Big data is now economics and not just technology
Hitchi Ventara CTO Bill Schmerzo tells TechRepublic’s Tonya Hall all about how cleaning the right data using machine learning can yield a positive ROI in your AI. The following is an edited transcript of the interview.
Tonya Hall: Are Big Data and Analytics all about the technology, or is there something bigger? Welcome, Bill.
Bill Schmarzo: Thanks for having me.
Hall: What does Hitachi Vantara do?
Schmarzo: Think of us as the digital arm of Hitachi Limited. That is, we are providing data, analytics, and applications around IoT, not only for Hitachi itself, but also for all of our customers.
Hall: You’ve written two books. One is Big Dataand Big Data MBA. In addition to Big Data, tell us what those books are about.
Schmarzo: The books I wrote, especially the second one, the Big Data MBA, is actually the textbook I use for the class I teach at University of San Francisco, which is called the “Big Data MBA.” The premise behind that class is that, tomorrow’s business leaders will need to embrace analytics as a business discipline, that the days where we could turn over analytics to somebody else in the organization, they’re over. That leading organizations, especially leading organizations in the area of digital transformation, are the ones who are embracing analytics as a way to differentiate their business and to fundamentally change their business models.
Hall: How does a company become more digital?
Schmarzo: Great question. It doesn’t start with technology. It starts with companies really understanding, amongst their customers in the market: What are the sources of value creation? Where is value created in the marketplace? And then, how do I leverage digital assets, particular data, analytics, and intelligent applications, to basically capture and monetize those high value situations?It really starts with a very intimate knowledge of your customers, what it is they’re trying to accomplish, where the points of value. And then, a very thorough understanding of your own internal value capture processes, so you understand how I’m going to capture those points of customer value creation. Sounds kind of complicated, doesn’t it?
Hall: Well, that’s the question, then, I guess: How do you decide what data you should collect? I mean, how do you assign value to the data?
Schmarzo: Great question. We actually did a research project at USF on determining economic value of data. What we’ve found is that, the key linkage point for identifying the points of value creation are the decisions your customers are trying to make in their journey map. If you think about somebody who’s trying to buy insurance, for example, there’s an epiphany moment where the customer realizes, “I need to have insurance.” As in, if I was an insurance provider, I would want to make sure that I’m there at that epiphany moment, to help set the agenda, and then the customer’s going to go through a variety of different phases and decisions and processes to understand, “What insurance should I buy? What kind I need? What price am I going to pay? What am I going to cover?” The whole litany of insurance, not only when you buy it, but through your entire life of that policy up until the point in time where you expire, either you or the policy expires.
And so, organizations need to understand the journey that taking place. Where are the points of value creation? Where are the inhibitors of value capture? Then, they can build the kind of internal applications and processes to help capture the points of value and mitigate those points, those inhibitors.
Hall: When digital transformation goes wrong or is unsuccessful, what is often the cause?
Schmarzo: It’s really that organizations don’t understand what customers are trying to do. They haven’t taken a time to really focus in on the decisions that customers are trying to make.
