It is often said that “data is the new oil.” The tools we use daily generate lots of it, and it is the fuel behind artificial intelligence, which is already integrated into many applications that we use. With so much data, we have moved from databases, to data warehouses, to data lakes, and Microsoft Corporate Vice President for AI and ISV engagement, Steven Guggenheimer (pictured), jokes that we will soon have “data tidal waves.”

Guggenheimer spoke with John Furrier and Rebecca Knight, co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the recent Informatica World conference in Las Vegas. They discussed the importance of establishing a data estate, the Common Data Model, how doing “BI before AI” is critical for longer-term success, and the skills gap (see the full interview with transcript here). (* Disclosure below.)

[Editor’s note: The following answers have been condensed for clarity.]

Knight: How are companies engaging in AI?

Guggenheimer: The reason that AI is such a hot topic right now is [because of] the combination of … compute storage and networking at scale, which means the access for developers and data scientists to work with large sets of data. If you don’t have data, you can’t build models. If you can’t build models, well that’s what is the definition of AI. So you need data.

Furrier: What’s the sequence of architecture to the usability of data so that not only can you just have analytical systems, but where developers start building their SaaS apps with data?

Guggenheimer: The question becomes in each of the [business] processes there are human-generated forms over data. You’re getting tons and tons of data. The trick now is to make it reusable.

Architecturally, I think about the Common Data Model and the Common Data Service, both generically by industry. For big companies, you have to decide, what do I keep and what do I throw out? What do I just give up on and start from fresh? What do I actually clean? Where do I use tools from Informatica or others to help me clean it, secure it?

Furrier: How does today’s enterprise get the benefit of SaaS, as if they were cloud-native SaaS with the data, and move out of those legacy constraints?  

Guggenheimer: If you get your data in order … then whether you build by or partner up for the SaaS services, you can use that data underneath and you should be feeding it back in and making it such that it is reusable and the pipeline is consistent.

Furrier: How should people approach AI?

Guggenheimer: Get your data estate in order. Second, in a partner model, what’s your differentiation in the company? Where do you want to use either your unique data or your unique skill sets to use AI against that differentiation to help you grow?

Most of the conversations moved from the hype to “OK, let’s get pragmatic,” which is why I always come back to data first. Because if you’re not doing that, you’re not setting up for the long run.

Knight: How do you think about AI for internal Microsoft business purposes? What are the conversations around AI?

Guggenheimer: We do three things throughout AI, where basically there’s a layer growing on top of the core development stack, compute, storage and networking for AI.

We’re building a layer … a set of tools for developers to infuse AI into things that they build. So that’s No. 1. Thing No. 2 is, we infuse AI into our own products, into Windows, into Office, into Azure, into Dynamics. You don’t see it. We don’t talk about it. We don’t say Microsoft Windows LinkedIn brought to you by Azure AI. It just works better. The third thing you think about then is how do you actually use AI to run the business better? How do we think about how we do marketing better? How are we forecasting sales?

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Informatica World 2019. (* Disclosure: TheCUBE is a paid media partner for Informatica World 2019. Neither Informatica LLC, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE