Process-Aware Analytics: Business Processes and Advanced Analytics integrated efficiently
Process-Aware Analytics: Business Processes and Advanced Analytics integrated efficiently

Process-Aware Analytics: Business Processes and Advanced Analytics integrated efficiently

Whether implicitly or direct – analytics have to contribute to business processes.

Implicit contribution is given whenever business involves analytical capabilities. It is an indirect establishment of ad-hoc services, which are made available to business as a supplement for various decisions in processes. This approach provides a maximum degree of flexibility and the ability to adapt to the business needs in a very agile manner. But often it comes at a high cost. The transfer of business questions into appropriate analytical models always leaves a notion of inefficiency.

It comes at hand, that starting from an analytical model would help to come around the issues of high costs and inefficiency. Building a model upfront, which tackle specific needs in the process lead into tool-based analytics. Analytics are made available directly to a business within dedicated process steps to address a specific need. They can come at relatively low cost – e.g. via appropriate segmentations. But is the use of predefined models still advanced analytics? I tend to have some doubts.

My experience has shown that this he use of predefined models is working out in very few cases. More often the results of “predefined analytics” lead into further questions. Results are not that clear that they can be converted into decisions and actions directly. It tends to lead back into the implicit approach, where we see the need for ad-hoc capabilities.

The obvious approaches are not really compelling. It feels like being trapped. We should look for an alternative if we do not want to accept high costs or blurry results. The answer lies within the process.

The big challenge of advanced analytics lies within the transfer of business problems into analytical models and – vice versa – the integration of analytical results into business decisions. Here we have to look at the process. The process defines the actions and decisions to be taken to run your business in an adequate way. As this is the starting point for optimizations, analytics have to support these processes. To do this in an efficient AND flexible way, the process has deliver a clearly understandable input to the analytics. It has to include information about what kind of information is required and why this information is required. By then, this can be translated into analytical measures easily without the need to predefined models. And even more important, it will give analytics an idea, what kind of precision is required to take an action, as this can have a major impact on the choice for a simple or complex model.

We can come up with a setup, where the process defines the framing for analytics, which can be understood by everyone. Analytics can take the decision, where standard models will do the thing and where some more advanced or investigative methods will be required. Analytics will have the chance to adapt results and act in a very agile manner, whereas business can be sure that it gets the results. It will help them to drive decisions and actions in a better way. Let’s replace advanced analytics by process-aware analytics and we will come up with a setup with optimized costs.

Makes sense?