & Conversational Computing
I’ve previously written about the analytics continuum, which spans a range of capabilities including reporting, visualization, planning, real-time processes, natural language processing, artificial intelligence and machine learning. I’ve also written about the analysis that goes into making intelligent decisions with decision intelligence. In this perspective, I’d like to focus on one end of the analytics continuum, which I’ll label advanced analytics.
Topics: Analytics, Digital Technology, AI & Machine Learning, Analytics & Data
Ventana Research recently announced its 2023 Market Agenda for Analytics, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value from technology investments to improve business outcomes.
Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, natural language processing, Process Mining, Analytics & Data, Collaborative & Conversational Computing
For far too long, business intelligence technologies have left the rest of the exercise to the reader. Many of these tools do an excellent job providing information in an interactive way that lets organizations dive into the data and learn a lot about what has happened across all aspects of the business. More recently, many of these tools have added augmented intelligence capabilities that help explain why things happened. But rarely did any of these tools provide information about what to do or how to evaluate the alternative ways in which you might respond.
Topics: business intelligence, Analytics, Digital Technology, AI and Machine Learning, Analytics & Data
Analytics processes are all about how organizations use data to create metrics that help manage and improve operations. Yet, the discipline applied to analytics processes seems to be lacking compared to data processes. I’ve pointed out that the weak link in data governance is often analytics. Organizations can also do a better job tying AnalyticOps to DataOps and do more to define and manage metrics. Our research has shown that creating and managing metrics in a semantic model improves analytics processes.
Topics: Analytics, Business Intelligence, Data Governance, Data, Digital Technology, Analytics & Data
In previous perspectives in this series, I’ve discussed some of the realities of cloud computing including costs, hybrid and multi-cloud configurations and business continuity. This perspective examines the realities of security and regulatory concerns associated with cloud computing. These issues are often cited by our research participants as reasons they are not embracing the cloud. To be fair, the majority of our research participants are embracing the cloud. However, among those that have not yet made the transition to the cloud, security and regulatory concerns are among the most common issues cited across the various studies we have conducted.
Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Digital Technology, AI & Machine Learning, Analytics & Data, Governance & Risk
Recently, I suggested you need to “mind the gap” between data and analytics. This perspective addresses another gap — the gap in skills between business intelligence (BI) and artificial intelligence/machine learning (AI/ML).
Topics: Analytics, Business Intelligence, Digital Technology, AI & Machine Learning, Analytics & Data
If you’ve ever been to London, you are probably familiar with the announcements on the London Underground to “mind the gap” between the trains and the platform. I suggest we also need to mind the gap between data and analytics. These worlds are often disconnected in organizations and, as a result, it limits their effectiveness and agility.
Topics: business intelligence, embedded analytics, Analytics, Data Governance, Data Management, data operations, Analytics & Data