We now live in a 24 hours a day, 7 days a week, 365 days a year world-wide whirlwind of constant information flow, where the very air we breath is literally teeming with digital data streams—continually inundating us with new information.
The challenge is our time is a zero-sum game, meaning for every new information source we choose, others are excluded.
There’s no way to acquire all available information. And even if we somehow could, due to the limitations of human memory, we often don’t remember much of the new information we do acquire. In my blog post Mind the Gap, I wrote about the need to coordinate our acquisition of new information with its timely and practical application.
So I definitely agree with Jarrett that the need to find the right amount of information appropriate for the moment is the needed (and far from easy) solution. Since this is indeed the age of the data deluge and TMI, I fear that data-driven decision making may simply become intuition-driven decisions validated after the fact by selectively choosing the data that supports the decision already made. The human mind is already exceptionally good at doing this—the term for it in psychology is confabulation.
Although, according to Wikipedia, the term can be used to describe neurological or psychological dysfunction, Jonathan Haidt explained in his book The Happiness Hypothesis, confabulation is frequently used by “normal” people as well. For example, after buying my new smart phone, I chose to read only the positive online reviews about it, trying to make myself feel more confident I had made the right decision—and more capable of justifying my decision beyond saying I bought the phone that looked “cool.”
Data Confabulation in Business Intelligence
Data confabulation in business intelligence occurs when intuition-driven business decisions are claimed to be data-driven and justified after the fact using the results of selective post-decision data analysis. This is even worse than when confirmation bias causes intuition-driven business decisions, which are justified using the results of selective pre-decision data analysis that only confirms preconceptions or favored hypotheses, resulting in potentially bad—albeit data-driven—business decisions.
My fear is that the data deluge will actually increase the use of both of these business decision-making “techniques” because they are much easier than, as Jarrett recommended, trying to make sense of the business world by gathering and sorting through as much data as possible, deriving patterns from the chaos and developing clear-cut, data-driven, data-justifiable business decisions.
But the data deluge generally broadcasts more noise than signal, and sometimes trying to get better data to make better decisions simply means getting more data, which often only delays or confuses the decision-making process, or causes analysis paralysis.
Can we somehow listen for decision-making insights among the cacophony of chaotic and constantly increasing data volumes?
I fear that the information overload of the data deluge is going to trigger an intuition override of data-driven decision making.