Do you need Process Mining?
- Are your business processes running on multiple systems? Are you able to monitor the end-to-end process?
- Do your business processes take too long to execute due to high levels of manual intervention? Does this make it difficult to scale due to spikes in customer demand?
- Do you have a clear understanding of the business processes that are currently under your automation initiatives, how are these prioritised?
- Do you know:
- What the unexpected behaviours on your business processes are?
- Which are the repetitive low-value tasks in your business processes?
- How do your remote teams capture and consolidate operational data for analysis, and collaborate on the design and documentation of business processes?
- When you implement improvements to your business processes, are you able to measure the expected ROI?
Process Mining addresses key business challenges around understanding what a business process is, and identifying bottlenecks inefficiencies, deviations and variations in the process, as well as governance and conformance exposures.
The traditional approach of manually interviewing business process actors and owners is inefficient and prone to inaccuracies. Often what is thought to be the process is not reality. On average 70% of the resources are spent on pre-automation identification and prioritising of tasks for automation and typically, only 3% of automation projects scale up.
To address these inefficiencies, Process Mining applies data science to discover, validate and improve workflows. By combining data mining and process analytics, organisations can mine log data from their information systems to understand the performance of their processes, revealing bottlenecks and other areas of improvement. Process Mining leverages a data-driven approach to process optimisation, allowing managers to remain objective in their decision-making around resource allocation for existing processes.
When compared to Business Process Management (BPM), Process Mining takes a more data-driven approach to BPM, which has historically been managed more manually. BPM generally collects data more informally through workshops and interviews, and then uses software to document that workflow as a process map. Since the data that informs these process maps is more qualitative, process mining brings a more quantitative approach to a process problem, detailing the actual process through event data, providing data driven insights powered by AI.
Wil van der Aalst, a Dutch computer scientist and professor, is credited with much of the academic research around process mining. His research describes three types of process mining:
Process discovery uses event log data to create a process model without outside influence. Under this classification, no previous process models would exist to inform the development of a new process model. This type of process mining is the most widely adopted.
Conformance checking confirms if the intended process model is reflected in practice. This type of process mining compares a process description to an existing process model based on its event log data, identifying any deviations from the intended model.
This type of process mining has also been referred to as extension, organisational mining, or performance mining. In this class of process mining, additional information is used to improve an existing process model. For example, the output of conformance checking can assist in identifying bottlenecks within a process model, allowing managers to optimise an existing process.
In any complex environment, there will always be a set of stop-gap fixes, workarounds and process hacks buried in the minds of long-term employees, which the BPM approach of interviews and document mining will not discover, as well as best practices and recommendations that have not been adopted. IBM Process Mining includes Task mining capabilities, allowing the discovery and exploration of these workarounds and common practices.
Process mining, by accessing the actual events as captured in the detailed event logs, builds a detailed process map of how the business actually operates. This allows the creation of a digital twin of the organisation and provides the opportunity to understand not only the process optimisation opportunities, but also task automation options, compliance issues, and opportunities to enhance processes, services or deliverables. The digital twin then allows for the monitoring of the processes, identifying hidden bottlenecks, areas where there are gaps between the process model and the reference model, and unearthing the root cause of these deviations and inefficiencies. Fact-based compliance checks and monitoring allow businesses to ensure that they are up to date with the latest regulatory requirements.
The third benefit to having this digital twin in place is that the organisation can run simulations, modelling proposed changes to their environments and processes, and experiment with different paths forward without committing time and money, or endangering their service delivery.
One of the areas of business that would benefit from the application of Process mapping would be IT Operations, where all three types of process mining can be applied to help organisations address the cost, compliance and efficiency challenges presented by the modern hybrid IT environment.
Starting points could be IT-focused project performance, optimise spend and simplify operations facets, but could equally contribute to improving incident management, enterprise observability, improving CI/CD pipelines and developer experience, eliminating tool and process sprawl, or to creating sustainable IT.
When looking at IT Incident management, we typically see profiles like:
- 5% of the service desk costs are staffing
- 74% of tickets on average are 1st-level resolution
- 69% of support incidents are resolved in one touch, (many are password resets)
Creating massive opportunities for automation driving cost saving and improving user satisfaction ratings
However, IBM Process mining allows you to go further, with its ability to execute multi-level or multi-object process mining, allowing a global analysis of many to many processes, for example, Procure to Pay, Order to Cash, or Customer onboarding / unboarding, providing a unified picture of synergies across countries, departments and functions, allowing the organisation can see how changes in one area might ripple across the organisation. Once modelled, the IBM Automation portfolio includes the toolsets (IVA /Bots, RPA, decision making, document processing, workflow) to execute and automate the candidate tasks. 78% of organisations who automate say that process mining is key to enabling their RPA efforts.
Read the Orb Data blog on Integrating IBM Process Mining with Jira