For years, “actionable insights” have been the Holy Grail for data analytics companies. Actionable insights, the thinking goes, are the end product of data collection, aggregation, analysis, and judgment. They enable a decision-maker to modify behavior and achieve desired outcomes.
The process begins with data collection, which can take many forms. There’s a big difference between collecting data and aggregating it in a meaningful way that can provide a picture of reality. That’s the “insights” part of the puzzle. First, you need high-quality data, then you need the technological prowess to clean and organize it.
With high-quality data that’s been cleaned and organized, the next step is to provide context. This is the realm of companies like Tableau, which provide tools that translate machine-friendly data points into human-friendly visualizations that strive to depict an objective picture of current conditions.
But whereas a snapshot of current conditions may, in fact, yield new and meaningful insights (for example, if I look ‘sales numbers’ across an organization I can see which channels are over- or under-performing), human judgment has always been paramount in choosing a particular action. A perfect picture of static conditions doesn’t by itself offer any suggestions as to how to achieve particular outcomes. We still rely on management to tweak sales incentives or redistribute resources.
Or at least we did, up until recently. Machine learning is now shifting the balance of institutional decision-making. Advances in processing and algorithmic self-improvement mean that computers can now anticipate future outcomes and take steps to maximize particular ones. Intelligent systems can now see the world in shades of gray and evaluate likelihoods from multitudes of variables far beyond human comprehension.
That’s the world we currently live in, and the evidence is all around us. Machine learning algorithms have swayed elections by stoking targeted outrage. Our clothes, food, and consumer products are designed according to data-driven analytics. Every design feature in your favorite app is being constantly optimized according to how computers anticipate your future behavior. It’s why YouTube is actually pretty good at showing you videos that keep you engaged.
The day is coming when we will no longer require “actionable insights,” because the action will have already been taken. Nobody at YouTube is looking at your viewing history to determine what to recommend next. Computers do that. The value of the stock market is now largely driven by automated trading algorithms, and as a consequence, there are fewer stock analysts than there used to be. Not only can computers process information far better than humans, but they’ve also demonstrated better financial judgment.
The day will soon arrive when “actionable insights” will seem like a quaint notion from a simpler time. Computers will be smart enough to act on insights by themselves. In doing so, they may, in fact, diminish the need for human oversight.
Until then, however, human enterprise is still structured around hierarchies of decision-making and judgment. The CEO of a company still needs to delegate day-to-day responsibilities to human actors whose knowledge and judgment have proven sound.
And so, for now, we still need actionable insights. Data analytics companies will continue to build better mousetraps, until the day when there are no longer mice.
Gil Rachlin, SVP of Products and Partnerships at Synup.
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