Description

This curriculum spans the technical, organisational, and governance dimensions of data collection in process redesign, comparable to a multi-phase advisory engagement that integrates data engineering, compliance alignment, and change management across complex business environments.

Module 1: Defining Data Requirements Aligned with Business Objectives

Selecting key performance indicators (KPIs) that directly reflect process efficiency and customer outcomes, such as cycle time or first-contact resolution rate.
Determining which operational stages require quantitative versus qualitative data based on redesign goals, such as automation feasibility or customer satisfaction.
Mapping data needs to stakeholder decision rights, ensuring process owners receive granular data while executives get aggregated insights.
Identifying legacy system constraints that limit data availability, such as batch-only exports or lack of timestamp granularity.
Deciding whether to collect data at event-level or summary-level based on downstream analysis requirements and storage costs.
Establishing thresholds for data completeness and accuracy acceptable for redesign modeling, such as minimum 90% form completion rates.
Documenting data lineage requirements early to support auditability in regulated industries like healthcare or finance.
Resolving conflicts between IT data standards and business unit data collection practices during cross-functional process mapping.

Module 2: Selecting and Integrating Data Collection Tools

Choosing between embedded system logging, third-party process mining tools, or custom instrumentation based on system access and budget.
Configuring API rate limits and authentication protocols when pulling real-time data from CRM, ERP, or ticketing systems.
Implementing middleware to normalize timestamps and user identifiers across disparate systems with inconsistent logging formats.
Deciding whether to use agent-based monitoring or passive network sniffing for capturing user interaction data in desktop applications.
Validating data integrity after ETL processes, particularly when merging structured and unstructured data sources.
Assessing scalability of collection tools under peak transaction loads to prevent data loss during high-volume periods.
Configuring fallback mechanisms, such as local queuing, when upstream data destinations are temporarily unavailable.
Integrating optical character recognition (OCR) pipelines for digitizing paper-based forms still in use during transition phases.

Module 3: Designing Ethical and Compliant Data Flows

Conducting data protection impact assessments (DPIAs) for processes involving personal or sensitive employee data.
Implementing role-based access controls (RBAC) on collected data to align with principle of least privilege.
Masking or pseudonymizing personally identifiable information (PII) in logs used for process analysis.
Establishing data retention schedules that comply with legal requirements while supporting longitudinal analysis.
Documenting lawful basis for processing under GDPR or CCPA when collecting behavioral data from employees.
Obtaining informed consent for observational data collection in manual or hybrid workflows.
Creating audit trails for data access and modification to support accountability in regulated audits.
Coordinating with legal and compliance teams to classify data as operational, personal, or confidential.

Module 4: Capturing As-Is Process Data with Minimal Disruption

Deploying non-intrusive monitoring tools to avoid altering user behavior during baseline data collection.
Calibrating sampling rates for high-frequency processes to balance data volume and representativeness.
Identifying shadow IT tools or spreadsheets used in practice and incorporating them into data collection scope.
Resolving discrepancies between documented workflows and actual system usage patterns observed in logs.
Synchronizing data collection start times across departments to enable cross-functional process analysis.
Handling missing or incomplete records due to system outages or manual bypasses during data aggregation.
Validating timestamp accuracy across time zones and systems to reconstruct correct event sequences.
Training supervisors to log exceptions manually when automated capture is not feasible.

Module 5: Ensuring Data Quality and Consistency

Implementing automated validation rules to flag outliers, such as processing times exceeding three standard deviations.
Standardizing naming conventions for process stages across departments to enable aggregation.
Resolving mismatches in user identity resolution when employees use multiple system accounts.
Creating reconciliation routines to align data from parallel systems tracking the same process.
Establishing data stewardship roles to review and correct anomalies in weekly data quality reports.
Defining acceptable error margins for manual data entry fields used in hybrid processes.
Using referential integrity checks to detect orphaned records in multi-system workflows.
Developing dashboards to monitor data completeness, timeliness, and consistency in real time.

Module 6: Managing Stakeholder Access and Feedback Loops

Configuring tiered dashboards that expose only relevant data to process participants, managers, and executives.
Setting up automated alerts for process deviations that trigger review by designated owners.
Facilitating feedback sessions where frontline staff validate observed patterns against lived experience.
Documenting and resolving discrepancies between system data and employee-reported bottlenecks.
Implementing version control for data definitions to track changes in metric calculations over time.
Establishing SLAs for data refresh frequency based on stakeholder decision cycles.
Restricting ad hoc query access to prevent inconsistent interpretations of raw data.
Creating standardized report templates to ensure consistent communication of findings.

Module 7: Preparing Data for Process Simulation and Modeling

Aggregating event logs into case-level records with start, end, and milestone timestamps.
Imputing missing transition times using domain-informed heuristics, such as median handling duration.
Classifying rework loops and parallel paths from sequence patterns in event data.
Discretizing continuous variables, such as processing duration, into categories for decision tree modeling.
Generating synthetic data to model edge cases not present in historical logs.
Validating model assumptions against observed variance in throughput and resource utilization.
Aligning data granularity with simulation engine requirements, such as discrete-event versus agent-based models.
Tagging data records with scenario flags to support comparative analysis of redesign options.

Module 8: Transitioning from Collection to Redesign Implementation

Freezing baseline datasets before process changes to enable before-and-after comparisons.
Configuring parallel data streams to capture both legacy and redesigned process variants.
Updating metadata documentation to reflect changes in data sources post-redesign.
Revising data collection logic to align with new process steps, roles, or systems.
Decommissioning obsolete data pipelines and archiving legacy datasets according to retention policy.
Validating that new system logs capture all required redesign KPIs from day one.
Establishing ongoing monitoring to detect unintended consequences, such as new bottlenecks or compliance gaps.
Transferring stewardship of data assets to operational teams responsible for sustained performance tracking.