It’s the results, genius!
It’s the results, genius!
It’s the results. The purpose of any sort of data analytics or data science is to get results.
It isn’t about the spreadsheet that comes three weeks after the campaign. It isn’t about sandbagging numbers. It isn’t the few slides in the Quarterly Business Review. It isn’t even data entertainment.
It’s the results.
Great! So what’s the deal?
Why is so much time expended on activities that don’t directly tie to getting results?
Analytics Maturity
It’s because of maturity, or the sum of experiences that an organization/culture chooses to remember.
Very good models of analytics maturity exist. Stephane Hamel has a great one.
Stances inform tools and tools cause experiences.
Where you stand affects which, if any, tools you’re going to use. If the stance is that you just need a few numbers to hold a department accountable, then that’s going cause a distinct investment in certain tools, like Excel. And that’s going to cause a fairly predictable kind of experience path.
The stance of many leaders is that data analytics can contribute to better results.
But is it inevitable that organizations have to go through earlier stages of analytics maturity, or can they make huge leaps forward, can they leap ahead in one go?
In my view alone, the vital variable is the stance of a given market segment.
If the customer is demanding high maturity, they’ll get high maturity.
If they demand lower maturity, they’ll get lower maturity.
(And what passes for a market also exists within corporate institutions and nonprofits too.)
Demand spreadsheets and that’s what you’ll get. You’ll be flooded with spreadsheets to the point where you’ll complain, ‘these reports aren’t telling me anything!’.
Demand something vague, like shouting ‘insight, insight, insight’, and you’ll get back something equally vague.
Maturity and Results Orientation
The height of maturity is the direct translation of data into code that delivers better results.
If you understand the relationship between prospective customers, customers, and next best offer, then the deliverable ought to be personalized next best offer.
If the objective of data anaytics and data science is results, then that ought to be the deliverable.
How big is that market?