{"id":1683,"date":"2024-03-25T14:32:06","date_gmt":"2024-03-25T14:32:06","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1683"},"modified":"2024-03-25T14:32:06","modified_gmt":"2024-03-25T14:32:06","slug":"all-things-ai-the-gartner-data-analytics-summit-2024","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/all-things-ai-the-gartner-data-analytics-summit-2024\/","title":{"rendered":"All Things AI: The Gartner Data & Analytics Summit 2024"},"content":{"rendered":"

I recently had the pleasure of attending my favorite conference of the year: The Gartner Data and Analytics Summit, which convened in Orlando, Florida on March 10-13, 2024. As usual, it was educational, fun, and social, plus I came away with many useful tips and actionable visions of where we\u2019re going in our field of data and analytics (D&A). Please allow me to share what I learned, along with my general observations.<\/p>\n

Artificial Intelligence (AI)<\/h2>\n

The 2023 summit strongly focused on data fabric, which in the Gartner view is the modern rallying point for unifying all data management technologies and best practices. However, many attendees scratched their heads and asked me something like this: \u201cI can see why Gartner promotes the data fabric, but why does Gartner have so little to say about AI, at a time when the rest of the IT world is lit up by it?\u201d Well, I’m not sure why that was the case in 2023. But I\u2019d say that Gartner corrected that omission in spades by focusing the 2024 summit on all things AI.<\/p>\n

And rightfully so, because AI \u2013 whether generative or not \u2013 has been one of the most debated and hyped topics of recent years in the D&A community, along with related topics in machine learning (ML) and large language models (LLMs). Hence, these high-profile topics were the subjects of most 2024 Gartner D&A Summit sessions, from the opening keynote (by Gartner analysts Debra Logan and Ehtisham Zaidi) to sessions on AI trends (Frances Karamouzis) and scaling AI (Arun Chandrasekaran).<\/p>\n

Even the sessions that were not expressly about AI usually touched on how the rise of AI affects them (from the CDAO\u2019s office and miscellaneous Ops methods to data ecosystems and metadata). Similarly, all sessions on data management tools and practices explained how these must contribute to traditional data goals (integration, quality, modeling, semantics, scale) while evolving to also support \u201cAI-ready data.\u201d<\/p>\n

\u00a0Note that session speakers at the Gartner Data and Analytics Summit had a lot to say about so-called\u00a0generative AI<\/em>\u00a0(or simply GenAI), which is the hottest topic in D&A today. However, sessions by Gartner analysts usually addressed\u00a0all<\/em>\u00a0forms of AI, not just GenAI. (See Figure 1.)<\/p>\n

<\/a><\/p>\n

FIGURE 1.\u00a0<\/strong>Definitions of AI, from Joe Antelmi\u2019s presentation\u00a0What Everyone in D&A Needs to know About (Generative) AI: The Foundations<\/em>\u00a0(Source: Gartner Inc.)<\/p>\n

The Gartner view, as I understand it, is that nascent GenAI will eventually complement and coexist with more established analytics practices, especially AI-driven predictive analytics, which already provides technical success and business value in many production systems. Likewise, non-AI analytics (reporting, dashboards, statistical analysis, self-service data prep, etc.) will also continue into the future, as long as they continue to deliver business value.<\/p>\n

Chief Data & Analytics Officer (CDAO)<\/h2>\n

The CDAO is a management role that has recently evolved, as the responsibilities of the chief data officer (CDO) broadened to also encompass analytics. Gartner analysts saw this role coming, and a few years ago they created a Gartner advisory service specifically for it. The research and guidance of that service are represented in a CDAO track and several additional sessions in the main summit track:<\/p>\n