Process Mining: Event log suitability for automation

Key criteria for event log suitability in Process Mining

For successful application of Process Mining before automation, event logs must meet minimal data structure requirements. According to recommendations from the IEEE Task Force on Process Mining, every event log must minimally contain three mandatory elements: a unique Case ID, an Activity name, and a Timestamp processmining.org. These elements form the foundation for reconstructing the actual process flow and identifying deviations. Without this basic data, Process Mining tools like Celonis or SAP Signavio cannot build reliable process models, making it impossible to make informed automation decisions celonis.com, signavio.com.

Assessing data completeness and quality: Case IDs and activity attributes

A unique Case ID is critically important for grouping related events and distinguishing different process instances processmining.org. Its absence or incorrectness can distort process models, leading to incorrect conclusions about duration, variations, and bottlenecks. For example, if a system generates different Case IDs for the same order at different stages, Process Mining will interpret these as separate processes, resulting in a fragmented and inaccurate view of the actual flow. Incomplete or poor-quality data in event logs can lead to flawed process models, inaccurate predictions, and ineffective automation decisions.

In addition to mandatory fields, event logs can contain supplementary attributes such as resource, cost, or outcome to enrich analysis processmining.org. These attributes allow for deeper analysis of deviation causes, identification of performers impacting performance, and evaluation of the financial implications of different process variants. The absence of such attributes limits the depth of analysis and can lead to missed opportunities for optimization and automation.

Timestamp accuracy and its impact on event sequence

The accuracy and consistency of timestamps are fundamental for determining the order of events, identifying bottlenecks, and correctly reconstructing the actual process flow processmining.org. Timestamps allow for calculation of activity durations and transitions between them, which is key for identifying delays and inefficient stages. Inaccurate or missing timestamps can lead to incorrect event sequencing, distorting the process model and rendering optimization conclusions invalid.

Timestamp granularity also matters. For some processes, minute-level accuracy is sufficient, while for others, such as in manufacturing or financial transactions, millisecond accuracy is required. Insufficient granularity can hide short-term but critical delays or parallel activities that are important for accurate modeling and subsequent automation. Operational consequences of inaccurate time data include false automation priorities, investments in optimizing non-existent bottlenecks, and missed real opportunities for efficiency improvement.

Data readiness for automation decision-making

The quality of data in event logs directly impacts the reliability of analytical conclusions and the success and ROI of automation investments celonis.com, signavio.com. Incomplete or poor-quality data in event logs can lead to flawed process models, inaccurate predictions, and ineffective automation decisions. This creates the risk of investing in automating processes that are not actually bottlenecks, or conversely, ignoring critical areas that require intervention.

Thresholds for “sufficient” data quality are not universal, but a general rule is that data must be sufficiently complete and accurate to provide a representative picture of the process. If Process Mining reveals significant discrepancies between expected and actual process behavior due to data quality, this is a signal for additional data preparation, not immediate automation. Data readiness for decision-making means that Process Mining analysis results are reliable and can be used to justify investments in RPA, BPM, or other forms of automation.

Strategies for improving and preparing event logs

Data preparation for Process Mining often requires identifying relevant events, combining them from various source systems (e.g., CRM, ERP), and adding business context. This may include developing ETL pipelines to collect, clean, transform, and load data into a format suitable for Process Mining, such as XES (eXtensible Event Stream), which is an official IEEE standard processmining.org. Standardizing data collection at the source system level is ideal but often requires significant architectural changes.

Practical approaches include:

• Identifying data sources: Determining all systems that generate events relevant to the analyzed process.
• Defining Case ID: Creating or extracting a unique case identifier that allows tracking the full process path. This may require combining data from different systems using a common key.
• Normalizing activities: Standardizing activity names to avoid duplication or inaccuracies.
• Data cleansing: Removing anomalies, duplicates, and incorrect records.
• Data enrichment: Adding additional attributes (e.g., department, performer role, customer type) for deeper analysis.
• Validation and monitoring: Continuously checking data quality and monitoring ETL processes to ensure the timeliness and reliability of event logs.

Practical selection matrix for evaluating event logs

This checklist will help assess the suitability of your event logs for Process Mining before automation. For each criterion, evaluate the current data state and determine necessary actions. The higher the score, the better the event log is prepared.

Criterion	Score (0-3)	Explanation / Consequences	Required actions
Presence of unique Case ID	0: absent, 1: incomplete/inconsistent, 2: present but needs cleansing, 3: present and correct	Critical for grouping events. Absence distorts the process model.	Develop a Case ID identification strategy, combine sources.
Presence of Activity Name	0: absent, 1: unclear/unstandardized, 2: present but needs normalization, 3: present and clear	Necessary for defining process steps. Unclarity complicates analysis.	Standardize activity names, create mapping.
Presence of Timestamp (start/end)	0: absent, 1: only start or end, 2: present but not always complete, 3: present (start and end)	Fundamental for event sequencing and duration calculation.	Ensure both timestamps are recorded for each activity.
Timestamp granularity (seconds/milliseconds)	0: date only, 1: hours/minutes, 2: seconds, 3: milliseconds	Impacts accuracy of bottleneck detection and parallel activities.	Check source system capabilities, configure collection.
Data completeness (percentage of missing values)	0: >20% missing, 1: 10-20% missing, 2: 5-10% missing, 3: <5% missing	High percentage of omissions reduces analysis reliability.	Identify causes of omissions, implement filling mechanisms.
Uniqueness of Activity Name	0: many duplicates, 1: some duplicates, 2: minimal duplicates, 3: unique	Duplicates complicate building a clear process model.	Normalize activity names, create a dictionary.
Presence of additional attributes (resource, cost, outcome)	0: absent, 1: some but incomplete, 2: most but need cleansing, 3: present and correct	Enriches analysis, allows identifying deviation causes and optimizing costs.	Define necessary attributes, integrate with sources.
Data accessibility (format, API)	0: manual export, 1: unstructured files, 2: structured files (CSV, XML), 3: API or direct DB access	Impacts ease and speed of data integration.	Develop automated export/access mechanisms.

How to apply: For each criterion, assign a score from 0 to 3, where 3 indicates an ideal state and 0 indicates complete absence or unsuitability. The total score will provide an overview of the overall readiness of event logs. Criteria with low scores (0-1) require immediate attention and an action plan for improvement. Use the “Required actions” column to formulate specific tasks for your team. This tool will help you make an architectural decision on whether your data is ready for effective Process Mining, or if significant investments in data preparation are needed before starting automation.

DMIG offers expertise in building ETL pipelines and data architectures, which is critically important for preparing high-quality event logs that meet Process Mining requirements for successful business process automation. Additional information can be found at Intecracy solutions and inbase.com.ua solutions.

Thorough evaluation and preparation of event logs is not just a technical task, but a strategic step that determines the success and ROI of any automation initiative. Underestimating this stage can lead to significant financial and operational losses.