What Executives See as Tech Barriers to the 'Data-Driven Enterprise'

Loraine Lawson

Capgemini interviewed 600 C-level executives, senior management and IT leaders worldwide about the challenges of Big Data. Scott Schlesinger, vice president and head of North American Business Information Management at Capgemini, told IT Business Edge’s Loraine Lawson that integration topped the list of concerns.

Lawson: There is this feeling that we’re moving away from businesses being led by someone with experience and sort of a gut-level instinct for making sound decisions, that we’re moving towards data-driven businesses. Do you think that’s what’s reflected here?

Schlesinger: Moving forward, it’s not so much data-driven, because data could be from anything and not really digestible. It’s really taking that data and turning it into information, being able to make forward-looking predictions and model what my business should look like to realize maximum profitability.

I just wrote an article that the premise is you can’t predict the future if you don’t know the past. Some CFOs and CIOs want to make business decisions that will impact them not today so much, but years down the road in a positive way. Well, if I can’t get my arms around my data and the data is not accurate, how can I build any projections? How can I model anything that gives me really any insight to be able to predict the future to help my company?

Lawson: To what extent is integration a concern? What kind of technology issues or data discipline issues will they have to address to be able to take advantage of what Big Data offers?

Schlesinger: There were some disciplines or best practices that were discussed and there were some open-ended questions with respect to that.

Data integration is always a part of it. It wasn’t so much the focus, it was more understanding what types of issues and one of the big things was, and we hear it all the time, is unstructured data is one of the big concerns globally with these 600. The reason being is, if you think about all of these things that we produce and they are not always produced in systems. It could be a PowerPoint presentation, right? It could be a whole bunch of logs from the call center and we can take that unstructured data and track customer sentiment based on key words and algorithms that are written to extract some of that data into a positive, negative, very negative and get some of these things.

And the big thing that everybody’s trying to get their arms around is the unstructured and semi-structured data and integrating that. So that’s where the integration piece comes with what they know from true systems that are structured and then being able to run some advanced analytics against that and say, “OK, here’s what’s happening. Here’s what happened in the past. Here’s what’s happening in the future. Here’s the trends that are going on,” and in knowing this, I can do some predictive analytics to find out what my business is going to look like if we keep going down this path.

So I think the big overlying thing is that integration of all of this data, how the data fits together, how the data tells a picture and then the biggest thing is just solving unstructured data and structured and semi-structured data into an environment. Let’s say we put it into a massive data warehouse, right? We use a technology partner and we start to collect some of this data leveraging some different tools, different technologies. It’s really what do we get or how do we get the data out of there? And that’s where some of the analytics plays come in.

Lawson: Do you think the tools exist to be able to do that now?

Schlesinger: Actually, there are. I don’t want to get into any specific tools, but I have meetings on a regular, consistent basis with a lot of our partners and those partners span from just about every different technology point you could imagine, that we leverage and that we partner with. Each one of them, to some extent, has very robust data warehouse capabilities — very nice tools or sets of tools that will allow you to extract, to translate and load unstructured, semi-structured data into a format that can be digestible and placed into a data warehouse where you can run analytics against it.

So the tools do exist out there. It’s really the teams of people that have the capabilities to weave these tools together to provide some sort of solution set for our customers.

Lawson: Typically, people talk about Big Data and Hadoop together, although there are other ways to deal with Big Data. When CIOs think Big Data do they think Hadoop?

Schlesinger: Hadoop is definitely something that most people have an understanding for, because Hadoop has gotten a lot of press in the past two years or so. So a lot of people are reading about Hadoop.

Generally a lot of people out, some CIOs included, don’t have a firm grasp on what Hadoop is, how to leverage Hadoop and how Hadoop fits within their current environment. I also think we need to think about Big Data, because a lot of people are thinking about other technologies out there, whether they be from SAP, Oracle, MicroStrategy, HP or Teradata, and how they address Big Data. A lot of people have some of those existing technologies in their landscape now and they want to understand, how can they leverage those, maybe in conjunction with Hadoop, but what can they do with what they have now.

People saying, “I’m going to go get Hadoop. It’s going to solve all the world’s problems.” I think we’re educated enough to understand it does have a lot of benefits. Great technology, but until people have a firm understanding of what they can do with it, they're not all running out and grabbing it.

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