In my blog Use Data To Support Arguments, Not Arguments To Support Data, I articulated how better-informed decisions are typically made and the role that business intelligence (BI) should play. Shortly after I wrote the blog, I experienced a real-life event that clearly illustrates three main phases of “data-entangled decisions.”

Since my family likes to take a day off from cooking on Fridays, we recently visited the deli of our favorite organic grocery store. At the take-out bar, I noticed an unusually long line of people under a large sign reading, “In-House Made Wing Buckets. All You Can Fill. On Sale for $4.99, Regular $9.99.” Well, I love wings and couldn’t resist the temptation to get a few.

The opportunity was to add wings (one of my favorite appetizers) to my dinner. But instead of using the special wings bucket, I chose the regular salad bar container, which was priced at $8.99 per pound regardless of the contents. I reasoned that the regular container was an easier-to-use option (shaped like a plate) and a cheaper option (since I was buying only a few wings). My assumptions about the best container to use led to a split-second decision—I “blinked” instead of “thinking twice.”

Interestingly, a nice employee saw me getting the wings in the regular container and approached me. Wary of my reaction, he politely reminded me of the sale and pointed out that I may pay more if I use the regular container because the wing bucket had a fixed cost (managed risk).

Although at first this sounded reasonable, when I asked if it would weigh enough to result in a higher cost, he took it to one of the scales behind the counter and discovered it was less than half a pound. This entire ordeal took less than 30 seconds and now I had the information I needed to make a better-informed decision.

This clinched it, because now two factors were in my favor. I knew that a half pound of the $8.99, regular-priced option was less than the $4.99, fixed-priced bucket option. And I knew that they would deduct the weight of the regular deli container at the register, resulting in an even lower price. I ended up paying $4.02.

This every-day event provides a good story to demonstrate the three phases as it relates to the business of better-informed decisions and the role of BI—or data in general.

Phase 1: Reaction

When the business opportunity (wing purchase) presented itself, I made some assumptions with limited data and formed my preliminary conclusion. If it weren’t for the store employee, I would have continued to proceed to the cash register ignorant of all the data. Sometimes in business, we tend to do precisely the same thing. We either don’t validate our initial assumptions and/or we make a decision based on our preliminary conclusions.

Phase 2: Validation

By weighing the container, I was able to obtain additional data and validate my assumptions to quickly take advantage of business opportunities —exactly what BI is supposed to do. With data, I was able to conclude with a great degree of confidence that I had mitigated the risk that it was the right approach. This is also typical of how BI can shed more light on many business decisions.

Phase 3: Execution

I made my decision by taking into account reliable data to support my argument, not arguments to support data. I was able to do this because I (as the decision maker) had an interest in relying on data and the data I needed was available to me in an objective form (use of the scale). This allowed me to eliminate any false personal judgments (like my initial assumptions or the employee’s recommendation).

• From the beginning, I could have disregarded the employee’s warning or simply not cared much about the final price. If that had been my attitude, then no data or BI tool would have made a difference in my final decision. And I might have been wrong.
• On the other hand, if I had listened to the initial argument by that nice employee without backing it up with data, I would have been equally wrong. I would have made a bad decision based on what appeared to be a reasonable argument that was actually flawed.
• When I insisted on asking the question that would validate the employee’s argument, I took a step that is the business equivalent of insisting on more data because we may not have enough to make a decision.
• By resorting to an objective and reliable method (using the scale), I was able to remove personal judgments.

In 20/20 hindsight

Now, I realize that business decisions are never this simple. Organizations’ risk is likely measured in the millions of dollars, not cents. And sometimes we don’t have the luxury of finding objective tools (such as the scale) in time to support our decision making. However, I believe that many business decisions mirror the same sequences.

Consider the implications if this were a business decision that resulted in a decision of $100 in the wrong direction. Now simply assume that these types of less-informed or uninformed decisions were made once a week throughout the year by 1000 employees. The impact would be $5 million.

Hence, the cost to our organization increases as:

• The cost of the error rises
• Errors are made more frequently
• The number of employees making the error grows

Bottom line

Better-informed decisions start and end with leadership that is keen to promote the culture of data-driven decision making. BI, if designed and implemented effectively, can be the framework that enables organizations of all sizes to drive growth and profitability.

What other obstacles do you face in making better-informed decisions?