Digital transformation means business change based on innovative data sources and technology. This episode examines how data, predictive analytics, Internet of Things, AI, and machine learning can power digital transformation and new business models.
Our guest is Mike Flannagan, who is Senior Vice President runs Analytics for software giant SAP. Flannagan is also responsible for product management for SAP's Leonardo. Michael Krigsman is an industry analyst and host of CXOTALK.
Dion Hinchcliffe and Michael Krigsman spoke with Flannagan at the SapphireNow conference in Orlando, held in May 2017.
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From the transcript:
(01:11) Officially I’m the Senior Vice President of products for analytics. And now that we have launched SAP Leonardo, I have also taken on the role of Head of Products for SAP Leonardo. And, we had a big announcement this morning by our CEO Bill McDermott.
(01:28) What is SAP Leonardo?
(01:37) It is a digital innovation system, but the idea behind SAP Leonardo is fairly simple. Everybody struggles with business problems, particularly now with the pace of change and the need for transformation of digital business. If you're in retail, problems that you have are not that dissimilar from problems that your peer companies have. And the solutions to those problems from a methodology standpoint and a technology standpoint also have a lot of commonalities. So why does every company have to feel like they’re reinventing the wheel? And Leonardo is intended to help accelerate digital transformation for companies by leveraging the SAP’s experience with other companies to help them solve the same problems using the same methodologies and approaches. Obviously, there’s some customization that’s involved in the company, but you start with a nucleus that is able to accelerate solving the business problem.
(04:46) Give us examples of some of the new data sources that are available to us?
(05:09) … you have a lot of really valuable enterprise data. I think the power of things like industrial IoT is adding to that some data from new sources, and so you think about data from sensors. We’ve got examples of train companies who outfit the brake systems of their trains with sensors so that they can measure break wear. In fact, my car has sensors on the brakes. It doesn’t send me an email, but it gives you a little display on your dashboard. Everybody can sort of relate to that little e example. Now imagine you’re managing like Trenitalia does to thirty thousand locomotives, and you’re trying to minimize the amount of time that you’re out of service for maintenance, both to decrease your downtime costs, but to improve your customer experience by having trains running on the tracks. The ability for them to just add some sensors to monitor a little bit of data about maintenance really gave them the ability to transform the business process around predictive maintenance.
(06:05) Sensors are really one example. Wearables are a new data source. And you know, I think if you consider those types of data sources, you could imagine what the future might hold of all kinds of different wearables, embedded sensors… Video is becoming a really powerful new data source; deep learning starts becoming a more mature technology. So, it's an incredibly interesting time for data people.
(09:25) There are advances in analytical techniques, things like machine learning; you know, lots of industry buzzwords; excitement around machine learning, these days. The power of machine learning is that it really gives you the ability to go back into data that may be two, three, ten, twenty years old, and take all of that history that you have about customers and store operations, and a variety of different things, and turn that into training data, right? To teach the machines what a customer looks like. What does a good customer look like? What does a bad customer look like? What does fraud look like? Those sorts of things require processing the quality of data from which you learn that a human would be incapable of dealing with, right? So it has to be about using the power of machines.
(10:15) And then, obviously, there are examples in manufacturing here you take that learning and turn it into artificial intelligence; things like robots. But, there are also examples in customer service, for example, with chatbots, where now I want to ask a few questions to my bank, and instead of having to have a teller answer the questions, I can just go online and chat and get automated responses that are amazingly accurate for the questions I’m asking.