The “4th Industrial Revolution” is upon businesses, and for each of them the difference between winning and losing will increasingly depend on the quality and speed of insights available to it. And with the current increase in business uncertainty such insights can be used to turn a threat into an opportunity.
But making the transition to a data-driven business requires a significant change in culture. “Gut feel”, “Groupthink” and “We’ve always done it this way” go out – replaced by business decisions underpinned with evidence.
Without board level support such initiatives always fail, so despite the fact that most business leaders did not grow up with data analytics (even if their staff did), the Digital Board also becomes a crucial driver.
>See also: The Fourth Industrial Revolution: Technology alliances lead the charge
The 15x return on investment and 26% average productivity improvements that analytics have delivered now make it less a question of “if” and more of “when and how”. Many people worry that their data is imperfect. And of course it is never complete, but this is no reason not to start.
Triangulating data – filling in gaps, and validating via multiple views – is just one example of the skills that everyone will need to learn. This is called “data articulacy”.
But while better insight and dashboards are a key part of transformation, there is much more. Think of insight more as “the fuel that drives action” – it’s then only a small step to selecting the action most likely to produce the desired outcome.
Amazon’s recommendation engines, for example, generate 35% to 60% revenue uplift. Responding to events might include: better targeted and more timely actions, anticipating or even predicting future events, and identifying optimised responses that can be triggered automatically for each scenario.
>See also: Turning big data into high-class insights
Carrying analytics “beyond insight” into action in this way has two key benefits. The first is that it closes the circle of continuous improvement, moving business managers outside the process to instead use their expertise to refine the optimal responses.
The second is that it creates a more direct cause-and-effect relationship that leads to a more objective return-on-investment case based on outcomes.
The move from the traditional “Rear-view Mirror” to – as it were – a “sat-nav for business transformation”, enables continuously updating the route to reflect changing conditions.
BigData4Analytics describes the ultimate goal as the “Self-Aware Enterprise” reflecting its ability to respond autonomously to events – in a manner similar to the self-driving car.
So, apart from ensuring that everyone in the business is involved in the transition, the key message is to make sure that the goals are clear: whether they are shortening the time for the business to respond to events, using your data to produce more actions and less insight, using “marginal gains” to drive up performance, post-merger integration of data assets, or generating new revenues from monetising your data.
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