How do processes run, what defines process performance, and what are decisions based on? With a good picture of the business context, we identify and select the processes where data science applications can make the most impact.
To get maximum results from the identified opportunities, we gather information from process stakeholders. In addition, we dive into the information systems to identify relevant data.
To make sense of the process data, we apply various data science and process mining techniques. With these, we validate and quantify the previously identified opportunities.
Acting on the identified opportunities, we develop a solution to capitalize on them. Sometimes we use more complex data science techniques, but more often a simple process change or small RPA-bot proves to be effective.
We don't leave until the solution is embedded in the organization. That means that the process stakeholders can work with the solution independently, and can monitor it.