A Look Back at the Gartner Data and Analytics Summit
Artificial intelligence (AI) is something that, by its very nature, can be surrounded by a sea of skepticism but also excitement and optimism when it comes to harnessing its power. With the arrival of the latest AI-powered technologies like large language models (LLMs) and generative AI (GenAI), there’s a vast amount of opportunities for innovation, growth, and improved business outcomes right around the corner. All of that technology, though, depends on data to be successful.
With the backdrop of optimism and interest in these technologies, the Cloudera team headed to Orlando last month for the Gartner Data and Analytics Summit, which brought together industry leaders including Chief Data and Analytics officers (CDAOs) and data and analytics (D&A) leaders to share insights and learn how data, analytics, and AI can transform their operations.
Here are a couple of the biggest takeaways we had from our time at the event.
More Businesses Are Taking a Holistic Approach to Data Strategy
One of the more common trends we saw coming up through conversations during the summit was the need for a reframing of how we approach data strategy—taking a much more holistic viewpoint to it than organizations otherwise would have in past years. In those discussions, it was clear that everyone understood the need to treat data estates more cohesively as a whole—that means bringing more attention to security, data governance, and metadata management, the latter of which has become increasingly popular.
In the context of emerging technologies, like GenAI, there is always a healthy dose of skepticism that comes along with its use. What we’ve seen is that more businesses are looking at the potential of those tools through the lens of trust—specifically, do I trust these open models with my data? Do I trust my data to be ready for use in AI? Do I trust the models to give me useful insights? Answering these questions is an important piece of being adequately prepared to leverage GenAI or LLMs. And that’s where we’ve seen this more holistic approach come into play, as more businesses are doing due diligence and making sure all these things—security, governance, and metadata—are clean and ready to support new use cases.
Modernization Is Foundational to Generating Business Value
For all the talk of GenAI, one thing remains clear—many organizations still need, and are looking, to modernize and move workloads that are still running on legacy capacity, into something modern. It’s not just a matter of deciding to move to the cloud wholesale, there’s a desire, and increased willingness, to complement the stack provided by a given cloud service provider (CSP) and supplement it with tools and solutions that are tailored to a business’s specific needs, often across clouds and on-premises. With that, we’ve seen heightened excitement when it comes to third-party solutions—particularly integrating those into existing infrastructure in a way that helps drive the maximum possible business value.
The emphasis on modernization is also driven, in part, by a push among businesses to get data moving and accessible in real time. More and more organizations are realizing that the immediacy of their data and the ability to be proactive as close to a point in time when a change happens is invaluable. That immediate access and real-time capability is what keeps businesses agile and enables them to leverage GenAI and other tools successfully.
Preparing For an AI-powered Future
There’s plenty of optimism and interest surrounding GenAI and AI more broadly. But even in all the excitement, the steps required to make those technologies impactful were at the top of mind for attendees. Among other shifting trends, we saw just how much the approach to data management is shifting, with data strategies moving to account for the data that feeds AI use cases and ultimately makes them trustworthy, and successful.
Learn more about how Cloudera is accelerating enterprise AI.