Process Mining

Process Mining – Knowing what really works, and how

Companies know their business processes, albeit usually on a rather theoretical basis. In reality, they often lack full process transparency.

There are many reasons for this: workflows are perceived subjectively, steps are unwittingly added. And as a rule, employees only know about the sub-processes associated with their work. Processes also change, for example due to new compliance requirements or changing customer needs. And finally, the intended process flow is not always a good fit, for example in the case of special, non-standard production requirements.

At the same time, there is constant pressure to make all processes more efficient. However, you can only optimise a process if you are completely aware of it. As the saying goes:

"You can't fix what you can't see."

And this is where process mining comes into play: the automated analysis of business processes based on digital traces in IT systems. Traces are left, for example, when a customer complaint is processed, starting with electronic receipt, creation of a ticket and its processing and ending with the feedback by e-mail to the customer. Process mining tools visualise the overall process by evaluating corresponding event logs and the information contained in them, such as case IDs, activity names, time stamps, supplier information, etc.

Like the red pill, process mining opens users' eyes to business processes as they really are. Seeing this reality then enables companies to identify inefficiencies and potential for optimisation. As a result, measures can be implemented to improve business transactions.

How Process Mining Works

After companies have defined the processes to be analysed, process mining usually takes place in five steps:

  • Process data acquisition

    Relevant event data is extracted from a company's event log systems and integrated into the process mining software. In the simplest case, the a result log is extracted  in the form of a CSV file. Advanced solutions enable continuous synchronisation of process data.

  • Process visualisation

    The individual events are mapped, from the beginning of the process. The result is an end-to-end visualisation of the entire process as a flowchart, including all the variants, and as such a faithful model of the process reality in the company.

  • Process analysis

    The depicted processes are examined by asking a series of questions in order to understand why process inefficiencies occur and to quantify their effects on key performance indicators. Typical examples include: "Where are the bottlenecks?", "Which resources are stretched?", "How does a certain variant affect a process KPI (e.g. operating costs)?".

  • Process Benchmarking

    The existing processes can be compared with target specifications or against each other (for example at a regional level). Subsequently, the deviation of the actual process from the ideal and/or a comparison process is measured, and also whether these deviations are acceptable or if it makes more sense to make adjustments.

  • Processes

    From the knowledge about how processes work and how well they are running companies derive optimisation measures that are geared towards goals such as increasing price efficiency, accelerating process execution or optimising productivity. 


Process mining is not a one-time project, but a cycle of the analysis, improvement and monitoring of processes. Companies must continuously review their business processes in order to be able to consistently exploit optimisation potential. Thus it is all the more important to deploy technology that can be used easily and without spending a lot of time in any of the phases of process mining.

Process Mining based on Business Intelligence

Process mining based on business intelligence is particularly popular with companies which want to optimise their processes. BI technologies enable companies to analyse processes interactively and easily in a self-service process. They can

  • extract, merge and check the quality of event data from heterogeneous system landscapes,
  • ensure efficient, data governance-compliant management of data,
  • use KPI analyses to assess the extent to which processes are relevant for the KPI specifications which form the basis of strategic corporate goals,
  • visualise KPIs in an easily understandable and appealing way,
  • evaluate data on an ad hoc basis and use simulation analyses to check and compare the effects of process changes in advance, and
  • establish a process hierarchy according to the importance and benefits of each and also prioritise which processes would benefit from and become more cost-efficient through optimisation measures such as automation.

Process Mining in Practice

Companies benefit from the use of process mining technology through the possible

  • optimisation of operational efficiency
  • increased employee productivity
  • improved customer satisfaction

Process mining is particularly suitable for companies with large, process-relevant amounts of data, in which inefficient business processes have significant negative consequences.

Process Mining in Practice


Industry-specific use cases Process Mining

  • Retail:

    efficient, smooth e-commerce processes to optimise the customer journey and boost customer satisfaction

  • Financial services:

    continuous and automated detection of abnormal behaviours, for example in loan application processes

  • Healthcare:

    consistent, efficient, digitally supported patient support from initial contact to the end of treatment

  • Telecommunications:

    optimised processes from commissioning to activation

Functional Use Cases Process Mining

Typical application scenarios in central business areas are:

  • Sales


    • Identifying the reasons for overly long lead-to-order times
    • Checking whether the marketing measures are suitable for increasing conversion rates


    • Analysing the reasons for late deliveries
    • Identifying the causes of order cancellations and changes by customers


    • Identification of automatable steps to reduce error rate and post-processing time
    • Identification of suppliers, products or departments that cause delivery delays

    • Analysis of supply chains, quotas and material procurement
    • Analysis of the geographical reach of warehouses
    • Identification of the causes of delivery delays
    • Returns/Complaint Management

    • Detection of inefficiencies within production processes
    • Process-accompanying warning messages in the event of deviations from the standard during production

    Accounts receivable

    • Analysis of the causes of unpunctual payments by customers
    • Examining payment behaviour on granting cash discounts
    • Identifying bottlenecks in the invoicing process
    • Identifying reasons for sending unnecessary payment reminders

    Accounts payable

    • Identifying reasons for payment delays
    • Analysing the causes of incorrect invoices


    • Analysing end customer processes
    • Risk detection

    • Identifying errors or gaps in the IT systems
    • Uncovering the causes of long resolution times for tickets
    • Checking the service quality of the call centre

Process mining is on the rise

The fact that more and more companies are opting for the "red pill" for process optimisation is shown by the increasing investments in process mining technology: According to Everest Group, the global process mining software market is experiencing annual growth of 60 to 70 percent, reaching $320 million to $340 million in 2020*. The number of available solutions has also grown significantly in recent years – and thus presents companies with the "agony of choice". As a BI and process mining expert with practical experience, how can Informatec support the successful use of process mining for long-term process optimisation with the right technologies? The best way to clarify this is in a personal conversation.

*Everest Group, Complimentary Abstract “Enabling Data-based Process DNA Analysis – Process Mining State of the Market Report 2021” - Link

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