Last week’s Gartner Data and Analytics Summit in Orlando, Florida had the feel of a market in rapid transition. Given the tech world we’ve experienced the past few years that wasn’t a surprise.  

However, on the expo floor it was very interesting to see the “villages” layout hubs for various disciplines (e.g., analytics, AI, data management). I fully understand why the organizers went with this format. From an audience perspective, historically attendees are mostly experienced professionals who know what they need and are looking for discrete solutions.  

However, my impression after walking around the event and talking with a wide range and volume of booth visitors is one of worlds colliding. 

• In conversation after conversation at our booth we heard about teams struggling to get a handle on their data. They need help across the board – better managing data for analytics, preparing data for AI, getting compliance and governance in place. Scaling data access safely and securely. Their needs touch the entire landscape and stack. 

• Discrete solutions aren’t what many buyers really want. They don’t have the time, budget, or energy for stack building or heavy integration. There is increased buyer pressure to find cost-effective solutions that work well together and can scale. 

• AI has raised the bar on data management. All the gaps that have existed for years in policy management, governance, basic data fundamentals are even more important now that the AI horse is out of the barn, with agents coming fast and furious. 

• Quality, quality, quality. How to baseline and understand it, how to ensure it, and to maintain it in a world soon to be dominated by AI. If you can’t help or provide an answer to quality, you’re behind.  

• With quality top of mind, data lineage is becoming even more critical as a foundation for data and model observability. (We have some great things coming soon on this front, stay tuned!) 

• And even in a shifting regulatory environment, data governance is crucial to avoiding abuse and misuse of data in AI models and agents ahead of full production. Most organizations we talked to were concerned about the potential impact of under-governed data and are looking to shore up their stance.  

I also had the pleasure of connecting with Gartner analysts Cuneyd Kaya and Roxane Edjlali. It was really interesting to get their perspectives on a wide range of topics, including why a hybrid solution for model development in an increasingly open source model world makes sense, the value of helping organizations simplify their management of data complexity, and how data will be the key differentiator for any AI success as most algorithms become available off the shelf.  

We see the same needs and trends happening within our customer base. Becoming more data-fit for AI, reinforcing a data foundation for both core operations and AI with strong governance, and cost effectively simplifying data chaos are what we are helping organizations achieve through the Pentaho+ Platform.