BI today vs. BI tomorrow: Three predictions for a future of actionable insights
The move towards cloud computing and analytics is creating a world in which every possible cut and aggregation of a dataset can be done automatically. The problem is that while analytic engines can scale infinitely, human attention does not.
With hundreds of possible models to apply, and infinite possible insights from each, the process to go from statistical results to clear action is painful, murky, and lacking a common language for relaying insights. In the end, models with even the highest accuracy will run into a wall of objection — “What am I supposed to do with this?”
Today, analysts and business leaders frequently make decisions by squinting at myriad charts and visualizations, eyeballing trends in the search of useful insights. Unfortunately, many of these supposed insights are hard to understand by the business, and most are never acted upon due to non-interpretability, irrelevance to the stakeholder, or simple mistrust.
Top data teams can address this gap by marrying these observations with a clear understanding of how a statistical result can map to a specific action. This means establishing common ground with their stakeholders and using that to drive the conversation.
When data is tied to action, analysts can lead with recommendations for action and then use visualizations as a complementary mechanism to validate the findings. For more teams to unlock this style of action-oriented analytics, three key shifts need to occur:
In many organizations, there’s typically a disconnect between analysts and business operators. Analysts present slide after slide of time trends and bar charts, often leaving operators without a clear understanding of how to respond, or even what responses should be considered.
Let’s use a restaurant franchise as an example, who had developed a weekly email report on key individual store metrics, sent to managers. One report in particular described “a drop in the week-over-week CSAT on the warmth of breakfast pastries in a particular restaurant that exceeded two standard deviations.”
Perhaps this was easy for analysts to understand, but it was gibberish for the restaurant managers. No one translated this fact into a clear recommendation. Exasperated at looking at a wall of numbers and being expected to act from it, a restaurant manager exclaimed: “If you want me to leave the muffins in the oven longer, just tell me!” The analytics team had skipped translating statistical jargon into a clear and concise recommendation in a format that is accessible to the restaurant manager, leaving the manager to draw her own conclusions or ignore the finding altogether.
This frustration made it clear that while an analyst may uncover a critical statistical result, it still has a long way to go before creating value for the business. The key is to map these statistical results to clear recommendations by deeply understanding the levers that operators have to move the business. Then, it becomes easy to translate how data like week-over-week CSAT variations on the warmth of baked goods maps on to an action like leaving baked goods in the oven for longer.
While many statistical results might not have a corresponding action and vice versa, there is tremendous opportunity where there is a regular and repeatable intersection of action and data. Even in prototype forms, these kinds of “intelligent actionboards” that map certain statistical results into clear actions have resulted in wide business gains everywhere they’re implemented. In the study above, McKinsey measures how one Latin American telecommunications company implementing this action mapping resulted in productivity gains of 18 percent.
