There is a lot of discussion around how data sciences and data analytics differ, from the tools that are used to the methodologies that are employed. Two useful perspectives are to look at the differences (what separates them) and then looking at the commonalities (what brings them together). The “tail of the tapes” (below), provides nine common measures used to differentiate these two “data fighters.” The most notable for this discussion is the first – Philosophy. Data analytics tends to focus it’s mental energy on confirming (quantifying and qualifying) things we know we want to know. On the other hand, data sciences is about revelation – the discovery of something new in a previously unknown area.
Another lens through which we can look at the differences question is that of the Knowledge Model (used above). This model divides our understanding (or not) of the world around us into four groups: you know what we know, you know what you don’t know, you don’t know what you know, and you don’t know what you don’t know. Simple examples of the first two are: you know your age, but don’t know mine. The third is a bit trickier in that this is about recall and recognition. A possible example is recall an event earlier in your life when you smell a particular scent or hear a specific song (ah, those where the days). There are an infinite number of examples in the last category, but one I use a lot is that you probably don’t know much about pebble nuclear reactors and you did not know you didn’t know it until you read those words. On with data sciences. By the way, hardly ever spend time looking into thing we don’t know we know (recall), since a lot of assessments-oriented event highlight them during discovery, which results in more knowing what you know.
The Knowledge Model is very useful when thinking through data analytics and data sciences. Data analytics is fundamentally about providing clarity around those things we know we know. For example, what is my product inventory throughout my global supply chain. Data sciences, on the other hand, explores those things what we don’t know we don’t know, with the goal of producing actionable insights. An example is finding undiscovered ways of limiting product leakage throughout a global supply chain. In the middle, the connective layer, is where data analytics and data science often come together. For example, trying to better understanding why there are different levels of inventory throughout our supply chain or discovering events that will impact them.
While there are differences and commonalities between data analytics and data science, they are both equally important. Without analytics, we would not be able to operate our factories or even pay our employees. Data Analytics powers the economic engine of society. On the hand, without data science we would be suck doing the same thing over and over, our businesses would be incapable of real strategic growth. Data Sciences is a catalyst that move our society through stagnation. Both very different, but both interconnect. A perfect example of Coopetition.