With the advance of Digitization, Big Data and Industry 4.0, the need for a clear, flexible visualization of the data that floods companies' servers every day is growing. At the same time, however, the volume and complexity of this data is also increasing, and with it the need to collect more than just quantitative data and evaluate it using classic Key Performance Indicator models. Those who want to be fit for the digital transformation must draw qualitative and process-oriented conclusions from large volumes of data and digital traces in order to be able to localize and optimize weaknesses in business processes. 

But how to deal with this challenge?

An innovative solution lies in the intelligent, software-based fusion between Business Intelligence (BI) and Process Mining. What this fusion and the resulting synergy effects mean for the optimization of business processes is explained below. First, however, a closer look at the two approaches and what makes them special:

Business Intelligence (BI)

BI refers to a form of business analytics and is an IT word from business information technology circles that has been circulating since the 90s. Due to the digital transformation, companies today are confronted with a jumble of data flowing into their server structures from all sources, such as SAP, other ERP systems, MS Excel, etc.

By collecting, evaluating and presenting this data using BI, concrete statements can be made about the performance of one's own company. In addition, through the systematic analysis of Big Data, correlations of the data can be worked out on the meta-level and Key Performance Indicators (KPIs) can be made tangible through visualization.

The added value lies in using the insights gained from data analysis to improve business processes, customer and supplier relationships, but also to better plan business strategies in advance and, for example, to make predictions about problems and upcoming trends.

Process Mining

The term "Process Mining" refers to the automated analysis of business processes based on digital traces in IT systems. If these traces are evaluated and all relevant key figures are included, the implicit process knowledge contained in the data can be visualized and thus made tangible and transportable. In classic BI applications, this knowledge remains hidden. Accordingly, the decisive difference between Process Mining and BI is the ability to visualize not only the initial and final data of business processes on the basis of KPIs, but also to represent processes in the totality of all their individual process steps and thus to examine them in greater depth.

This means that a to-be process can be compared with an as-is process and traced back to the core in order to identify weaknesses and outliers. Processes can then not only be optimized, but also harmonized. This saves time, resources and, above all, costs.

The Golden Symbiosis of BI and Process Mining

Although BI can be used to visualize and evaluate KPIs, it is precisely the analysis of individual business process steps from Process Mining that is missing in order to be able to precisely trace the causes of errors from process flows and their deviations early in the process using Process Performance Indicators (PPIs). Conversely, Process Mining lacks a visually appealing presentation of performance indicators (PPIs and KPIs) as well as the ability to perform e.g. ad-hoc analyses (also known as Self Service BI), which are otherwise familiar from typical BI tools.

The crucial crux now is to combine both technologies to achieve a "symbiosis" and to benefit from the unique synergy effect of both approaches. BI-based Process Mining enables an interactive Self Service Analysis of business processes based on easily interpretable visualizations and the comfortable comparison of Key Performance Indicators and process variants. This not only ensures a higher quality of the end results, but also creates more transparency within processes and the "Big Data" conglomerate.

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