In my previous post, I said, en passant, that the purpose of Business Intelligence is building a business model, whose purpose is to return the KPIs required to assess business performances and to predict how these performances may vary in response to internal decision and external perturbations.
I had some private discussions on this subject, which I believe is central, that made me think that a clarification is required.
I am old enough to remember when, in the first Decision Support Systems, some vendors used to call their cubes "Models". At the time we were implicitly conscious that we were building mathematical models that were describing part of the business.
Even the figures in a simple report comparing sales vs target are the outcome of a mathematical definition that is built in the BI system in use. Actually, in my previous post the term Key Performance Indicator (KPI) was used quite loosely to name all the numbers that may be extracted from a business model and represent something meaningful for the purpose to control the business
The output look different from what is obtained from models in scientific research because presentation for the business is generally made simpler and graphically appealing. The process and the tools used to implement the model are different as well, because business requires a much higher ease of use and constant updates.
Nevertheless, there is a set of mathematical rules that link the raw data to the outcome consumed. I think that we can all agree with that.
The crucial point that is often missed is that the model, to be really effective, must encompass as many business processes as possible and identify the mathematical rules that link them. If we see the company as composed of linked processes, one change in one of them will bring along changes in others. The real power of BI is unleashed when I can numerically assess these dependencies.
Since I produced 100 widgets and sold 50, my stock went up, my assets did it as well, receivables and bank account varied, the number of customers buying widgets as well, the post sale support calls went up and since I sold in Japan for the first time I had to hire some Japanese speaking staff and payroll thus changed, bringing along changes in my credit lines etc. etc.
Once these dependencies have been assessed and a sound business mathematical model has been identified, we finally get the possibility to assess the likely outcome of a management decision or the impact of a change in external conditions. Every internal project may be weighted against a sound estimate of its effects, thus reducing the amount of gut feeling involved in business management. I have been asked time and again by long sighted managers to build something like this, and planning and budgeting applications are usually the place where the model may live. This is the reason why I keep including Performance Management in BI as it is its natural extension.
If we compare this vision with the traditional BI view of "providing the right data to the right person at the right time", we see how the latter is really simplistic and naive.
What is the role of big data technologies in all of this? They just provide new inputs and a new processing power that may help to make the model more accurate at a cost lower than what was possible just few years ago.
What is the role of the data scientist in all of this? She is one of the assets required to design the model and to implement it in an engineered way.
The road that leads to such an integrate business vision is often long and hard. In my career, I did not have more than a couple of customers which reached a point where a consistent business model was in place. The results, however, were shining brightly. They could run their business with way less cash at hand, they cut all the business dead branches and all the new initiatives were accurately assessed so no more money sinks were created. This is evidence based management at its best.
I hope that this post might spark some discussion about the means and the purpose of BI. If someone among the professionals in the BI space would start elaborating along these lines, I will be absolutely happy.