Process Mining and Essential Data Science Tips

For today’s businesses, the gap between ideal processes and process realities can lead to inefficiencies, blind spots, and ultimately an inability to effectively manage the employees entrusted with completing those processes. In fact, it’s not uncommon to hear the phrase, “That’s just not how we work,” from business leaders when they look at their process documentation and compare it to their actual workflows.

What to Know About Process Mining

In recent years, the average organization’s ability to generate and collect data has grown exponentially — and so has its potential to analyze and interpret that data in order to discover the actual manner in which processes are executed. This is where process mining — or the data-driven improvement of business processes — combined with effective data science techniques can objectively build bridges between current working methods, process improvements, and model-based visualizations on how to attain greater efficiency, compliance, and cost-effective results.

It’s important to understand that process mining and data science can help businesses improve — whether they have established processes or not. For instance, by collecting data from event logs and other online management systems, companies can employ process mining to perform Automated Business Process Discovery — or ABPD — to determine and document what its processes actually look like.  And, the systems and tools that are used in the work environment universally generate event logs about how the systems and tools are being used.

Once a company has its processes documented, it can use process mining to check on an existing workflow’s conformity to an ideal process. As such, this particular application of data science is highly useful when companies are preparing for regulatory audits, ISO standardizations, and related compliance matters.

Of course, in one of its most beneficial applications, process mining allows data scientists to suggest tangible ways to improve performance, eliminate variations, and reduce costly rework. This use is especially effective when combined with the implementation of process methodologies such as Lean Six Sigma.

Important Tips for Process Mining and Data Science

The following process mining tips are essential to keep in mind when applying the principles of data science to the collection, organization, and analysis of a company’s data:

• Spend sufficient time to clean up data: Whether it’s taking the time to create data sets grouped by individual invoice numbers or collating data from multiple sources to match preselected variables, spend as much time as needed to sufficiently collect and clean data so that you can clearly understand and explain any variations — as well as the lessons learned.
• Work like a scientist: To adequately justify the results of process mining, always work as responsibly as a scientist does. Doing so involves creating replicable paper trails for all data-driven actions, results, and reports. Being able to reproduce results to demonstrate to both leadership and workers how the results were derived is important
• Discover the gaps between ideal and real processes: The true nature of how a company works isn’t revealed simply by documentation reviews or interviews. The real process is partly described in documentation and from interviews—but it is also discovered in how systems are used. Once the data is collected, these real workflows need to be compared to the ideal process to ascertain where gaps exist.
• Validate process models against actual workflows: Just like with the Lean Six Sigma concept of Gemba, be sure to validate any potential process model against the organization’s actual operations. The best-discovered process models are ones that build from data but are checked against what can be seen in the organization as well.
• Produce quality visual representations: Volumes of data can contain a plethora of information to experienced data scientists, but they may mean little to the untrained eye. That’s why it’s crucial to put in the extra effort to produce engaging, informative visuals that are easily interpreted by decision makers. Human beings are good at visual pattern recognition—be sure to look at data visualizations, not just data.

In addition to these practical tips, it’s also important to learn the language of the business you’re helping. When executives hear that you accurately recognize the problems and concerns their businesses face, they’ll be far more likely to support the implementation of the improvements your process mining uncovers.

To learn more about the principles of process mining and data science, feel free to contact us today. BTI has been helping businesses increase their efficiency and eliminate waste for well over a decade, and we look forward to helping you too!