To create a good business strategy, start by building a solid data foundation. The path begins when a business delivers data to support decisions, so leadership can learn the facts, let go of preconceived notions and use the data to gain real insights.

Once data is employed in building bridges across the troublesome chasms of business operations, decision-makers can start leveraging the power of artificial intelligence or machine learning. Training algorithms and trusting the machine to begin making million-dollar business decisions can be the key to a higher revenue stream, according to Seth Dobrin (pictured), vice president and chief data officer of IBM Analytics.

“The whole role of a chief data officer … is to drive fundamental change in the business,” Dobrin said. “How do you manage that cultural change? How do you build bridges? How do you make people a little uncomfortable but at the same time get them excited about how to leverage things like data, analytics and AI to change how they do business? And the uncomfortableness comes in … getting them to let go of that [preconceived notion] and allow the data to provide some inkling of things they didn’t know were going on.”

Dobrin spoke with Dave Vellante (@dvellante), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the IBM Chief Data Officer Summit in San Francisco. They discussed the role of the CDO, digital transformation, and key IBM customers going through this transformation (see the full interview with transcript here). (* Disclosure below.)

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

Vellante: What have you learned from your internal experiences [on the transformation], and what are you bringing to customers? 

Dobrin: I found that many of our clients needed help and guidance on how to do this. And so I started a team we call the Data Science and AI Elite Team, and really what we do is sit down with clients, share not only our experience, but the methodology that we use internally at IBM.

Vellante: When you think about your [digital transformation] experiences of a completely different industry bringing the expertise to IBM, were there similarities that you were able to draw upon? Or was it a completely different experience? 

Dobrin: This concept of a digital transformation is about moving away from traditional products and services more toward outcome-based services and not selling things, but selling as-a-service. And it’s the same whether it’s IBM … moving away from fully transactional to cloud and subscription-based offerings or it’s a bank reimagining how they interact with their customers — or it’s an oil and gas company, or it’s a company like Monsanto. [The key is] really thinking about how do we provide outcomes.

Vellante: How do you make sure that every as-a-service … can scale so that you can actually make it a business? 

Dobrin: Underneath the as-a-service are a few things. One is data … machine learning and AI. The other is really understanding your customer. And those are all very consistent things, right? They’re all pieces that kind of happen the same way in every company regardless of the industry. And then you get into understanding what the desires of your customers are to do business with you differently.

Vellante: [Trusting the machine to make the decisions for you] has got to be one of the biggest cultural challenges, because you’ve got somebody who’s … running a big business … and [on the other hand] you’re saying, ‘Well, that’s not what the data says.’ And you say, ‘Here’s a future path for success, but it’s going to be disruptive.’

Dobrin: If you look at what the business journals say, [when] you start leveraging data and AI, you get double-digit increases in your productivity … in differentiation from your competitors. That happens inside of businesses too. So the conversation even with the most profitable parts of the business or highly contributing the most revenue is really, what we could do better, right? And then things like moving to the as-a-service from the single points of transaction [model]. That’s a whole different business model, and that leads from once every two or three or five years getting revenue to you to getting revenue every month.

Vellante: [Could you share] some examples of client successes that you’ve had or even not-so-successes that you’ve learned from?

Dobrin: So, ExxonMobil was on stage with me talking about some of the work that we’ve done with them in their upstream business. In the oil and gas industry, you’re talking massive data, tens or hundreds of petabytes of data that constantly changes. So they really want us to help them figure out: “How do we build a data platform on this massive scale that enables us to be able to make decisions more rapidly?” And through leveraging some of our tools, as well as some open-source technology, and teaching them new ways of working, we were able to lay down this foundation. 

So now they want some AI or some machine learning to help guide those geophysicists to help determine where, based on the data, they should be dropping wells. And these are hundred million- and billion-dollar decisions they’re making, so it’s really about how do we help them. 

Vellante: What’s the biggest challenge you’re finding, both at IBM and in your clients, in terms of operationalizing AI?

Dobrin: I think [it is a challenge] as we start getting to really think about integrating AI and data into our operations. And I think DataOps is very similar to DevOps in that things don’t change that rapidly. You build your data pipeline; you build your data assets; you integrate them. They may change on the weeks or months time frame, but they’re not changing on the hours or days time frame.

So this is actually an IBM-wide effort that my team is leading to start thinking about how do we incorporate what we’re doing into people’s CI/CD pipeline so we can enable AIOps or MLOps? And, really, IBM is the only company that’s positioned to do that for so many reasons.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Chief Data Officer Summit. (* Disclosure: TheCUBE is a paid media partner for the IBM Chief Data Officer Summit. Neither IBM, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE