March 19, 2024

Establish MDM Benchmarks for your Data Management Team

Microsoft Cloud Solutions

Managing data has never been more critical than now, with businesses and organizations relying increasingly on data to make decisions and stay competitive. To ensure effective data management, establishing benchmarks for your Master Data Management (MDM) strategy team is essential. MDM benchmarks provide a standard for measuring performance and effectiveness.

Define the Objectives and Goals of Your Data Management Team

The first step in establishing MDM benchmarks is to define the executives' and team's objectives and goals. This will provide a clear understanding of what everyone's trying to achieve through benchmarking and when you are working on a big, seemingly never-ending project. Objectives and goals can include improving data quality, increasing productivity, reducing errors, or improving data integration across systems. Each objective should be specific, measurable, achievable, relevant, and time-bound (SMART), making tracking progress and measuring success easier. And remember to celebrate and acknowledge milestones to remind yourself that you're making progress and that there is value in what you're working toward.


Identify MDM Benchmarking KPIs

Once your objectives and goals are defined, the next step is identifying the MDM benchmarking Key Performance Indicators (KPIs) you will use to measure performance. They can include things like data quality, data governance, data security, data integration, and data accuracy. Each element should be relevant to your objectives and goals and measurable in some way. Measuring these milestones enables you to monitor progress over time and identify areas of improvement by comparing month-over-month or year-over-year.

Ideally, each milestone will build from the last in an upward climb. This clarifies that your KPIs will have a measurable and beneficial impact.

KPIs should be encouraging to both the project team and executive-level stakeholders. Place them tactically throughout the project's life for consistent momentum.


Establish the Appropriate Metrics for Measuring Success

Once your benchmarking components are identified, the next step is establishing the appropriate metrics for measuring success. Metrics should be specific, measurable, and relevant to your objectives and goals. For example, if you aim to improve data quality, relevant metrics might include completeness, accuracy, consistency, and validity. Establishing the right metrics will make tracking progress and measuring success easier. It will continue to be utilized long after project completion. They become a regular part of monitoring your departments. Use MDM metrics to analyze and assess the flow of data and the trends of internal and external data integrity. Watching your data moving forward must be a top priority for your MDM team.

Some examples of MDM metrics are:

  • Percentage of duplicate data
  • Cycle time for new customer set-up
  • Error rate in the data record
  • Mean time to repair data issues
  • Percentage of current accounts with incomplete data
  • The average database availability time

There are many more options, and you must talk with your team to develop the best metrics to help meet your objectives. Again, this should be done early in the project, as your MDM strategy should be built from your data needs. Therefore, your metrics should be indicative of those needs.


Utilize a Data Governance Tool or Platform to Monitor Performance

Utilizing a data governance tool or platform is essential to track progress and measure success. Data governance tools provide visibility into data quality, integrity, and protection. They help ensure your team follows established policies and procedures and provide a framework for managing data across the organization. Using a data governance tool or platform, you can monitor performance and ensure your team meets established benchmarks for your MDM strategy.


Create Actionable Strategies for Improving Performance from the Measurements

Once you have established benchmarks and are tracking the appropriate metrics, the next step is to create actionable strategies for improving performance from the measurements. These strategies can be based on data analysis or team performance. For example, they can include training, process improvement, data cleansing, or enrichment projects. Creating actionable strategies ensures your team is taking steps to improve performance and meet established benchmarks.


Implement MDM Best Practices to Ensure Sustained Improvement in Performance

Finally, to ensure sustained improvement in performance, it's crucial to implement MDM best practices. Best practices can include things like data profiling, data standardization, data cleansing, and data enrichment. By following established best practices, you can ensure your team consistently manages data to a high standard, continuously improving data quality, governance, security, and integration.


Establishing MDM benchmarks is an essential step in effective data management. By defining objectives and goals, identifying benchmarking components, establishing metrics, utilizing data governance tools or platforms, creating actionable strategies, and implementing MDM best practices, you can ensure your MDM strategy team effectively manages data and drives improved performance. In addition, with benchmarks in place and measurable metrics being achieved, your data management team will continue to drive advances in data analytics, allowing your organization to make more informed decisions that will ultimately help to secure a more secure future.



Interested in learning more about Master Data Management, establishing benchmarks, or data analytics? Fill out the form below, and one of our experts will get in touch with you.


Sayed is a seasoned technology executive with a distinguished career in design and development of Business Intelligence & Analytics. Over the last 20 + years, he has a proven track record of effective leadership and execution, having implemented end-to-end BI solutions, including data engineering, data visualizations, infrastructure design, and execution for various multinational companies to provide actionable insights, increased resilience, and higher profits.

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