The paths toward our desired business outcomes are paved with hundreds, thousands of decisions.
It remains true that data analysis or decision support improves business most by improving every transaction and every decision just a little.
Despite the real problem of decision fatigue and the trend toward algorithmic automation of many of our business processes (think: programmatic advertising, machine learning in video streaming, and more, which all require embedded decision-making criteria anyway), most aspects of business will continue to require a combination of human intuition and data as we progress through every decision from beginning to end. Every decision, no matter how small, can be supported by the incremental (and occasionally downright huge) improvements in daily business that analytics brings to the table.
Chip and Dan Heath, out of Stanford and Duke, have given us an incredibly useful way to describing how to march through the life cycle of a decision, which they’ve condensed into what they call the WRAP process. At Analytics Guild, we’ve applied the WRAP framework to analytics in order to clarify what goes into a great process of decision-making:
Widen your perspective:
Analytics allows you to look at data (evidence) like a rubics cube—turning from one statistic to the next, drilling in and out of different data grains—in order to discover what you didn’t know before. Eventually, the puzzle (a problem or opportunity) at hand is solved, supported, or refuted by comparing your perspectives prior to data with the different scenarios you see unfolding in a dashboard.
Reality-test your assumptions:
Data is an abstraction of real events in the world: a customer purchases groceries, an excited fan leaves an ecstatic comment, a car passes through an automated toll. All of these events are then aggregated and summarized into statistics and visuals to help us go beyond the “gut check.” By relating our assumptions or intuitions with data summaries, we’re able to create a powerful container for excellent decision-making.
Attain distance before deciding:
The very act of checking a dashboard, scorecard, or report before you decide on something means that you’re putting distance between you and the decision. In business we also tend to make decisions within a community, so others will check work, hear pros and cons, and fill the gaps between your intentions towards a goal (i.e. decision/action) and the total array of options at play.
Prepare to be wrong:
Analytics allows us to see alternative sides to any story, as well as the myriad of answers at hand. Summary statistics aside, what we should do in business is always a probability game; no one can see the future perfectly. Mitigating decisions with simulations, performing risk assessments, and fully understanding what could happen when you choose a certain path means your decisions are always contextualized, and you’ve always got a good decision in sight.
When we follow the WRAP process leveraging analytics, we’re setting ourselves up to achieve our desired business outcomes. By itself, data won’t give us superpowers that allow us to bypass the way we make good decisions; it’s when we use data to amplify solid decision-making processes that the (somewhat unsexy) superpowers of data emerge. Day to day, it’s the more modest qualities of leveraging analytics that are truly the most powerful. A 100,000-person company changes one decision per employee, per year, because of analytics? 100,000 good decisions sound good to me.
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