As the leader of Deloitte’s analytics practice, Paul Roma directs the company's analytics offerings across all businesses, so he sees companies struggling with a range of issues. Network World Editor in Chief John Dix recently talked to Roma about everything from what analytics problems companies are facing (Hint: the swamp reference above), to tools that help extract more value (cognitive analytics and machine learning), and even the executive management roles that are evolving (the title doesn’t matter much, but ownership of the problem does).
What do customers typically bring you in to address? Are they looking to solve a specific problem, or are they trying to address bigger picture, overarching analytic issues?
Most routinely we are brought in to work on a specific business outcome. A customer may be looking to, say, improve their consumer Net Promotion Scores (NPS), which is an industry norm for scoring a consumer’s relation to a particular corporation and its products. It’s called Net Promotions because it’s a heuristic that adds together several factors and builds up to a way to judge yourself. Or a healthcare organization may come to us to help improve outcomes in certain healthcare protocols, so we’re usually talking about business outcomes.
When you arrive do you find companies have the analytic tools they need, or are they looking for input on new tech as well?
Larger customers have the analytic tools. There isn’t a company we go into that doesn’t have one of everything. The question is more of focused usage. It isn’t a shortage of data, either, because they’ve got tons of data. It’s normal now to have data warehouses or data lakes that have been put together over the years. But I’ve seen multiple millions of dollars spent on data lakes that become what I call data swamps. They’ve spent all this money to put everything together, yet they can’t do anything with it. The major question now is how to use the data to get better outcomes.
Given there is so much data and so many different tools available to make sense of it, how do you go about helping clients move forward?
I offer up three ways to think about it. First, if you’re grounded in an outcome it leads you to certain questions to interrogate the problem. If I want to improve consumer relations or if I want to improve outcomes in healthcare, you’re at least grounded in what you want to do. As you analyze the data the experience leads you to create certain domains and take what is a very unstructured data lake and start to apply structured boundaries.
Once you’ve done that you can start to use more advanced tools like cognitive analytics to apply structure to the data lakes, and natural language processing and machine learning to give you a way of letting the data give you hypotheses.
The advanced techniques have gone beyond putting out a report and then looking at the graph to see what it says. Now machine learning can actually create causal analysis and tell you what the hypotheses are for which variables, or which data domains are most influential to a particular outcome. In healthcare, for instance, the machine can indicate why readmissions on a given protocol are high. The causal analysis leads to that type of analytic.
The advanced techniques are probably where we’re called in the most to try to make sense of all the data. Without advanced techniques there is no way to cut through it. You have no knife to cut through the data. Just running reports will create endless reams of paper that, frankly, you could never get anyone to interpret.
We bring custom-tuned algorithms to a lot of our engagements -- whether it’s in healthcare, supply chain or customer marketing -- and with machine learning algorithms and supervised learning cycles we can run against their data and create hypotheses you can investigate with your experience.
Interesting.
