OM DETTA AVSNITT
If you’re hoping that adding AI to your analytics product or capabilities is going to unlock new revenue, sales, and greater user adoption, but you’re not sure what’s involved in this transformation, this episode is for you!
Today, I’m talking with Juan Sequeda today, an expert in knowledge graphs and ontologies who most recently was Head of the AI lab at data.world, which was recently acquired by ServiceNow. Juan and I met while speaking at CDOIQ a few years ago, and after being on his former podcast “Catalogs and Cocktails.” (With a name like that, I naturally had him out to my local tiki bar while visiting Cambridge!)
Talk-to-your-data products – effectively next-gen business intelligence applications – are a hot topic right now, and this has made much of Juan’s PhD work in semantics highly relevant right now as companies try to make analytics more user-friendly via natural language.
Juan is clear that the starting point for this transformation isn’t the model or the UI, but actually the customer’s workflow—and that was like music to my ears! Analytics only matters when it drives action, so the real challenge is not answering more questions, but enabling better decisions and outcomes.
A key theme is semantics, which, in product design language, I think of as making users’ mental models of their business or domain map logically to system and data models so that AI produces the right answers in the right context. Juan outlines a practical path to getting started with this: strong data modeling, a well-defined semantic layer, buy-vs-build considerations, and throughout, a constant focus on what the customer’s workflow and problem is.
Highlights/ Skip to:
- Juan Sequeda’s background (2:14)
- Is AI for BI the way to go for proprietary analytics products? (4:30)
- Bolted-on AI versus transformational AI, and what customers are doing with current reporting (8:26)
- Knowing your product’s boundaries and when extending into adjacent customer workflows stops making strategic sense (14:46)
- Setting proper expectations for non-technical founders around what AI can “answer” with analytics (18:43)
- The role of customer problems in informing the prerequisite technology and data decisions (24:37)
- What's the actual lift to add chat-with-your-data capabilities to a SaaS product: data foundation, semantic layer, and the build-vs-buy call (33:38)
- Why Juan thinks every company should become “AI-native” (41:20)
- AI might theoretically make for a better analytics UX, but are users ready to change their behavior or abandon the analytics tools they use now? (46:00)
- How to follow Juan Sequeda (49:03)
