## DO YOU DO ANALYTICS?

This is one question that I have been faced with repeatedly while making sales pitches. What is analytics anyway? While everyone has their perspective of what analytics actually is, this is how I see things – Problem statements fall into two fundamental categories (along the Y-axis as shown in the figure above):

• Problems where the physical relations between the data-sets to be analysed are known – the relations can be captured by some mathematical formula, by rules, logic or structures.
• Problems where the physical relations are unknown or too complex to be modeled.
##### Q1: Known Problem Statement + Known Physical Relations between datasets

Suppose a refiner is analyzing its working capital and wants to know how much of that is tied in crude oil inventory, intermediates, and in finished product inventory. He needs the analysis sliced and diced by refinery, by region, by material group, etc. The analysis also needs to show how much of the inventories are stuck in dead stock, the opportunity cost of it and historical trend of that for last 5 years.

This is an analysis where all the data relations are known. One can perform the analysis combining some logic (SQL), mathematical formulae and structures (hierarchy). In a scenario such as this one, the business question or problem statement is known along with the model required to arrive at an answer. This is what I call “Known-Known”modelling – we know both the business question and the physical relations of the data.

##### Q4: Known Problem Statement + Unknown Physical Relations between data-sets

There are however, areas, where we do not know the physical relations between the data or they are too complex to be captured in any hard-coded physical relations. Supposing if the same refiner now wants to know well in advance, when a particular critical equipment might fail (e.g. the feed pump to the crude distillation unit), it is difficult to capture it in any physical model. Needless to say, there are always sensor based alarms and alerts that warn against above-limit vibrations, winding temperature, etc. There are also sophisticated models to estimate the thermal, mechanical and electrical stresses for large rotating machines such as this one (feed pump).

However, today, we have commercially viable techniques available to complement these traditional techniques and extend our foresight far beyond of what was hitherto possible. These techniques do not insist on a given relation between data (such as bearing vibrations, winding temperatures, differential pressure, lubrication oil test results, both preventive and breakdown maintenance work order history, etc. as in this example). Rather, these techniques discover the probabilistic relation between an event (e.g. potential failure) and some particular constellations or sequence of constellations of the data sets. These techniques are commonly known as Machine learning, Artificial Intelligence, Data Mining or Statistical Data Mining, Neural Network etc. There could be fine differences between these techniques, however, for broad understanding, these can be clubbed together for what I call “Unknown-known” type analytics (i.e. business issues are known but specific data relations are not).

We have just covered the right hand side of our problem space, i.e. when business issues are known. But what about when business is not aware of the issue or just did not raise it?