How Organisations Can Start Thinking About Using Big Data For Creativity

Maybe the biggest inhibitor to creative thinking is the thrash about data and analytics that we have picked up over the years.  Organizations need to embrace the power of “thinking differently,” especially with respect to:    

• 1) Building analytic profiles at the individual (human, machine) level to uncover individual behaviors, tendencies, propensities, interests, passions, associations and affiliations that can lead to specific actionable insights, versus relying on aggregated data to uncover general market trends
• 2) The power of data science to quantify those variables and metrics that are better predictors of performance, versus business intelligence that just reports on what happened while monitoring current business performance
• 3) Data as a strategic asset to be gathered, enriched and shared, versus data as a cost to be minimized
• 4) The potential of predictive (likely possibilities) and prescriptive (what action to take ) questions versus of just mechanically capturing descriptive (what happened) questions

I have worked with very brilliant data scientists.  And most of them have the ability to apply unique data enrichment techniques and  analytical  algorithms in attempt to identify those variables and metrics that are better predictors of performance.
The potential of big data is only limited by the creative thinking of your business stakeholders, and that may be the most important concept in the “thinking like a data scientist” process.  The “thinking like a data scientist” process guides the business stakeholders into envisioning how big data can optimize their key business processes, create a more compelling customer engagement and uncover new monetization opportunities.  But neither the business stakeholders, nor the data scientists, can likely do that envisioning entirely by themselves.

But without the business stakeholders helping them by identifying the right hypotheses to test, the variables that will be better predictors of performance, and the business decisions that they need to make, the data science process can end up being rudderless – without a clear sense of direction and an understanding of what success looks like – a solution looking for a problem.

For me that  impactful, yet biggest challenge, to drive value out of big data doesn’t lie with the technology, but lies with getting the business stakeholders to think like a data scientist.Buying the idea at the end of the day means a lot.