Process mining is a technology that discovers, monitors, and improves the actual processes by leveraging historical and real-time data for business processes and operations. According to process mining stats, 93% of businesses state that they are willing to apply process mining within their organizations due to its ability to identify obstacles, diminish unnecessary steps, and provide real insights. However, process mining faces challenges and limitations related to data (e.g. data quality, format, complexity) and features of process mining tools (e.g. root cause analysis, performance analysis).
Despite these limitations, process mining is a valuable technology to increase an enterprise’s understanding of its own processes. We recommend corporate decision makers to adopt the technology but ensure that their teams
- are aware of its shortcomings and fill those gaps with human intelligence or
- adopt more modern process mining tools that incorporate machine learning
In this article, we explore the top 7 limitations that face process mining, and how AI can tackle them.
Improve data quality
PM tools may not inform you about data quality issues but the quality of their output relies on data quality. Most enterprise data can be incomplete, inaccurate, or have confusing timelines. Therefore, PM tools may analyze faulty data and provide inaccurate results.
It is important for data analysts, domain experts, data stewards and others involved in data quality initiatives to clean and prepare the data before implementing process mining. It is recommended that businesses have data quality assurance strategies and incorporation with AI and ML algorithms and data quality tools to constantly improve data quality. Some of the ways the AI and ML can help with data quality is to
- Automate the process of data entry
- Identify and eliminate duplicate records
- Deploy random forest algorithm to classify data
Accurate root cause analysis
Traditional process mining tools identify and portray process-related issues. Yet, they cannot provide granular answers on the root causes of these issues.
However, this problem has been tackled by leveraging machine learning algorithms in process mining. Combined with ML algorithms, diagnostic process mining identifies the root causes of the problems. There are 2 common approaches here:
- Some PM vendors offer software providing detailed process data for business intelligence (BI) tools and data mining/machine learning platforms or separate PM discovery tools identifying root causes
- Some other PM vendors integrate root cause analysis tools into the software to automatically run the analysis
Convert unstructured data into machine-readable formats
Business data can be both structured and unstructured, however, some traditional process mining tools can only process structured data, leaving unstructured data, such as invoices or receipts, out of the investigation process.
This problem can be tackled by integrating OCR, NLP, and machine learning algorithms to convert unstructured data into machine-readable formats in order to include all data sources in the decision-making process.
However, converting unstructured data to machine readable data is an imperfect process and can introduce errors into process mining output. Therefore users need to pay attention in such cases.
Enable faster process mining output generation
Traditional process mining tools used to provide less clarity in analyzing complex processes because they lacked the sophistication to evaluate processes with a large number of variables. For instance, including numerous stakeholders or extensive data in the process used to generate complexity in PM outputs which were hard for humans to understand and take action on.
In addition to the number of tasks or variables added, in some cases, processes are heterogeneous and cross-sectional. For example, in healthcare processes, it becomes difficult to generalize and model processes that include heterogeneity and multi-disciplinary collaboration.
New process mining tools that integrate AI and machine learning algorithms aim to overcome these complexity issues. For instance, leveraging AI and computer vision to capture and discover all process data, vendors can generate process mining output in a matter of days. A similar PM effort using traditional PM software could take months.
Predict future process performance
As the initial process mining tools focus on event data analysis, it monitors and analyzes past performances of processes rather than ongoing processes. As a result, it cannot alert users in cases of deviations or predict the process performance in the future.
However, applications of AI and ML in process mining can help develop predictive and prescriptive process mining models where PM predicts final results and future events in terms of key performance indicators and can notify users of possible shortcomings or areas for improvement.
Identify dependencies or bottlenecks within a process
Process mining does produce results in the shape of visualizations and tables, however, it requires the human analyst to interpret the outcomes and make suggestions to improve processes. Businesses can leverage AI and analytics tools to process results obtained from process mining tools in order to better identify dependencies or bottlenecks within a process.
To understand how AI augments process mining and what are the process mining contributions to your business, check out our in-depth articles:
Check out comprehensive and constantly updated list of process mining case studies to find out real-life examples and understand process mining limitations better.
There are various companies that provide process mining tools and software with a free trials and open source options. You can learn more on these platforms by checking Top 10 Open Source and Free Trial Process Mining Tools.
To gain a broader knowledge of vendors, you can use our data-driven list to identify a shortlist of vendors and save time.
If you believe your business can benefit from process mining, let us help you find the right vendor:
Next to Read
Your email address will not be published. All fields are required.