How Do You Win the Data Science Wars?  You Cheat By Doing The Necessary Pre-work!

If you can keep your head when all about you      Are losing theirs and blaming it on you,  

If you can trust yourself when all men doubt you,     But make allowance for their doubting too;  

If you can wait and not be tired by waiting,     Or being lied about, don’t deal in lies,

Or being hated, don’t give way to hating,     And yet don’t look too good, nor talk too wise:

I’m sure that most data scientists have experienced that moment when they realize that the folks around them have no idea what they do.  That moment when someone walks up to them and says “I’ve got some data.  Can you do some data science on it?”

Many organizations started their data science journey by hiring a “data scientist” and asking him or her to perform magic on the data.  And while there are countless problems with that approach, companies quickly learned that 1) not everyone who calls themselves a data scientist is a data scientist (I can call myself young and dashing, but that don’t make it so) and 2) there is no magic when it comes to data science. Sorry, but as Chris Rock famously said:

There is no sex in the champagne room…

Data Science is very hard work, requiring experience and expertise in gathering (scraping in some cases) data from a wide variety of poorly documented and hard-to-access data sources; dealing with the incompleteness, inaccuracies, vagueness and poor documentation about the data; massaging, twisting and torturing that data into some useful form; and trying a seemingly endless number of analytic transformations, enrichments and algorithms in an attempt to find those combinations of variables and metrics that might yield a better predictor of performance (see Figure 1).

The key to the successful execution of the data science analytics development process – where success is defined as delivering predictive and prescriptive results that materially impact the operations and success of the business – is the pre-work. This means not an hour or so of “showing up and throwing up’, but an orchestrated business stakeholder and subject matter expert engagement process that ensures that the data science team thoroughly and intimately understands the business opportunity under consideration, understands the metrics and KPI’s against which progress and success will be measured, has identified, validated, valued and prioritized the decisions that need to be optimized in support of the business opportunity and understands the costs of the analytics being wrong (the cost of False Positives and False Negatives).  So how do we ensure data science success?  We cheat.