What is the process of master cleansing?
Master data cleansing is the process of identifying and correcting inaccurate, incomplete, duplicated, or outdated master data within an organization’s systems. Maintaining clean master data is crucial for reducing operational costs, improving customer experiences, and enabling data-driven decision making. Here we will explore the typical steps involved in conducting a thorough master data cleansing initiative.
Assess Current State of Master Data
The first step is to assess the current state of your master data. This involves identifying where key master data resides, examining the structure and quality of data fields, and estimating the level of duplication, invalid entries, and out-of-date information within each data set. Auditing a sample of records per data set can uncover data quality issues. Documenting the extent of quality problems allows you to build a business case for initiating a full-scale master data cleansing project.
Define Governance Model
Next, define the governance model for an ongoing master data cleansing program. This involves assigning oversight responsibilities, documenting processes for continuous data maintenance, and integrating data quality KPIs into business metrics. A steering committee of data stakeholders can set policies, schedules, tools, metrics, and thresholds for acceptable data quality. Documenting the program helps uphold consistency as personnel and systems change over time.
Select Cleansing Approach
There are several approaches to cleansing master data on both a one-time and ongoing basis:
- Bulk updates via data transformation scripts
- Inline updates during transactions
- Real-time web services that validate against master data
- Data quality tools with cleansing functions
- Outsourced data cleansing services
Select the approaches that align to the skills of your team and requirements of your systems. A blended strategy often works best. For example, using scripts for large-scale one-time cleansing paired with real-time validation for ongoing maintenance.
Define Data Cleansing Rules
Rules must be defined to standardize, match, merge, and correct records. For example:
- Standardizing ‘St’ and ‘Street’ to ‘St’
- Merging records with identical account numbers
- Validating email addresses against format standards
- Flagging duplicate phone numbers
Subject matter experts should help define appropriate logic and values for their domain. Technical resources can assist with designing effective matching algorithms and data transformations.
Select Cleansing Tools
Specialized data quality and data integration tools can automate the process of standardizing, matching, merging, and editing master data based on predefined rules. Leading options include:
| Tool | Description |
|---|---|
| Informatica | End-to-end platform for data integration and quality |
| Talend | Open source tool for data integration with data quality features |
| MelissaData | Address verification and global data quality |
| SAP Data Services | Data integration and cleansing tool |
The needs, budget, and technical expertise of your team will determine the ideal cleansing tool(s) to employ.
Integrate with MDM Platform
For ongoing maintenance, integrating data cleansing capabilities with your master data management (MDM) system can provide automation. MDM hubs can invoke data quality services to validate and correct records in batch and real-time modes. This helps sustain clean master data after initial cleansing efforts.
Cleanse Data
With rules defined and tools in place, execute the data cleansing process across impacted systems. This involves standardizing formats, removing duplicates, merging records, validating entries, and enriching data. The scale of this effort depends on the level of initial master data quality issues.
Be sure to preserve original data via backups or data histories. Also, track data changes through logging and audit trails. This allows tracing back to root causes of data issues and monitors the impact of changes.
Verify Data Quality
Once cleansing is complete, thorough validation checks must be conducted to verify desired improvements to data quality. Some methods include:
- Sampling data for manual reviews by domain experts
- Programmatic validation of formats, ranges, cross-field checks
- Statistical analysis of duplication rates
- Data profiling to check for gaps, outliers, and inconsistencies
Data quality KPIs measured prior to cleansing should be re-checked against thresholds. This confirms the progress and effectiveness of the project.
Develop Master Data Maintenance Plan
To sustain improvements, master data maintenance processes must be deployed. This can involve:
- Ongoing data monitoring and issue logging
- Periodic cleansing updates
- Training programs on data entry and quality best practices
- Enforcing data standards across systems
- Automating real-time cleansing, validation, and enrichment
- Regular data quality assessments
A data-driven culture focused on quality will help sustain master data integrity over the long-term.
Conclusion
Master data cleansing is a complex but critical initiative to improve overall data quality. Following steps like assessing current data, defining rules, selecting tools, cleansing records, validating improvements, and ongoing maintenance helps ensure project success. With clean master data, organizations gain business benefits like reduced costs, better operational efficiency, and data-driven insights for strategic decision making.
