The optimism of #Purpose & science of #design in forging #digital customer experiences! Opinions my own!

Analytics is almost a pre-requisite for ten 21st century business. In the 80s, computer literacy was a novelty, in the 90s it’s a must have. Data science, like any other field of study, will mature with a critical mass of practitioners, thinkers and adopters.
There’s a slight mismatch in the demand and supply curve; businesses and brands know Analytics is fast becoming a must, but finding the skills and know how is still akin to digging for truffles in the ground: it’s a scarce resource.

The Lab, Factory and ConFab
Concept flips the problem around and presents us a little conundrum. Perhaps, we don’t need to go on an Analytics arm race after all, and tapping on talent and innovation can honed and modeled as a process.
There are merits to this; harnessing innovation successfully is how Analytical competitors cement their competitive advantage. Hoarding Analytical talent is not only expensive, it doesn’t make sense!
Counter intuitive, but my argument goes like this: Analytics, at least the advanced variants, are by nature, experimental and exploratory. Analytics Labs like the ones in CapitalOne, Verizon and AMEX test tens of thousands of possible hypotheses per month, refining and assimilating insights in a virtuous cycle. Failing, and failing quickly viz a viz continuous learning, is an intricately managed process to be encouraged. Data savant CEOs like Gary Loveman of Harrah’s views the lack of data driven insights as grounds for firing an employee.

Next, talent. It’s silly to hoard data science talents. It’s expensive and the rate new ideas are germinating, there’s no way to not be staid. Why not crowdsource and tap on an Innovation Network?
Learn and adapt new thinking from other practitioners, and contribute reciprocally.
Easier said than done!
Governance, guardrails, processes, benchmarks, assets, methodologies and a new (counter intuitive) way of thinking!
How would you get the old farts (baby boomer CEOs) to not listen to their guts?

Trial and error, and continuous reaffirmation of value. Small PoCs, small Lab engagements, each countersigned by a Business stakeholder – that’s how.
The Lab pushes and pulls at the same time with cadence around governance.

The Head of the Lab must have a direct cable to the CEO.
He or she shall be named the Chief Data Science Officer (not Chief Analytics Officer, that’s too myopic).