Change Mining

Business processes are not static. System implementations, organizational changes, new regulations, shifts in customer behavior and evolving workforce practices all cause processes to change over time — often gradually and invisibly. A process that was compliant, efficient and well-controlled two years ago may have drifted significantly, even without any formal process redesign.

Change mining is the discipline of detecting, measuring and understanding these shifts. By comparing process mining analyses across time periods, it reveals how process behavior has evolved — which variants have emerged or disappeared, which controls have become less effective, which performance characteristics have improved or deteriorated, and where process drift is creating risk.

Why Processes Drift

Process drift — the gradual divergence of actual process behavior from the intended or designed process — occurs for several reasons:

• Workarounds that become habits: A workaround created to handle an exceptional situation becomes the default approach for a growing class of cases, without any formal decision having been made.
• Staff turnover: When experienced employees leave, their successors may execute processes differently — not incorrectly, necessarily, but inconsistently with the established approach.
• System changes: ERP updates, configuration changes or new integrations can alter the available process paths — sometimes in ways that were not intended by the people who commissioned the change.
• Volume and mix changes: A process designed for a certain transaction volume and customer mix may behave differently at higher volumes or with different customer types, even without any explicit change to the process design.
• Control erosion: Controls that require human action — approval confirmations, review sign-offs, mandatory field entries — tend to become less rigorously applied over time, particularly as familiarity breeds complacency.

Types of Process Change Detected by Change Mining

Variant distribution shifts

The proportion of cases following different process paths changes. A variant that represented 5% of cases last year now represents 25%. This shift may be entirely benign — a new product type with a different process path — or it may indicate problematic drift toward a less controlled or less efficient path.

Performance characteristic changes

Cycle times, wait times, exception frequencies and rework rates change between periods. Change mining distinguishes between improvement (the intended result of an optimization initiative), degradation (unintended deterioration) and seasonal or structural variation that is expected and normal.

Control compliance changes

The frequency with which controls are bypassed or deviated from changes between periods. An increase in bypassed approval steps or three-way match exceptions signals control erosion that may not yet have appeared in a formal audit finding.

Activity sequence changes

The order in which activities are performed shifts. Activities that were previously sequential become parallel, or vice versa. New activities appear that were not part of the original process; established activities disappear. These structural changes may reflect legitimate process improvement or unauthorized process modifications.

Change Mining in Practice: Key Scenarios

Post-implementation validation

After an ERP upgrade, configuration change or new module deployment, change mining compares the pre- and post-implementation process behavior to verify that the changes performed as intended and did not introduce unintended process modifications.

Merger and acquisition integration

Following an acquisition, change mining compares the process behavior of the acquiring and acquired organizations — revealing differences in process execution, compliance levels and performance characteristics that need to be resolved during integration.

Regulatory change assessment

When new regulations take effect — NIS2, an updated ISO standard, a new tax reporting requirement — change mining verifies that process behavior has adapted as required, and identifies areas where the regulatory change has not yet been fully absorbed into operational practice.

Continuous process governance

Organizations with mature process governance programs run periodic change mining analyses — quarterly or semi-annually — as a routine check on process health. This prevents the accumulation of undetected drift that would otherwise require a more expensive correction later.

Change Mining and Continuous Process Improvement

Change mining closes the improvement loop. A process improvement initiative makes changes; change mining measures whether the intended changes actually materialized in process behavior, whether they have been sustained over subsequent periods, and whether any unintended consequences have emerged.

Without this measurement, improvement programs rely on assumption and anecdote. With it, organizations can make evidence-based decisions about whether a change was successful, whether it needs reinforcement, and where to focus the next improvement cycle.

For Business Central users, change mining draws on the same event log data as standard process mining — comparing analyses across defined time windows using the transaction history already present in the ERP. This makes it a natural extension of any existing process mining capability, rather than a separate data collection effort.

Related Concepts

• Process Mining
• Conformance Checking
• Process Monitoring
• Process Variants
• Process Governance
• Process Improvement
• Audit Trail