What Is Master Data Management, and Why Is It Important for Healthcare?

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What Is MDM and How Does It Relate to Data Governance?

MDM is one area of data governance (by which organizations set standards and policies for their data to ensure its usefulness, accuracy, security and availability). Other areas of data governance include data stewardship, data quality, metadata management, reference data management, data security management, data privacy and information lifecycle governance.

Master data is persistent critical data stored in disparate systems spread across an enterprise. This data is critical to nearly every application healthcare organizations use, which makes MDM an essential practice. By applying consistent standards to critical and shared data, an organization can ensure the data’s quality and then share it with many different systems.

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Organizations looking to engage an MDM practice should understand three key concepts:

  • MDM governance: This includes the processes, people and groups that create, manage and oversee master data to ensure its quality, accuracy and value.
  • Ownership and stewardship: The technical and business stewards of data, as well as the business owners of data, define and manage master data, guided by the governance process.
  • Tools: Software can be used to automate, execute and track data content and activities.

How Can Healthcare Organizations Use MDM?

In healthcare, data is commonly organized into three domains: patient, provider and location. Every health system needs to think about how to manage and cleanse that data so it is ready for use across numerous systems and applications. For example, an organization can have financial and clinical systems that both use the same data, which is managed or updated once and pushed into those systems.

Having consistent, accurate patient data gives organizations greater confidence in providing consistent care. Patient data errors can lead to care mistakes, such as delivering the wrong medication to a patient, or administrative issues such as billing problems.

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Ultimately, MDM leads to more efficient and effective data processes because it reduces redundant data effort and ensures that data is accurate. To achieve these objectives, healthcare organizations use MDM solutions from vendors such as Informatica and IBM to organize, de-duplicate and cleanse data from different sources. MDM solutions generally also include data quality tools as well as data movement and organization tools.

However, organizations can face challenges as they engage in MDM. It takes a coordinated effort from multiple people and departments over time as stakeholders identify and collect master data. Getting assistance from a trusted, experienced third party often helps organizations achieve their MDM objectives. These experts can also help an organization implement data governance best practices and apply them to master data. It’s also useful to establish roles for data quality managers and data analysts who can understand and apply knowledge on data quality to critical domains.

Healthcare organizations that implement MDM practices effectively will find themselves on solid ground as they engage in digital transformation initiatives that use data from a variety of sources.

This article is part of HealthTech’s MonITor blog series. Please join the discussion on Twitter by using #WellnessIT.

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