Master Data Management (MDM) plays an important role within the broader concept of Data Management as companies are acquiring data at an ever-expanding rate. The need to know what data lives where, and the frustration of differing layers of data quality and accuracy have pushed organizations to re-evaluate their data strategies.
How does one get the correct definition of a customer if there are multiple systems that contain differing definitions, attributes, and methods of categorizing said customer? How do different departments analyze, report, and communicate if their data is not consistently aligned?
The implementation of an MDM program is one of the solutions that addresses this problem and empowers better business decisions. However, before we all rush out to get the latest MDM tool, there are important steps to implement first. Organizations must illustrate what kind of data model is needed to ensure that an accurate and consistent view of the model’s definition can be provided back to the organization. They must also describe and execute an operational framework, guidelines, and best practices needed to support a highly effective data management program.
This framework is the focus of this article, which will address 3 main components for success:
1. Why do we need a master data management framework? 2. What should a framework look like? 3. How do you implement an MDM framework?
Why do we need a master data management framework?
A framework focused on master data management allows organizations to define a process that utilizes best practices for improved data quality, integration, reporting and analysis. In addition, it describes who should be involved and their roles and responsibilities.
How do you know if your organization needs an increased focus on MDM?
Disparate data – Quality issues related to data being incomplete, inconsistent, and inaccurate combined with data definitions that are not shared between multiple systems.
Disjointed BI – Inability to consistently report and analyze along the same definitions and attributes of a domain or entity.
Undefined or Incomplete Operations – Lack of a unified and cohesive definition of standards, policies, roles, and responsibilities.
Redundant and Inaccurate Data – Duplicate and inconsistent data leads to confusion about which sources should be trusted and a reluctance to use it for critical business decisions.
All the items listed above contribute to an organization’s inability to quickly make informed business decisions, either because of bad data management practices or costly processes due to tech debt that has accumulated over time.
What should an MDM framework look like?
Organizations are as unique and varied as their individuals, which is why there is no one-size-fits-all framework for MDM. Usually, the biggest challenge is knowing how best to implement the program based on the company’s strengths and values.
However, there are some common core elements that any framework should include to provide a solid foundation which can be expanded upon in the future. There are three main areas of focus to any MDM framework:
Data Quality – Knowing you can trust the data is paramount. Reducing areas of duplication and ensuring data is meaningful, accurate, complete, secure, and timely one of the most important aspects of MDM.
Data Integration – The second important element (depending on the hub architecture) is taking the improved quality of data and integrating it back into the systems that rely upon it.
Data Governance – Creating master data management policies and standards that are shared across the various systems and departments is key. These should describe what the MDM canonical model is and what it is intended for. It will list the rules for matching, merging and surviving data that is the golden record, define MDM roles and responsibilities so the expectations are clear on how to manage the MDM processes, what data is involved in which pipelines, who can and should interact to resolve mastering conflicts and create the rules to which MDM is enforced.
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Implementation of a Master Data Management framework, like most successful implementations, contains phases and components related to the quality, integration, and governance of operations. An iterative, top-down approach gains the most value because it blends business, functional, and technical aspects that continue evolving and providing value to the organization.
RevGen Partners’ approach combines those three areas together. Listed below are five important aspects that we use for implementing MDM and combining a business focus with both technical and functional disciplines focused on data-driven enablement.
With this methodology, we have successfully implemented MDM programs for many clients, delivering real, proven value to the business and empowering their organizations to grow their use of data in driving decisions. To learn more about RevGen’s Master Data Management solutions and other data services, please visit our Analytics & Insights page.
Corey Biehl is a technology leader in RevGen’s analytics and insights practice. He is passionate about designing and developing data & analytic solutions that make a difference.
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