Does your organization want to avoid high-impact traps? A field through which this is possible is that of Business Intelligence (BI). No doubt, the highest-impact trap of this field is this one: How can executives, managers and professionals make the right decisions when, with today’s BI, vital information for making decisions remains invisible? That must change, mustn’t it?
Right now, Business Intelligence, with buzzwords such as big data, analytics and data warehouses is one of the big trends. No large organization can afford to miss this trend, it seems. But, Business Intelligence is not new. It evolved from Decision Support Systems. Yet, something is missing.
When I started to ask questions about lessons learned, and especially about how to prevent high-impact traps, there were no answers. That is more than worrying, as the future of companies depends on making the right decisions! Let’s have a closer look at this.
As a mathematician, I liked to do BI during the ‘80s and ‘90s. SAS was already a powerful BI tool. It delivered excellent results, up to a world-wide lead in IT storage efficiency. That’s to say, when the following requirements were met:
1. The data is readily available or can be created with a modest effort.
2. The data is clearly definable.
3. The data can be processed through mathematical formulas.
4. The maintenance aspects are manageable and their costs acceptable.
After many recent BI discussions, several presentations and exceptions granted, I am afraid that I have to conclude that today’s BI’s is limited to requirements 1 to 3 above. That’s indeed where it will work. But watch out: There are important lessons learned with requirement 2. Somehow, it appears requirement 4 has been lost. Part of this are also mathematics-based Artificial Intelligence solutions delivering BI. Let us call this BI-data.
Unfortunately, in today’s complex world, these four requirements are quite often not given. More so, no trend or accepted best practice can be seen that delivers the missing BI part. With that, it can be predicted that the missing part will not get priority or budget. Add cost savings pressures and elements that may still be in production will be removed.
This brings us to the best practice dilemma of the introduction post: The complication of the previous paragraph becomes invisible, as the root cause of insufficient information for decision making is outside of the partial solutions receiving priority and budget. The tragedy is this: Sooner or later, it will become visible that the wrong decisions have been made while the decision makers thought to have made the right decisions. People will be made responsible and will lose their jobs. It doesn’t end there. Their successors will fall into the same trap. In addition, it will be no surprise when productive employees lose their jobs. During the ‘80s and ‘90s, this was not the case where I worked.
At my employer, it was common practice to drop data, definitions and mathematics when the four requirements above were not given. In such situations, we looked for lessons learned, knowledge and experience. We reduced an overload of patterns down to those showing the way to the best possible solutions. Let us call this BI-knowledge. Quite often, we used both BI-data and BI-knowledge. This delivered a stream of solutions and services that were ahead of their time. Clients were more than happy. While others were still trying to convince colleagues to follow boring definitions and bureaucratic standards, our solutions were already in production.
With this, it is time for guiding questions you can ask.
It starts with a general question: Where is the solution approach that delivers what executives, managers and professionals need to make the best possible decisions? To be more precise:
1. Where is the training and coaching of BI-professionals in the lessons learned and in the associated solutions of the past 25 years?
2. Where are the BI-professionals who ensure that BI remains prior to the tipping point, beyond which the quality of data drops and costs grow exponentially?
3. Where is the priority for both BI-data and BI-knowledge in decision making processes?
4. Where is the solution for delivering BI-knowledge?
More about a possible solution for BI-knowledge in the next post.