2018-2019 exchange Winter 2019 Newsletter FINAL - Page 14

Glenn: To me, data science and data analytics are the same. When people think of data analytics, they understand it in its simplest form of just creating a report or dashboard that business leaders can look at it and draw some conclusions. We call that descriptive analytics, or business intelligence. With predictive analytics, you’re not just describing the data and creat- ing some nice charts, but you’re predicting what will happen next. What will people do and how will people react to whatever is there. Things like insurance risk— what’s the likelihood of people dying or having an accident? And then there is the prescriptive analytics side. After we have predicted what might happen, what’s next is what are we going to do about it. Is there something we can do to change people’s behaviors? Then there is pre-emptive analytics, which is how can we prevent something from happening that might otherwise happen, like fraud. People might hear about things like neural networks or deep learning or regression or time series models, which are called supervised modeling methodologies. A supervised model means that you have a target that you are trying to predict and you have past data on that target. Something like marketing response or a fraud you knew was there, or a credit default. You know what you are predicting and you have some data to predict, which is usually historical data and you build a predictive model that addresses that target. Jen: One of the books I’ve read was about “separating the signal from the noise”, which was about predictions and the mentality of “more is more” when it comes to data. At some point, having too much data can be an obstacle to identify the insights you are looking for. Glenn: That’s the essence of the science of statistics. On a high level, it all depends on the signal one is trying to detect. Take a classic example: if you have a coin, is it a fair coin? Does it land heads and tails 50% of the time usually? Or is it way off- 70-30 be- cause it’s oddly shaped. Say you want to distinguish the 50-50 from the 70-30. If you cast that coin 10-15 times, it’s probably good enough to figure that out. But if you were to detect that the coin is 51 14