For so long, the foundation of the CEO’s empire has been experience. They have risen to the top, learnt as they went along, and they lean on those who have learnt alongside them. They have had enough information at their disposal to make what they saw as informed decisions, but, even so, many of them run their businesses with their gut rather than from a spreadsheet. The best leaders “feel” their business, they have an instinct for it. Occasionally that instinct is proven incorrect, but they are happy to take those percentages.
The emergence of Big Data is now allowing CEOs to increasingly base decisions on current “reality” rather than past experience, but the risks in the integrity and fullness of the data that they are “seeing” and “hearing” is often a barrier to getting a clear picture of what is actually going on.
In the past, they have had direct access to whatever data they were looking at and could consistently form their own opinions. The complexity of Big Data initiatives is now starting to change that, and far too many business leaders are taking the results for “gospel” rather than understanding how they were arrived at.
As insights make their way up the corporate ladder, from the data scientist to the CEO, the truth in the data can be lost along the way. Assumptions are made, but never fully communicated. The failure of those assumptions is never entirely quantified. What should take three hours to explain properly is limited to a ten-minute slot at a board meeting.
Big Data should not be “dumbed down.” To truly understand the truth in the numbers, the business needs to find a mechanism to communicate the bigger picture. At this moment in time, many CEOs are too trusting of the results, and to some extent that is losing their grip on the fundamentals of where the data came from in the first place.
The key lies in succinctly communicating risk. It is up to the data scientists to make the CEO well aware of the risk in assumptions. Even if that risk is unlikely, at least it is known. People need to accept that risks are normal and become less uncomfortable discussing the negatives with senior executives. Senior executives, on their part, should get more used to discussing the potential downsides.
There is also the problem of the aggregation of optimism.
If seven data scientists all err of the side of optimism in their forecasts, and if they all fail to mention a few of the less-likely risks, then the CEO will receive an entirely distorted overall picture. If all of the middle management sugarcoats their story, by the time it reaches the CEO, the stats will look appealing but look nothing like reality.
Team members at all levels need to take responsibility for holding to the truth in the data and maintaining complete transparency when communicating upwards. Executives have to ask their people to do this due diligence as they pass up the results, and they have to ask the questions, so it becomes a conversation around data, not simply a data download.
If a CEO or business leader questions the “truth” rather than blindly accepting it, they are much more likely to understand it.