Case Study: Auditing With Process Mining — Part II: Data Analysis Concept

Step 1: Data Analysis Concept

This is the 2nd article in our case study series on auditing with process mining. The series is written by Jasmine Handler and Andreas Preslmayr from the City of Vienna. You can find an overview of all the articles in the series here.

The City of Vienna Court of Audit follows a risk-based audit selection procedure. Therefore, the audit was already defined within the annual audit planning. The ‘Data analysis concept’ determines the audit scope in further detail. It gives an overview of the audited party, the process of interest, the IT framework, and the main audit objective.

The audited party - Wiener Stadtwerke - is one of Austria’s most significant infrastructure groups with about 15.000 employees. Its business activities can be categorized as follows:

  • Energy (electricity, gas, heating, cooling)
    • Generation
    • Distribution
    • Grid operation
  • Public transport (subway, tram, bus)
    • Traffic management and planning
    • Operation
    • Marketing
    • Distribution
  • Funeral
    • Cemeteries
    • Cemetery nursery
    • Stonemasonry
  • Car Parks

The total assets of Wiener Stadtwerke amounted to approximately Euro 13,900 million on 31 December 2020. The Wiener Stadtwerke Group was 100% owned by the City of Vienna.

We defined the process of interest in more detail by describing the general process scope. As we planned to audit the purchase-to-pay process, we delimited this process as depicted in Figure 4. The audited process scope comprised procurement and invoice processing, beginning with the demand report and ending with the payment of the corresponding invoice.

Purchase-to-pay process Figure 4: Purchase-to-pay process

This general process scope was used later as a reference point for further investigations and gave a first impression of the start and the end points of the process of interest.

The timeframe for the audit was determined to be the year 2019. More precisely, we considered all orders that were sent between 01 January 2019 and 31 December 2019.

We investigated the IT infrastructure of the City of Vienna Court of Audit and the audited party to get an idea about which type of data and which tools would be available to process and analyze this data.

From our preliminary research, we knew that the audited party used SAP to administrate the purchase-to-pay process. We expected that we would need to transform the data after we exported it from SAP. As an ETL tool to do these transformations, we chose the KNIME Analytics Platform because we already used this software for data transformations in earlier process mining projects and achieved good results.

The primary audit objective was to perform a compliance audit. We wanted to analyze the Wiener Stadtwerke’s purchase-to-pay process concerning its regularity and compliance with organization-specific framework conditions.

Due to this main audit objective, we planned to address aspects like the completeness of the process, segregation of duties, adherence to the four-eyes-principle, and the effectiveness of the internal control system. In addition, we wanted to consider the lead time and the occurrence of bottlenecks from a performance perspective. User experience questions were outside the scope of this audit.

After defining the general framework and the primary audit objective, it was time to specify the focus areas of the audit in more detail. So, in the next step, we identified the concrete analysis questions we wanted to answer within our process mining analysis.

New parts in this auditing series will appear on this blog every week. Simply come back or sign up to be notified about new blog entries here.

Process Mining Café 19: Auditing

Process Mining Café 19

To kick off our new series on process mining in audit, we have invited the authors of the article, Jasmine Handler and Andreas Preslmayr from the City of Vienna to the upcoming Process Mining Café this Wednesday. Join us!

Process mining does not replace the traditional audit approach. However, it requires some changes and a conscious effort to fit process mining into the existing way of working. In some places, more work is needed. In other places, things get easier. Together with Jasmine and Andreas, we will talk about their data-driven audit approach with process mining step by step.

Discuss with us about the changes to the regular audit process and the benefits and challenges this week, Wednesday, 1 February, at 15:00 CET! (Check your timezone here). As always, there is no registration required. Simply point your browser to when it is time. You can watch the café and join the discussion while we are on the air, right there on the café website.

Sign up for the café mailing list here to receive a reminder and the PDF version of the full article one hour before the session starts.

