in the decision-making processes, and more accountability for the results. It won’t surprise you to hear that this new phase is also labeled decision intelligence and that it’s measured by the value of its outputs, whether that’s in terms of impact or return on investment or both.
There is no more time, patience, or money for fishing in data lakes or panning for gold in data streams in the hope of discovering valuable knowledge. Decision intelligence insists on moving with purpose to achieve a predetermined end whose significance has been well defined.
Here’s a handy example: You may ask the data what the weather will be like tomorrow. But that isn’t the question. Nor will the answer “Partly cloudy with a high of 70 degrees” be of any significant use to you.
Think hard. What is it that you really want to know?
Perhaps it’s whether to plan a picnic tomorrow. In that case, you likely need an assessment of the weather, plus pollen counts, projected traffic at the park, and maybe even water sports availabilities and/or wait times for picking up prepacked picnic lunches at your favorite deli.
Perhaps you wanted the analytics to tell you that your best pick for a picnic tomorrow is “Happy Park on the north beachside with shelter from the wind but not the warmth of the sun, and plenty of tables, because it’s not a high traffic park. Also, your route has three delis, and two have less than a 10-minute wait for order pickups.”
Decision intelligence can be applied for a relatively-speaking best decision for a problem or question of any size, ranging from the highly personalized (like the picnic questions) to the truly huge (like a global pandemic).
I talk earlier in this chapter about how the COVID-19 epidemic revealed the limits of a data driven approach to problem solving. Some of the lapses in the initial response to the epidemic were certainly caused by the urgency of the threat and the novelty of both the virus and the vaccines. Yet several factors worked in favor of making sound public health decisions under pressure. For one, Israel struck a deal with Pfizer to share patient data on the efficacy and side effects of the Pfizer vaccine in real world use. Israel also has one of the world’s most efficient healthcare systems, complete with highly developed electronic healthcare records (EHRs) capable of collecting massive patient data in real time. The resulting database is well organized and filled with clean data — accurate and up-to-date data, in other words — which was vital to both understanding the disease and testing the vaccine.
Further, scientists, healthcare workers, and public health organizations around the world shared data and collaborated on finding insights and answers. The global response to the pandemic was a stellar display of how effective humankind can be in tamping down any threat when countries, health entities, and experts cooperate. The effort should be celebrated and commemorated for time eternal.
But all decision-makers can also learn from the shortcomings as well as the successes in this huge undertaking to end a dangerous pandemic. Chief among the shortcomings is that there is still uncertainty, after many months, about the specific actions that should be taken despite massive global data sets and ongoing analysis.
Businesses and other organizations find themselves in a similar predicament even in the absence of urgency, alarm, and dire consequences. In other words, even with the luxury of time and calmer heads, you can glean insights from data and still not know what to do about or with them. To put this in proper context, you should always remember this:
Even so, you can and should improve how you make decisions and judge them by their real world impacts. That requires the combined applications of several disciplines and more human input — a more than fitting definition of decision intelligence.
Going where humans fear to tread on data
Though the processes used under the big umbrella known as decision intelligence vary from one entity to the next, they’re likely to be more warmly embraced by people who were previously concerned that data analytics, and particularly those associated with AI, would eliminate their jobs.
AI, more often than traditional automation, is perceived by some as a direct competitor by managers and executives by virtue of science fiction depictions where AI is smarter than humans and capable of doing even high level jobs. That’s also partly because of the frequent and often wrong assumption that automation is limited to replacing jobs on the lower rungs of the career ladder. By comparison, AI cuts directly from the top. That point was first driven home when Deep Knowledge Ventures, a Hong Kong-based venture capitalist fund, added an algorithm named VITAL as a member of the board of directors in 2017. After that, it appeared that no job was safe from a machine takeover.
OK, some did note that appointing an algorithm to the board was likely a publicity stunt, since most board of directors use data to inform their votes, but the scare that AI may replace business leaders nevertheless lingers.
Deep Knowledge Ventures credits its algorithm with saving the company from bankruptcy caused by “overinvesting in overhyped projects.” Known as Vital (short for Validating Investment Tool for Advancing Life Science), the algorithm established itself as a seemingly crucial member of the board. It’s interesting that the rest of the board — or perhaps it was the stockholders? — apparently had little regard for the directors’ ability to keep the company financially strong.
Executives, whether at the head of business lines or at the top of the company pinnacle, typically fear data fueled algorithms. On the one hand, they’re expected to toe the data-driven company line. On the other hand, data-driven decisions may make their own talents obsolete.
In doesn’t help that executive pay, benefits, and perks are large line items in the biggest of company expenses: payroll costs. You can easily see where the same cost cutting logic that executives use every day could eliminate them as well.
Decision intelligence rebalances the scale by adding more weight to human roles in making key business decisions. That alone makes the concept welcome to leadership. However, decision intelligence is not a license nor the means to return to gut instinct, seat-of-the-pants, ego-driven, or agenda-loaded decision manipulations. The value in decision intelligence is that it is a far more effective way to make business decisions and savvy leaders will instantly grasp its importance to their organizations and careers.
In short, it is a rebalancing of how data is used and viewed. The evolution is in step with maturation patterns in other disciplines and a payback of sorts for data science’s contributions to those developments. One example speaks for many: computing and data science spurred the emergence of Digital Humanities as a new field in the 1950s and has enabled its steady improvement ever since. Now a similar development process is flowing in the other direction.
Decision intelligence is a recipe wherein data, automation, AI and human decision-making capabilities are blended to bake better outcomes into the processes. Further, it is a renewed focus beyond mechanical and digital efficiencies to make the outcomes more meaningful in human applications and impacts.
For many experts and observers, including many executives who have always highly valued business acumen in themselves and other people, decision intelligence’s acknowledgment and inclusion of the same is a natural progression in business applications.
