by Kaan Turnali, Global Senior Director, Enterprise Analytics, SAP
The concept of “better-informed” decisions is distinctly different than the concept of “better” decisions— the former is generally a choice, whereas the latter often results from an action. Better-informed leaders don’t always make better decisions, but better decisions almost always start with better-informed leaders. Business intelligence (BI) can be the framework that enables organizations of all sizes to make faster, better-informed business decisions.
BI Should Play a Role in Better-Informed Decisions
This same principle equally applies to individuals such as better-informed patients or better-informed consumers. Ultimately, when the final decision lies with us (humans), we either choose to ignore the data or choose to use it in our decision making—assuming, of course, that it exists and we can trust it. However, even the best implementations of BI solutions can neither change nor prevent uninformed or less-informed decisions if we choose to ignore the data.
Typically, “data-entangled decisions” involve potential use of data for analysis compared to other decisions that may be driven purely by our emotional states or desires. Most business decisions are data-entangled decisions. In these, existing or new data can play an important role compared to a personal decision, such as when to go to sleep. A data-entangled decision in general follows three main phases when a business question, challenge, or opportunity presents itself. BI, if designed and implemented effectively, should support all three phases.
Phase One: Reaction
In the reaction phase, the initial course is fashioned out of an immediate reaction to a threat or an opportunity. Typically, some preliminary figures are accompanied by known assumptions that form the initial direction. In this early stage, initial data is still “entangled” and only the requirement for additional information can be outlined. In some cases, however, the decision may be already made and if so, the effort to gather additional data for further analysis becomes a futile exercise.
Phase Two: Validation
Additional data produces opportunities for in-depth analysis, which should eventually lead to actionable insight. But these results need to be validated first using some type of critical thinking. Moreover, who validates the results is as critical as how it’s done.
Just as we don’t ask programmers to validate their own code, we don’t ask analysts or managers to validate their own conclusions of data. If available or feasible, objective methods that can remove assumptions or personal deductions from this phase provide the fastest and clearest path to actionable insight.
Phase Three: Execution
The execution phase is where the final decision will be made and the use of data will be completely up to the person in charge of the decision. There are three possibilities before the final decision is made and action is taken:
- The conclusion is supported by data, and we choose to take it into account for our decision.
- The conclusion is supported by data, and we choose to ignore it.
- The conclusion isn’t or can’t be supported by data, and we are left to our own judgment to make the decision.
In business, better-informed decisions often start with a strong appetite for data, followed by a healthy dose of skepticism for it. If available, our collective insight becomes the guiding light for our decisions enhanced by data. In the absence of it—when we are left to decide by ourselves—we seek wisdom in our own experiences to fill the void where we can’t find or rely on data.
The bottom line is, we need to use data to support our arguments instead of using arguments to support our data. And BI, if designed and implemented effectively, should be the framework that supports all of this by enabling us to make faster, better-informed decision at all levels of our organization. This, in turn, helps us drive growth and profitability.
Where do you see the biggest challenge in making better-informed decisions?