Tune in live for Process Mining Café by visiting this week, Wednesday, 1 February, at 15:00 CET! Add the time to your calendar if you don’t want to miss it.

Case Study: Auditing With Process Mining — Part I: Overview

Process Mining Camp 2021

This is a guest article by Jasmine Handler and Andreas Preslmayr from the City of Vienna. They previously presented their case study at the Process Mining Camp here. Now, they have written up their experience to help others learn from it even more. If you have a guest article or process mining case study that you would like to share, please get in touch with us via

Auditors have a clearly defined process in which they carry out an audit. Process mining does not replace this traditional audit approach. However, it requires some changes and a conscious effort to fit process mining into the existing way of working. In some places, more work is needed. In other places, things get easier. In this article, we describe in detail how process mining fits into the different phases of the audit cycle based on a concrete project. We describe the changes that needed to be made to the audit process and the benefits and challenges.

City of Vienna Court of Audit

The City of Vienna Court of Audit is an autonomous and independent public audit institution. It audits the institutions and entities in the City of Vienna concerning their financial management and safety. And it supports those in positions of responsibility in politics and administration with audit reports and recommendations.

In the framework of its audit work, the City of Vienna Court of Audit reviews the use of Vienna’s public funds. It also monitors compliance with safety regulations to protect the citizens of Vienna and its visitors.

However, the resources to perform these tasks are limited. To set audit priorities, the City of Vienna Court of Audit follows a selection procedure for audits in the form of a risk analysis.

For the selected audits, the City of Vienna Court of Audit team then uses different audit procedures depending on the audit. Process mining has been used as an audit method in several audits since 2016. In 2020, the purchase-to-pay process of the Wiener Stadtwerke, one of Austria’s largest infrastructure groups, was analyzed.

Using the example of the Wiener Stadtwerke, this article shows in detail how process mining was applied to perform a data-driven audit. The results of this audit are publicly available in the audit report here. The following article focuses on the method of process mining in the context of an audit. It describes in detail how process mining was leveraged in the different audit phases and the challenges and benefits we experienced.

Data driven audit approach with process mining

Internationally accepted audit standards guide the audit work of the City of Vienna Court of Audit. At the same time, it is the goal to further improve the existing standards in cooperation with national and international audit institutions while engaging in the exchange of experiences. Audits are carried out according to the standardized audit process (see Figure 1).

The overall audit approach Figure 1: Overall audit process

Each step of the process depicted above contains several tasks. Figure 2 shows the tasks related to the step ‘Conducting the audit’: First, an audit concept is created. Then, the data is collected. This data is then used to perform a situational and deviation analysis, from which the audit results and recommendations are derived. In addition, the audit trail and evidence are documented, and the audit file is generated.

Conducting the audit Figure 2: Conducting the audit

We must adapt our working method when we use process mining in our general audit approach. Especially the way of collecting and analyzing data changes within the audit process compared to other audit methods.

To include process mining into the ’Collection of data’ and ‘Situational and deviation analysis’ phases, we followed the nine steps shown in Figure 3.

Data driven audit approach with process mining Figure 3: Data driven audit approach with process mining

Following this model helped us a lot to standardize our approach when using process mining in an audit. It summarizes the essential deliverables for collecting the data and performing the situational and deviation analysis. In the following sections, we explain each step and each deliverable of the model depicted in Figure 3 in more detail.

  • Step 1 - Data analysis concept
  • Step 2 - Analysis questions (next week)
  • Step 3 - Process and data model (coming soon)
  • Step 4 - Raw data (coming soon)
  • Step 5 - Data transformation (coming soon)
  • Step 6 - Data sets (coming soon)
  • Step 7 - Discovered model (coming soon)
  • Step 8 - Analysis answers (coming soon)
  • Step 9 - Data analysis results (coming soon)
  • Challenges and benefits (coming soon)

New parts in this series will appear on this blog every week. Simply come back or sign up to be notified about new blog entries here.

