Video

Data Veracity

In the Accenture Technology Vision 2018 for Pega: Data Veracity, Don Schuerman, CTO, Pega, and David Steuer, Managing Director, Accenture, discuss the importance of trusted data from an artificial intelligence perspective, and how Pega is helping organizations obtain the right data.


Transcript:

David Steuer: Don, thanks for joining me today.

Don Schuerman: Of course.

David Steuer: We're going to talk a little bit about the Accenture Tech Vision and five themes that we have within the Tech Vision.

Don Schuerman: I'm excited to talk about this. In PEGA, we spend a lot of time thinking about how technology can impact both how our clients and our joint clients engage their customers and how they can drive better efficiency in the operations. So let's dig in.

David Steuer: Fantastic, fantastic. So let's go to the third theme. And the third theme is really all about data veracity and the importance of trust. Data is so important from an AI perspective, right?

Don Schuerman: Yes.

David Steuer: I mean really all artificial intelligence depends on having the right data and a trusted source of having that data. Maybe talk a little bit about what role would PEGA play in providing data veracity?

Don Schuerman: I think there are a couple of pieces. PEGA is, for many organizations, both a protector and a governor of the data, as well as a consumer of the data sort of from the AI space. And if you think there's been lots of projects, organizations, do lots of big heavy lifting around master data management projects and data consolidation. There's also more lightweight things that we've helped organizations do around overall data governance. It involves looking at your data through the perspective of a processor of the case, that needs to be done to manage that data. Great example of, actually what we're seeing this pop up right now, is in the area of GDPR, right? As more organizations need to be able to push back out to their customers information about the data, or perform global data cleansing, they're going to fill a lot of that initially through a lot of manual stuff. But at the end of the day, that actually needs to be a process that's governed. So we're seeing organizations use PEGA both in the case of GDPR, but also in the case of other areas of data cleaning. Use case management to sit on top of their data systems ...

David Steuer: Right.

Don Schuerman: Virtualize pulling that data together, and then ensuring that once you get the data right, it stays right, so that every time changes get made, I'm consistently updating back into the various systems that need it.

David Steuer: Makes sense. So using case management to really do that data management. Now when you talk about PEGA AI and AI enabled customer decision hub, and you've used the example of Google before, and Google's very dependent upon having lots and lots of faces, all of our faces, all of our pictures are loaded up in there, so they do a fantastic job with that. How dependent is PEGA on the data for the customer decision hubs?

Don Schuerman: We are data dependent, and we're using technologies, again like Baysesian machine learning, predictive analytics, that are fed and grow and learn through data. However, I think that there's been, at the enterprise level, a lot of excitement about big data, massive data scale, this sort of internet sized scale. And it's left a lot of what I will call little data unmined for value, right? So we are dependent on data, we pull data in, and we can do things to help organizations normalize and get the right data. But you can also do a lot in the AI driven decisioning space without massive Google sized data sets. You know we did some work together at a telecommunications company that dramatically changed how they retain a market to their clients, and I believe that first phase of that project started with about 30 to 40 pieces of data about each individual customer, right? And having those 30 to 40 right and fed into an engine that can learn from them allowed them to have triple and dramatically improve their ability to make the right offers to the clients. So it wasn't about getting this Google sized data set. It was about getting the right data set, and fed into the right kind of engine to learn from it.

David Steuer: And getting a data set that's actually very dependent upon the customer or client themselves, versus having to go outside of that ecosystem.

Don Schuerman: So many of these enterprises have as valuable and exciting as sort of the data economy is, and some of these data platforms will be for certain things, as that enterprise, if you're a bank, if you're a telecommunications company, you've got data about your client that is incredibly powerful, unique to you, that allows you to make a differentiated decision, not the same kind of capability that anybody can make from using Google's data. And I think at that at the enterprise level, that data is still relatively unmined. There's lots of value to be pulled from.

David Steuer: Yeah, tremendous opportunity.


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