Raise the Anticipation Quotient with Mobile BI
Practically everything has gone mobile now — business intelligence is no different. Information management now serves higher expectations for expansive information delivered through tiny displays. The challenge for most information managers is to harness the decision-making power of BI and squeeze it into a nice, neat package.
For example, most people agree that the ability to drill down is a useful feature of a good BI solution. The problem with this functionality in the mobile BI arena is that drilling down digs a deep well that is cumbersome for people to get into and back out. The customers of your mobile BI should not have to tap into dropdowns in order to find bits of buried information. Most people won’t try anyway. The power of mobile BI is making information readily available in the palm of our hands. The trade-off is it has to fit in the palm of our hands! We have to figure out what information is most desired and float it to the top. However, that’s only half the battle. The other challenge is identifying what bits are seldom (or never) needed and eliminate the clutter of too much information.
Raise the anticipation quotient
Mobile BI creates a powerful opportunity for information managers to raise the anticipation quotient of their organizations. Obviously, your organization’s leaders and managers need to be able to anticipate what the right decision is before it’s made. However, what’s often overlooked is anticipating a decision that will need to be made and being prepared to make it. For example, a colleague told me a story about playing a round of golf with a friend. Halfway through the round, the temperature dropped suddenly. My colleague pulled his jacket out of his bag, while his golfing buddy brought nothing warm to wear. My colleague had watched the weather forecast as a leading indicator of what to bring, while his golfing buddy did not and was consequently ill prepared. The measure of the two golfers’ preparation for making a decision is their respective anticipation quotients.
In math, a quotient is the result of dividing one thing by another (e.g., six divided by two gives a quotient of three). Thinking of it this way, we can calculate an anticipation quotient by dividing the number of possible outcomes by the number of decision variables that will affect the outcomes. We have no control over the number of possible outcomes, but we can limit the decision variables. The smaller the number of data points that are allowed into the decision equation, the higher the anticipation quotient becomes. The higher anticipation quotient, the better the chances leaders will be able to anticipate and make the right decision.
Thus, mobile BI becomes the perfect platform for raising the anticipation quotient. The limited visual output space forces us to put only the most important information upfront. If you can figure out the critical variables in a situation and move them to the foreground of a mobile app, you just improved someone’s anticipation quotient. You can start by eliminating all the variables that are mucking up the equation.
Reduce the Data Point Divisor
Raising the anticipation quotient is easier said than done. The key is determining which measures and data points to include within a mobile BI app and which ones are better left out (or moved lower in priority). I suggest the “measure the measures” mentality to make this determination. You can “measure the measures” in three ways:
1. Eliminate indicator overlap.
Do two or more of the decision-critical measures overlap? That is, do they tell a repetitive story? If measure A and measure B always correlate and agree to predict outcome C, why keep them both? As Winston Churchill said, “If two people agree on everything, one of them is unnecessary.” The same is true for key performance indicators. Use the most critical measure in your mobile BI and reserve the others for validation and verification purposes.
2. Exercise the 80/20 rule.
Which of the measures have the most impact on a decision? The 80/20 rule, also known as the Pareto principle or law of the vital few, says that approximately 80 percent of the outcome is often driven by 20 percent of the potential factors. Find the 20 percent of the performance measures that will affect 80 percent of the outcome and give them priority in your mobile BI application.
3. Develop leading indicators.
Is the measure a leading indicator or simply a historic value? Remember that past results do not always indicate future success. Mobile BI tools should not look primarily into the past for insights about what’s on the horizon. Business environments change too rapidly to rely primarily on past measures.
The late Dr. Russell Ackoff, a celebrated information management thought leader and professor who influenced people like Peter Drucker, published a thought-provoking paper called “Management Misinformation Systems.” Even though it was published nearly 50 years ago, the principles still resonate with the challenges of today. Dr. Ackoff wrote, “I do not deny that most managers lack a good deal of information they should have,” but, “it seems to me that they suffer more from an overabundance of irrelevant information.” Keep in mind that Dr. Ackoff penned that statement in 1967, so imagine how much more this problem has compounded in the current age of information. Mobile BI offers a rich opportunity to right this ship, because it forces us to focus on measuring what matters most and sort this out from the overabundance of information of lesser significance. When it comes to a critical decision, information that doesn’t matter is irrelevant, and we shouldn’t let it crowd the decision-level data in our mobile BI applications.