In data, admitting ‘I don’t know’ is a powerful thing. This admission opens all the possible doors in our search for an answer. Too often, we clutter and fog our approach and reduce the realm of possibilities with elements that come way too early in the process.
First comes experience—yes it’s an asset in funneling the answers efficiently but an overdose of it can lead to repeated patterns in analysis that may not fit the current need. Even if it is for an instant, we have to put that experience aside to open our spectrum. Second is ease or laziness. We all have our favorite data sources; the data sets we own or know by heart. Isn’t it convenient to spin great pivots and beautiful charts at speed of thought to explain, if not show off, data analytics even if the data is not the relevant one?
We are not hired to have it easy. Analysts are paid to provide the “right answer.” An easy or well-known metric is not synonymous with finding the right answer.
As I train and engage professionals every week in my classes, sessions and conferences, I see a new term rising up in data analytics: holistic. Based from the Greek word holos, which means ‘whole’, the theory of holism refers regarding nature as consisting of wholes. In data analytics, holistic refers to the theory that teams must expand the realm of investigation to ensure that no critical information is left behind.
We must ask ourselves, how can we be holistic if we are blinded by our own habits? How can we be holistic if we succumb to the instantiations? The best first step is to start with I don’t know and follow it with let’s find out.
By not dismissing any options, hypothesis or, as a consequence, any data, we are able to capture what was so far undetected or invisible. With the help of statistics and analytics, this ‘I don’t know’ starting point can be supplemented with the detection of patterns, clusters and correlations that can make our life easier by identifying the drivers of the problems we are trying to solve.
Last but not least, starting with I don’t know prepares us for the big data analytics leap of faith. It prepares us to go beyond the known-knowns, and embrace not only the unknown-knowns, but the unknown-unknowns.
By being upfront and honest with ourselves, as well as showing humility and grit to answer it puts us in a good situation to welcome all the things we hadn’t thought about. Instead of being the fool that never saw or ignored that incoming train, we become the pragmatic data-driven manager that knows that well processed data never lies and should always be considered.