Anne Rozinat & Christian W. Günther

Consider Anonymization – Process Mining Rule 3 of 4

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This is article no. 3 of the four-part article series Privacy, Security and Ethics in Process Mining.

Read this article in German:
“Datenschutz, Sicherheit und Ethik beim Process Mining – Regel 3 von 4“

If you have sensitive information in your data set, instead of removing it you can also consider the use of anonymization. When you anonymize a set of values, then the actual values (for example, the employee names “Mary Jones”, “Fred Smith”, etc.) will be replaced by another value (for example, “Resource 1”, “Resource 2”, etc.).

If the same original value appears multiple times in the data set, then it will be replaced with the same replacement value (“Mary Jones” will always be replaced by “Resource 1”). This way, anonymization allows you to obfuscate the original data but it preserves the patterns in the data set for your analysis. For example, you will still be able to analyze the workload distribution across all employees without seeing the actual names.

Some process mining tools (Disco and ProM) include anonymization functionality. This means that you can import your data into the process mining tool and select which data fields should be anonymized. For example, you can choose to anonymize just the Case IDs, the resource name, attribute values, or the timestamps. Then you export the anonymized data set and you can distribute it among your team for further analysis.

Do:

  • Determine which data fields are sensitive and need to be anonymized (see also the list of common process mining attributes and how they are impacted if anonymized).
  • Keep in mind that despite the anonymization certain information may still be identifiable. For example, there may be just one patient having a very rare disease, or the birthday information of your customer combined with their place of birth may narrow down the set of possible people so much that the data is not anonymous anymore.

Don’t:

  • Anonymize the data before you have cleaned your data, because after the anonymization the data cleaning may not be possible anymore. For example, imagine that slightly different customer category names are used in different regions but they actually mean the same. You would like to merge these different names in a data cleaning step. However, after you have anonymized the names as “Category 1”, “Category 2”, etc. the data cleaning cannot be done anymore.
  • Anonymize fields that do not need to be anonymized. While anonymization can help to preserve patterns in your data, you can easily lose relevant information. For example, if you anonymize the Case ID in your incident management process, then you cannot look up the ticket number of the incident in the service desk system anymore. By establishing a collaborative culture around your process mining initiative (see guideline No. 4) and by working in a responsible, goal-oriented way, you can often work openly with the original data that you have within your team.
Anne Rozinat & Christian W. Günther

Responsible Handling of Data – Process Mining Rule 2 of 4

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This is article no. 2 of the four-part article series Privacy, Security and Ethics in Process Mining.

Read this article in German:
“Datenschutz, Sicherheit und Ethik beim Process Mining – Regel 2 von 4“

Like in any other data analysis technique, you must be careful with the data once you have obtained it. In many projects, nobody thinks about the data handling until it is brought up by the security department. Be that person who thinks about the appropriate level of protection and has a clear plan already prior to the collection of the data.

Do:

  • Have external parties sign a Non Disclosure Agreement (NDA) to ensure the confidentiality of the data. This holds, for example, for consultants you have hired to perform the process mining analysis for you, or for researchers who are participating in your project. Contact your legal department for this. They will have standard NDAs that you can use.
  • Make sure that the hard drive of your laptop, external hard drives, and USB sticks that you use to transfer the data and your analysis results are encrypted.

Don’t:

  • Give the data set to your co-workers before you have checked what is actually in the data. For example, it could be that the data set contains more information than you requested, or that it contains sensitive data that you did not think about. For example, the names of doctors and nurses might be mentioned in a free-text medical notes attribute. Make sure you remove or anonymize (see guideline No. 3) all sensitive data before you pass it on.
  • Upload your data to a cloud-based process mining tool without checking that your organization allows you to upload this kind of data. Instead, use a desktop-based process mining tool (like Disco [3] or ProM [4]) to analyze your data locally or get the cloud-based process mining vendor to set-up an on-premise version of their software within your organization. This is also true for cloud-based storage services like Dropbox: Don’t just store data or analysis results in the cloud even if it is convenient.
Anne Rozinat & Christian W. Günther

Clarify Goal of the Analysis – Process Mining Rule 1 of 4

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This is article no. 1 of the four-part article series Privacy, Security and Ethics in Process Mining.

Read this article in German:
“Datenschutz, Sicherheit und Ethik beim Process Mining – Regel 1 von 4“

Clarify Goal of the Analysis

The good news is that in most situations Process Mining does not need to evaluate personal information, because it usually focuses on the internal organizational processes rather than, for example, on customer profiles. Furthermore, you are investigating the overall process patterns. For example, a process miner is typically looking for ways to organize the process in a smarter way to avoid unnecessary idle times rather than trying to make people work faster.

