Murphy Choy

Business Analytic Opportunity Identification Process: Ontological Approach

In Uncategorized on October 23, 2011 at 6:18 am

A few days ago, we were having a meeting and discussing about the aspects of coming out with a procedure to identify processes or functional area groups where analytics can be applied vigorously and regularly in order to optimize the business. Now this is a very good idea in theory, but it is very difficult practice in reality. One of the key issue that one will face in trying to come out with such a process is that one can never truly understand all the underlying processes that governs an organization. This is particularly the case for companies that are very big all or multi-national in nature.

However this does not mean that we cannot identify processes in order to improve them. The simplest thing that one can actually do is to actually map out the entire process or the entire operational model or aspect of the organization. However, this is not without its difficulties. Many organizations because of the long established history of operation, they have actually forgotten about the origins of the many existing processes and could not give satisfactory explanation as to why this processes were placed or implemented. This resulted in many confusing meetings in which many senior managements will have difficulty communicating with the middle managers.

In order to identify processes, there are a few approaches to doing so and the approach given above is just one of the many processes possible used to identify potential areas. Now the above process is actually an empirical process which relies on the ability to map out the processes of the company. However in this case, I propose an ontological approach towards the identification of potential areas where business analytics can be applied easily. In in my proposal, suppose that any processes or any functional area in which it can be measured quantitatively – they can be optimized further through the use of analytic. Why do I use quantifiable measures? The reason behind is only quantifiable measures in our organization can be optimized by understanding the constituent components, modeling them and in turn improving the overall results. This is far more difficult to achieve using qualitative information. While this is something that I have not experimented with existing data, because this is an ontological approach this is after all a theoretical move.


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