Whether you’ve heard of process mining or seen it here and there but still wonder what it is, you’ve come to the right place. We'll explain the basics, the use cases, but most importantly: what are the next steps after mining.
Process mining is a way to see how business processes are carried out inside the organization. It is a set of techniques that gathers the data from event logs, analyses it, and identifies the inefficiencies and bottlenecks within information systems.
Process mining gives visibility to end-to-end processes, thus providing companies with valuable data that can be used to improve business operations and reach better company performance.
If this sounds a bit confusing, don’t you worry, this article will break down each of the terms.
Process mining emerged based on a clear need to have a comprehensive tool that would analyze the right data and help a company through its automation journey.
Many different tools and techniques have long been present to attempt it but they all had limitations of some sort. For example, Data Mining used Big Data to identify patterns and insights but was limited to only the structured data, while Business Process Management (BPM) as a larger concept involved a lot of manual processes. Process mining, therefore, evolved to fill those obvious shortcomings.
Process Mining does not actually “automate” things by itself; there are many emerging technologies like Robotic Process Automation (RPA), AI, low-code/no-code that can help with that. What process mining does though is that it finds and pinpoints that automation potential and gives the concrete route for companies to follow.
Process mining helps with digital transformation by being the first touchpoint for the companies in their process automation journey. It is essential to have a clear picture of all the business processes and see them as it is to know which use cases need to be prioritized.
To successfully optimize the processes they need to be repetitive and relatively scalable, otherwise, it wouldn’t be worth the effort (read more on this in our blog).
Process mining gets the data from, you guessed it right, processes. In this context, a process is a chain of individual events with a clear starting and ending point.
That being said, process mining is not a single-step project and the following stages show how it works in a simplified way.
Process mining cannot work with the raw unorganized data that processes are initially made of. Therefore, it should be transferred into so-called event logs.
Event logs record, gather and store the events from different information systems such as ERP and CRM that the company has in use. As a minimum requirement for mining a single performed activity or process, event logs need to have a distinct case ID, timestamp, and know what the performed activity is.
Data from event logs are then cleaned and prepared by analysts for further analysis in case there is incorrect, inaccurate, or missing data.
Once the necessary data is collected in a nice neat way and freed from false data of any kind, it is ready for the actual “mining”. This is when the process model is reconstructed presenting a graphical view of the process.
In this step, freshly represented processes can be analyzed for various purposes. Depending on the goal, you can identify inefficiencies and deviations from the normal processes, find previously unknown processes, and analyze potential risks and bottlenecks.
Step 4. Monitor performance continuously
Once you’ve done the changes (e.g. implemented an RPA bot) based on the findings from the previous step, it is important to get an understanding if things are continuing to work properly. Some of the process mining solutions are able to monitor the changes and measure the results.
We have so far looked at the whats and hows of process mining, which helped to understand why process mining is necessary for the automation journey. If that wasn’t convincing enough, here are more concrete answers for the question “why process mining”.
Automatic process discovery finds the most manual, repetitive, and generally inefficient processes, which are usually of the highest cost for different units.
The quality of customer service depends directly on the efficiency of the client-facing processes. If the process is smooth and free of bottlenecks, then most probably customers will be more satisfied.
Coupled with automation tools like RPA, process mining helps to optimize previously inefficient processes.
Process mining tools foster data-driven decision-making, closing the data gaps within raw data and analyzing recreated processes.
Most of the decisions in big companies are based on experienced senior-level people and their expertise and gut feeling. With process mining, it’s possible to see how the processes are running de-facto.
To sum it up, process mining helps organizations optimize their processes, which eventually leads to a better customer satisfaction, reduces costs, and improves job satisfaction.
Process mining is a large initiative that can bring enormous benefits if and when used properly. That being said, it also requires quite a level of effort from organizations adopting it. A typical process mining project requires at least the following roles to be involved in it:
Process owner. The person who is responsible for the strategic side of the project. Process owner sets the goals, defines the objectives, and leads the process, providing the team with any help and support.
Database expert. The primary person who derives the data from multiple information systems, checks the quality of the data and hands it over for further analysis. It’s usually someone from the IT or analytics team or both, and he/she can truly be called a “data guru”.
Process mining experts. It’s usually a small team of specialists, where one carries out the more technical part of actual analysis and the other one evaluates the process and finds automation opportunities based on the data insights in line with the objectives initially set by the process owner.
However, in larger organizations the team might comprise more people, thus making their roles even more narrow and specific.
So far we’ve discussed whats, hows, and now whys of process mining. We can now look at a few more concrete examples of how process mining could bring value to different units/roles within an organization.
This is the most typical and more generic use case for process mining. Through an analysis of process data, companies can easily find more apparent repetitive and inefficient processes and work on improving those.
Data insights from information systems allow a better and more effective audit of the processes, instead of having to conduct countless interviews, surveys, and analyze hundreds of documents.
There’s a lot of automation potential in purchase-to-pay processes that would result in fewer errors, reduced costs, and better processes. This is possible through the visibility that process mining provides to the procurement team.
Although process mining can do wonders for organizations and has been proving that for many years, there are still areas where process mining falls short.
Process mining analyzes the latest data extracted from information systems but that still doesn’t guarantee the full picture of a company's current performance. The data from event logs are first derived at a point in time, cleaned, and only then are they analyzed, resulting in more “static” offline analytics. As an outcome, traditional process mining tools aren't able to notify about possible process deviations on an ongoing basis.
The rollout of process mining tools requires a lot of effort and inputs from multiple units and teams that result in very high costs. As an example, an IT team would need to do some upfront development and software integrations before the software can start running.
Although process mining ultimately aims to facilitate automation of the processes, it is still very much reliant on the human brain and the work of business analysts, data analysts, and IT people. Two big areas where people are absolutely necessary for process mining are:
Depending on their complexity, different systems take a lot of “preparation” time before they can even start providing event logs. Because it’s a necessary step in process mining, it ends up affecting the whole time-to-value of the projects.
Work intelligence tools like Workfellow address aforesaid limitations and provide a more complete comprehensive analysis of work.
Workfellow analyses real-time data revealing the current performance of an organization and alerting in case of any deviations on an ongoing basis. It covers all business work-related applications, systems, and documents, leaving no space for undocumented “shadow” work that might not be covered by event logs.
Work intelligence platform starts running and collecting data right away, without having to do multiple integrations and occupying IT peoples’ time for the data extraction.
Workfellow platform collects the data independently by observing all work-related activities within teams. This is the data that reveals the reality of work so no data scrubbing/cleansing is needed.
Once data insights are ready, the platform automatically analyses them and provides suggestions for further actions, taking up the role of a business analyst and data analyst.
Workfellow needs only 2 weeks before it starts generating business cases and data-backed solution recommendations for them.
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