However, as soon as you would like to better understand the performance of a particular process, you often need to know more about other case attributes that could explain variations in process behaviours or performance. And people might become worried about where this will lead them.

Therefore, already at the very beginning of the process mining project, you should think about the goal of the analysis. Be clear about how the results will be used. Think about what problem are you trying to solve and what data you need to solve this problem.

Do:

  • Check whether there are legal restrictions regarding the data. For example, in Germany employee-related data cannot be used and typically simply would not be extracted in the first place. If your project relates to analyzing customer data, make sure you understand the restrictions and consider anonymization options (see guideline No. 3).
  • Consider establishing an ethical charter that states the goal of the project, including what will and what will not be done based on the analysis. For example, you can clearly state that the goal is not to evaluate the performance of the employees. Communicate to the people who are responsible for extracting the data what these goals are and ask for their assistance to prepare the data accordingly.

Don’t:

  • Start out with a fuzzy idea and simply extract all the data you can get. Instead, think about what problem are you trying to solve? And what data do you actually need to solve this problem? Your project should focus on business goals that can get the support of the process managers you work with (see guideline No. 4).
  • Make your first project too big. Instead, focus on one process with a clear goal. If you make the scope of your project too big, people might block it or work against you while they do not yet even understand what process mining can do.
Mathias Golombek

Data Science on a large scale – can it be done?

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Analytics drives business

In today’s digital world, data has become the crucial success factor for businesses as they seek to maintain a competitive advantage, and there are numerous examples of how companies have found smart ways of monetizing data and deriving value accordingly.

On the one hand, many companies use data analytics to streamline production lines, optimize marketing channels, minimize logistics costs and improve customer retention rates.  These use cases are often described under the umbrella term of operational BI, where decisions are based on data to improve a company’s internal operations, whether that be a company in the manufacturing industry or an e-commerce platform.

On the other hand, over the last few years, a whole range of new service-oriented companies have popped up whose revenue models wholly depend on data analytics.  These Data-Driven Businesses have contributed largely to the ongoing development of new technologies that make it possible to process and analyze large amounts of data to find the right insights.  The better these technologies are leveraged, the better their value-add and the better for their business success.  Indeed, without data and data analytics, they don’t have a business.

Data Science – hype or has it always been around?Druck

In my opinion, there is too much buzz around the new era of data scientists.  Ten years ago, people simply called it data mining, describing similar skills and methods.  What has actually changed is the fact that businesses are now confronted with new types of data sources such as mobile devices and data-driven applications rather than statistical methodologies.  I described that idea in detail in my recent post Let’s replace the Vs of Big Data with a single D.

But, of course, you cannot deny that the importance of these data crunchers has increased significantly. The art of mining data mountains (or perhaps I should say “diving through data lakes”) to find appropriate insights and models and then find the right answers to urgent, business-critical questions has become very popular these days.

The challenge: Data Science with large volumes?

Michael Stonebraker, winner of the Turing Award 2014, has been quoted as saying: “The change will come when business analysts who work with SQL on large amounts of data give way to data EXASOL Pipelinescientists, which will involve more sophisticated analysis, predictive modeling, regressions and Bayesian classification. That stuff at scale doesn’t work well on anyone’s engine right now. If you want to do complex analytics on big data, you have a big problem right now.”

And if you look at the limitations of existing statistical environments out there using R, Python, Java, Julia and other languages, I think he is absolutely right.  Once the data scientists have to handle larger volumes, the tools are just not powerful and scalable enough.  This results in data sampling or aggregation to make statistical algorithms applicable at all.

A new architecture for “Big Data Science”

We at EXASOL have worked hard to develop a smart solution to respond to this challenge.  Imagine that it is possible to use raw data and intelligent statistical models on very large data sets, directly at the place where the data is stored.  Where the data is processed in-memory to achieve optimal performance, all distributed across a powerful MPP cluster of servers, in an environment where you can now “install” the programming language of your choice.

Sounds far-fetched?  If you are not convinced, then I highly recommend you have a look at our brand-new in-database analytic programming platform, which is deeply integrated in our parallel in-memory engine and extensible through using nearly any programming language and statistical library.

For further information on our approach to big data science, go ahead and download a copy of our technical whitepaper:  Big Data Science – The future of analytics.