Understanding How People Analyze Their Process Mining Data

How exactly do people analyze their data during the explorative and targeted analysis phases of a process mining project? This is the topic of a whole sub field in the process mining research area.

In our latest Process Mining Café, we spoke with Pnina Soffer from Haifa University and Barbara Weber and Francesca Zerbato from the University of St. Gallen about the process of process mining.

Researchers approach the topic from two different angles: (1) A top-down approach guided by cognitive psychology and (2) a bottom-up approach based on behavioral discovery (process mining!). For both approaches, the researchers collect detailed data sets in user test labs that capture the analysts’ behavior in multiple dimensions: Think aloud data, application logs, screen recordings, interview data, and even eye tracking data. In the café, we looked at two of these data sets to see what you can learn from them.

If you missed the live broadcast or want to re-watch the café, you can now watch the recording here. Thanks again to Pnina, Francesca, Barbara, and all of you for joining us!

Here are the links to the papers that we mentioned during the session1:

  • Zerbato, F., Soffer, P., Weber, B. (2021). Initial Insights into Exploratory Process Mining Practices. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management Forum. BPM 2021. LNBIP, vol 427. Springer, Cham.

  • Zimmermann, L., Zerbato, F., Weber, B. (2022). Process Mining Challenges Perceived by Analysts: An Interview Study. In: Augusto, A., Gill, A., Bork, D., Nurcan, S., Reinhartz-Berger, I., Schmidt, R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2022 2022. LNBIP, vol 450. Springer, Cham.

  • Zerbato, F., Soffer, P., Weber, B. (2022). Process Mining Practices: Evidence from Interviews. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management. BPM 2022. LNCS, vol 13420. Springer, Cham.

  • Zerbato, F., Koorn, J.J., Beerepot, I., Reijers, H., Weber, B. (2022). On the Origins of Questions in Process Mining Projects. In: Almeida, J.P.A., Karastoyanova, D., Guizzardi, G., Montali, M., Maggi, F.M., Fonseca, C.M. (eds) Enterprise Design, Operations, and Computing. EDOC 2022. Lecture Notes in Computer Science, vol 13585. Springer, Cham.

Contact us via if you have questions or suggestions for the café anytime.

  1. We link to pre-prints where the open-access version is not available. Note that some of the research that we discussed is still ongoing and has not been published yet. You can contact Pnina, Francesca, and Barbara directly if you have questions about their work. ↩︎

Disco 3.3

Software Update

We are happy to announce the release of Disco 3.3.

This update fixes a number of issues and annoyances that we have discovered, and that you have reported, since the last 3.2 release. If you are affected by any of these isolated issues, you probably already know.

While we were at it, we put on the winter tires and topped up the oil, to get us ready for the cold season. So you get the latest security fixes and performance improvements all around. And of course we thoroughly dusted the corners and polished the UI some, so this baby’s ready to drive!

All this comes fuelled, as per usual, by your ideas and bug reports. Keep us posted and, as always, thank you for using Disco!

How to update

We recommend that you update to the latest version of Disco at your earliest convenience. Disco will automatically download and install this update the next time you run it, if you are connected to the internet1.

If you prefer to install this update of Disco manually, you can download and run the latest installer packages from


  • CSV Import:
    • Fixed an issue where import errors could point to one-off line numbers.
    • Prioritize high-severity issues in import problem feedback.
    • Improved description of import problems.
  • Excel Import:
    • Improved XLSX import.
    • Improved performance and stability.
  • Process Map:
    • Fixed an isolated issue with graph layout on the Apple M1/2 platform.
    • Improved performance and stability of graph layout.
  • Airlift: Improved import performance and stability.
  • Control Center: Fixed an issue where disk benchmark could fail.
  • UI: Improved interface fidelity for fractional HiDPI displays on Windows.
  • Connection: Improved stability and performance.
  • Platform: Java update

  1. You need to download and install this update manually to make sure you get the latest version of the Java runtime and graph layout. ↩︎