Most businesses have some methods in place to analyze and improve business processes. Increasingly organizations are adapting advanced data science techniques such as process mining to gain a competitive edge.
What is process mining and why should companies use it? This will all be revealed in Process Mining 101.👇
What is process mining?
Process mining is a technique used to discover, analyze and improve business processes using data mining methods. Process mining is seen as an objective and data-driven way to evaluate the real state of processes and workflows in an organization.
Process mining can be seen as the intersection of business process management and data science. Process mining tools interact with business software systems to extract process information, typically in the form of event data or business objects, for the purpose of process discovery and business process improvement.
Process mining gives visibility to end-to-end processes, thus providing companies with valuable data that can be used to improve actual business processes and reach better company performance. In this way, it is also talked about as an x-ray to key workflows and business processes.
Why mine business processes?
Business process analysis is not a new concept. In the past analysis in business processes could be based on time and motion surveys, process mapping or process discovery interviews of key personnel. The result of the manual analysis may then have been summarized in business intelligence initiatives, as part of an audit findings or seen as part of a process map visualization.
You can see process analysis as a way to assess the "as-is" state of business processes and a way to design and model the "should-be" state of processes. While many companies have a perfectly designed process model and standard operating procedures (SOPs) the reality of business processes often don't match these expectations.
How process mining fits in business process management
While process analysis has become a proven and established part of effective business process management, process mining resolved some of the key limitations of manual analysis:
- manual process analysis rely on subjective views of the interviewees or survey analysts,
- they need significant time, cost and team resources needed for conducting analysis,
- you only get a one-time snapshot of processes - instead of continuous results you can track and improve.
In summary, process mining provides an automated process discovery solution that gives businesses a data-driven way to manage process improvement initiatives.
Is process mining just automated process discovery?
It would be an oversimplification to say process mining is just an automated process discovery solution. In reality, the solution gives a data-driven way to continuously improve business processes and can be used flexibly to not just discover processes, but also model and forecast impacts of process improvements.
There are also other automated process discovery solutions to consider that don't require data mining of event data. For example, task mining solutions can be used effectively to discover as-is processes from user interface activities.
Did you know you can combine task mining and process mining? Read the Work API whitepaper!
Why is process mining so popular?
Process mining helps uncover inefficiencies in business operations, bottlenecks in processed and opportunities for automation more systematically and at greater scale than interview-based process analysis. What's more, process mining is faster and typically more reliable in managing large amounts of data and as a result is more accurate in the insights provided.
Today, process mining is one of the most active areas of enterprise software development. In a recent survey of business executives in Global 5000 enterprise businesses, HFS Research found that process mining and discovery was the #1 priority emerging new technology for investment. According to Deloitte, 63% of enterprise use or plan to adopt process mining software in the near future.
How exactly does process mining work?
Process mining can be viewed as a four step method from data extraction to data analysis.
- Extract data - a process mining tool is used to extract process data from IT systems.
- Reconstruct data - process data is collected and harmonized in preparation for analysis.
- Visualize data - process mining algorimths are used to display as-is state of processes.
- Analyze data - process data is analyzed to identify process improvement opportunities.
Key process mining use cases
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.
- Process discovery. This is the most typical and more generic use case for process mining is process discovery - where the goal is to identify the "as-is" state of business processes.
- Root-cause analysis. Another key use case is simply quantifying and identifying the root cause of key process challenges by using real process data.
- Conformance checking. Process mining allows you to monitor and continously track processes alignment to SOPs and process models through conformance checking.
- Opportunity identification. Process mining uncovers opportunities to streamline or automate processes, for example, with robotic process automation (RPA).
- Process optimization. In process optimization, the end-state of effective process mining is that processes are continously monitored and improved.
Each of the above use case examples can be applied to a team, business unit or the entire organization.
Some common use-cases for process mining in enterprise business include:
- Finance - improving working capital, increasing productivity of the finance team, or ensuring compliance within financial operations.
- Procurement - simplifying approval processes, reducing maverick buying, or identifying errors in supplier performance.
- Customer service - reducing the lead time of customer response, identifying root-causes of issues, improving customer satistaction.
- Logistics - ensuring reliability of supply, optimizing inventory and improving productivity within logistics and transportation.
- Human relations - including recruitment, employee on-boarding and payroll.
Benefits of process mining
There are a number of obvious benefits for mining process data. The seven top advantages include:
- Reduce costs. Automatic process discovery finds the most manual, repetitive, and generally inefficient processes, which are usually of the highest cost for different units.
- Reduce lead time. For most businesses time is money - and a key benefit of process mining is the reduction of lead or throughput time in key processes.
- Improve quality. Process mining can help uncover faults, delays or errors that reduce the quality of goods or service.
- Improve customer satisfaction. The quality of customer service depends directly on the efficiency of the client-facing processes.
- Enable process automation. Coupled with automation tools like RPA, process mining helps to optimize previously inefficient processes.
- Make data-backed decisions. Process mining fosters data-driven decision-making, closing the data gaps within raw data and analyzing recreated processes.
- Increase transparency. 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.
Read a case-study how one medium-sized accounting firm uncovered over € 2 million ($2.17 million) in process waste.
Choosing the right process mining software
There are over 35 different process mining solutions available in the market catering for different types of organizations and clients. No matter what your specific needs, it’s good to keep in mind some key areas to consider:
- Ease of use - process mining doesn’t need to be difficult to use. Seek screenshots or a product demonstration to see if it looks easy to use or navigate.
- Configurability - some solutions only cater for specific use-cases or require significant effort to configure or integrate with different source systems.
- Analytics and reporting - if you’re looking for an enterprise-grade solution you’re likely to need advanced and trustworthy analytics and reporting capabilities.
- End-to-end visibility (E2E) - as more work is digitalized, you need full E2E visibility of the full process journey across different tools and systems.
- Level of support - whether you are a first time process analyst or an experienced process architect you’ll likely have different needs for support and on-boarding.
- Total cost of ownership - process mining solutions come in a variety of costs and service levels. It’s smart to consider the total cost of ownership including the implementation cost as well as the cost of running on-going process analytics.
💡 Looking for a software solution? For more information see our deep dive on 15 top process mining tools.
Common limitations of process mining tools
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.
According to recent research by HFS Research, 91% of enterprise leaders felt process intelligence technology was hugely significant in driving business value but at the same time 2 in 3 respondents felt process intelligence technology such as process mining is too complex to use effectively in their organization.
Analysis of latest but not real-time data
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. In this way, the mining process never gives fully real-time data.
High initial costs
Often the rollout of process mining solutions require 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. In many cases integrations have to be created and maintained to all enterprise resource systems and the costs quickly add up.
Heavy reliance on human analysts
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:
- Interpretation of the data: once analyzed, insights alone are not sufficient, and a business analyst is needed to interpret that data and find concrete use cases in line with the initial objectives.
- Data cleansing and extraction: data from event logs might be incomplete, incorrect, or duplicated, and data analysts need to take time and clean the data to prepare for further use.
Long time-to-value
Depending on their complexity, different systems take a lot of “preparation” time before they can even start providing event logs. Because of the heavy data integration needs of most process mining solutions, it is not uncommon for implementations to take between 12-18 months.
Related articles and research
Looking to expand your knowledge in process mining? Find related articles and research below.
-> Process mining algorithms simply explained
-> 6 top benefits of process mining
-> Process mining vs task mining
-> Process mining vs data mining
-> Process mining vs process intelligence
-> Process mining software comparison
