WATCH: The three pillars of your enterprise AI infrastructure

This session discusses truths about your structured data, unstructured data, system integration and building capabilities of your people to ensure that AI doesn't become another issue not beholden to only a few people. Enterprise AI needs to be at scale, operationalized, with clear responsibilities.

Your structured data

We’ve reached a moment where most global corporate enterprise practitioners at least know of the structured data they have and at least have an idea of how they would be going about intelligent automation around that structured data.

Your unstructured data

Corporate enterprise practitioners are now truly realizing solutions that can truly uncover unstructured data. But just applying technology doesn’t cut it. It comes down to knowing what you want from an outcome perspective.

Your systems integration

The technologies used to manipulate your data have been rapidly deployed and developed. These tools and technologies are now siloed in the enterprise. So how do you bring your business-led automation together- keep the systems integrated- make sure the data is flowing through the systems, and at the same time as the models that you're working on, you're using the right data informed by the entire enterprise? That's the key challenge here.

6 select key takeaways:

• Structured data is where the 80/20 rule fits in. Even though this might be 20% of all the data you need, it's where you're currently spending most of your time. 
• Make sense of your data and integrate with your systems so you can discover how best to interpret, visualize, report and analyze the insights you want to gain.
• Put some mechanisms, algorithms, and tools around your unstructured data to make sense of it. Extract insights that tell you not only about the past and the present, but the future.
• You can put the most fancy algorithms, technologies, models and tools that you can throw at your data. But, the question is what do you want to know from it?
• To uncover the right data it's important to know where the data is coming from, and know the sources of data might not be sources that you want to play with moving forward.
• AI systems integration is like any other transformation and change management you’ve done. Put a vision out, put a tracker on the vision, insert an application landscape layer, place your target operating model, and chart the path of where you want to go.