The plan, "Driving the Business End of Master Data Management," focuses on enhancing the quality and utility of data through effective selection and rule development. By targeting key metrics such as accuracy, completeness, timeliness, consistency, and relevance, organizations can ensure that their data integrity supports strategic decision-making and operational efficiencies.
Data accuracy, for instance, is critical as it ensures that decisions are based on reliable information. Implementing regular audits and automated validation tools can significantly reduce error rates. Similarly, data completeness is essential to provide a full picture, preventing incomplete records that could skew analyses.
Data timeliness ensures that the most up-to-date information is available for immediate processing, vital for dynamic and fast-paced business environments. Meanwhile, data consistency across different systems is paramount to prevent discrepancies that could lead to costly errors. Finally, maintaining data relevance ensures that resources are devoted to data truly beneficial for current business goals.
Top 5 metrics for Data Selection and Rule Development
1. Data Accuracy
Measures the percentage of data entries that are correct and error-free across the system
What good looks like for this metric: Above 95%
How to improve this metric:- Implement regular data audits
- Use automated data validation tools
- Provide staff training on data entry accuracy
- Establish clear data entry guidelines
- Enable error-detection algorithms
2. Data Completeness
Assesses the percentage of data records that are fully filled and not missing any critical fields
What good looks like for this metric: Above 90%
How to improve this metric:- Conduct routine completeness checks
- Utilise automated form filling
- Standardise data requirements
- Regularly review data input processes
- Incentivise complete data entry
3. Data Timeliness
Measures the speed at which data is updated or made available for processing
What good looks like for this metric: Within 24 hours
How to improve this metric:- Automate data update processes
- Set clear timelines for data entry
- Monitor data latency regularly
- Establish a data steward for timely updates
- Prioritise data updates during peak times
4. Data Consistency
Evaluates whether data is consistent across different databases and sources
What good looks like for this metric: Close to 100% consistency
How to improve this metric:- Implement cross-system data comparisons
- Use master data management tools
- Regularly review data transformation processes
- Ensure consistent data entry formats
- Provide training for consistent data handling
5. Data Relevance
Determines the degree to which data is relevant and useful for current business needs
What good looks like for this metric: Above 85% of data in use
How to improve this metric:- Regularly review and update data policies
- Conduct user feedback sessions
- Align data selection with business objectives
- Utilise data analytics to assess relevance
- Remove outdated or redundant data regularly
How to track Data Selection and Rule Development metrics
It's one thing to have a plan, it's another to stick to it. We hope that the examples above will help you get started with your own strategy, but we also know that it's easy to get lost in the day-to-day effort.
That's why we built Tability: to help you track your progress, keep your team aligned, and make sure you're always moving in the right direction.
Give it a try and see how it can help you bring accountability to your metrics.
