Frank Smietana: What are the most significant data utilization challenges facing firms these days?

Jeremy Hurwitz: So, I think the one thing we see as being the monster is the size and the scale and the dimensions of data. As we’ve evolved from a more traditional field attribute level data world, where we worried about getting in security master data, client data, portfolio data, we’re moving now to a massive scale data world where the data can be spatial, it can be digital, it can be media. We have this whole deluge of data that’s coming into our landscape, and we don’t have the capability to either store it, process it, analyze it, and make sense of it. And so one of the biggest challenges we have is changing the data utilization patterns of what we’ve done traditionally, which is handle file data, sort of record-based data to moving to streaming spatial digital dynamic data. So we have to retool our whole data infrastructure in order to be able to handle that. We’ve got to retool the way we store, manage, organize, and then utilize that to allow us to interpolate, predict, use AI, use smart, intelligent processing models to be able to make sense of the data. So that’s number one. 

Number two, we’re going to have to build what I call friction-less systems. We have to allow data to move without causing a tremendous amount of overhead and delay in terms of the way the data processing cycles work. So all of that has to transition, how we store the data, how we move it to the cloud, how we open up the sort of access and the connectivity to the data, and then the tools that we have to allow the data to move around. So cloud to cloud-to-cloud integration, cloud-to-terrestrial integration, old legacy, access to the cloud, all those things have to happen. 

And then we need the ability to have tools to allow us to make sense of the data. So the next generation of AI, BI predictability, sentiment, reading type data tools is going to allow us to really be able to utilize this data in a more efficient way. We also have to worry about the digital control, security, and access to that data. So while we’re opening up and allowing data to free flow and use different formats and different technology to interpolate the data, we have to make sure we keep it secure. So we have to fortify our data environments, and we have to follow regulations. So we have these conflicting demands. One is we want open, we want full access, we want full interpretation and use of the data. On the other hand, we have to make sure the data is secure, protected, and maintained historically to meet all of the regulation requirements that we have around the utility of data. 

And then the third thing really is this idea about reimagining how the next generation of consumers wants to be able to digest their data. So we have, I consider the changing of the guard, the Gen X, Gen Y, and Gen Z are the new kids on the block. They’re going to be thinking about data differently. They want to be fed their data through different technology capabilities, through different interface methods. And so we have to reimagine what that experience is like for the next generation of people. 

In summary, we have to bring the data in, open it up, and be able to use the tools and technology to interpret all the data components in there. We have to secure it and then we have to make it accessible to the next generation of technology consumers.