Dr. Athanasios (Thanos) Gentimis, Assistant Professor of Math and Analytics at Florida Polytechnic University sees the future of analytics as a team effort, where subject matter experts (SMEs) collaborate in teams with Data Scientists and each team member plays to his or her strengths. He also sees a reliance on Deep Learning for creation of relevant questions and answers about data.
Dr. Gentimis’ field of expertise is Algebraic Topology, with a special interest in Geometric Group Theory. In a recent DATAVERSITY® interview, Dr. Gentimis discussed how analytics is changing and what the future might look like.
“If you go back into the 80s, analytics were performed on small data sets where somebody would come up with a survey,” says Dr. Gentimis. “You would ask about 1000 people, and then try to extrapolate to give you some information about the society at large.”
It’s a process that involved starting with small samples and attempting to extrapolate for the whole population. This process required more mathematical intuition than computing power, and the tools used were created for that specific purpose. Simple questions like “Who is the average Amazon customer?” have no meaning with the sheer volume and variety of data available now. It’s no longer possible to take a few data points and draw significant conclusions, he says.
The presentation methods need to be more sophisticated now that the breadth of data is also more sophisticated, so using simple graphs or PowerPoint presentations, he says:
“That’s not going to cut it anymore. The complexity of those data sets, as you start doing things in more than one dimension, you have to think of products now that have four or five or six attributes, so that doesn’t fit anymore into a nice presentation.”
While simple presentation methods are no longer able to adequately show the story within the data, business executives still need that story to use data effectively for predictive and prescriptive purposes. A 2015 DATAVERISTY research report by Kelle O’Neal and Charles Roe entitled Business Intelligence versus Data Science, found that nearly half of respondents cited “improved understanding of customers” as the primary reason for investing in Data Science:
“Better decision-making and improved understanding of business results ranked as the most important measurements of Predictive Analytics/Data Science program effectiveness. Increased operational efficiency, better impact analyses, cost reductions, and more accurate forecasting were some of the top outputs that senior executives want to see from analytics .”
Making Meaning from Data: Telling the Story How is it possible for the Data Analyst to share complex data with end users in a way that has meaning? “You have to collaborate with the SMEs,” Dr. Gentimis says, and that may require some added communication and social skills. He sees positions like Data Analyst now requiring collaboration or team building skills, so that a group that collectively has a more varied skill set “can take the knowledge of the market and see how it is applied.”
Dr. Gentimis confesses that, as a mathematician, “I can tell you what the data says, but I cannot explain what it means, so I really need to partner up with a SME and create the story.
