The Ins and Outs of Analytical Master Data Management (MDM)

by | May 4, 2022 | Healthcare, Master Data Management

A person using a heat map generated from analytical MDM in a healthcare organization to make data-driven decisions

In healthcare organizations, analytical Master Data Management (MDM) presents an opportunity to improve care and reduce the cost of treatment. Data-driven decision-making improves the medical supply chain, but the true value of analytics depends on an analytical MDM implementation that supports the entire ecosystem of payers and providers.

Healthcare organizations are going through a phase of rapid transformation. The industry had to adapt to the challenges of a global pandemic and reimagine its approach to providing quality care across the medical supply chain. One indication of this trend is the significant growth predicted for the healthcare analytics market, poised to reach $75 billion by 2026 from $21 billion in 2021. This shows that healthcare organizations are investing heavily in solutions that help them work smarter and faster while keeping costs down and improving the quality of care.

For organizations considering MDM as a core component of their future strategy, the choice comes down to analytical vs. operational MDM. Below, we look at analytical MDM and how it differs from operational MDM in the healthcare industry.

Key Takeaways:

  • Modern healthcare ecosystems depend on analytical MDM to make data-driven decisions from large volumes of internal and external datasets
  • Analytical MDM is a lightweight implementation that is non-intrusive and focused on improving an organization’s reporting and analysis of daily operations
  • With analytical MDM, healthcare organizations can gain valuable insights into patient outcomes, reduce administrative costs, and ensure compliance with evolving regulations

What is Analytical MDM in Healthcare?

Analytical MDM aims to move the management of all master data from the technical processes and authoring tools to a business-orientated solution with stringent governance processes. In practical terms, it requires a data warehouse that improves the quality, cross-referencing, and hierarchies of all data to support the core capabilities of business intelligence applications.

The use of MDM in healthcare organizations enables:

  • Extracting valuable, trustworthy, and meaningful data across the healthcare supply chain
  • Delivering value-based care in a consumer-centric healthcare marketplace
  • Understanding the processes, procedures, and outcomes within the organization that influence performance

Operational vs. Analytical MDM: What’s the Difference?

Traditional approaches to healthcare MDM put much of the onus on the IT leaders who had to harmonize master data from the organization’s core systems. Often, these systems operate in silos to support specific business functions like finance, purchasing, accounts, sales, and services. Gaining a single source of truth from these disparate systems requires solid data governance and stewardship in the source application.

Operational MDM makes it challenging to provide actionable insights to the organization due to its resource-intensive approach to implementation. Combined with the volume of data used in healthcare, operational MDM often becomes an endless loop of sanitizing transactional data without adding any value to the decision-making process in the organization.

Operational MDM faces three significant challenges:

  • Organizational – Differing priorities in unrelated business units make stewardship and governance complex to implement and maintain
  • Technological – Core solutions will have duplicate data with little or no integration, which hampers the business’ ability to gain a single source of truth
  • People – A lack of awareness at the authoring level can compromise data integrity and lead to compliance, privacy, and governance issues

Analytical MDM solves many of these challenges by extracting source data into a data warehouse without intruding on operational applications. From an architectural perspective, analytical MDM is easier to implement and maintain to support the organization’s decision-making processes.

How to Implement Analytical MDM for Healthcare

Although analytical MDM delivers better decision-making at the organizational level, the implementation still requires strategic planning and proper governance to add value. A two-phased approach to building a roadmap will give the project the biggest chance of success.

  • Phase 1 – Define the vision for the MDM project and scope the level of effort required to align the MDM with the business’ goals
  • Phase 2 – Create and implement a detailed plan that addresses the data requirements for the organization, including the alignment of its current MDM capabilities

The diagram below shows what a typical analytical MDM implementation should entail.


Two-phase implementation project plan for analytical MDM that aligns with the organization's decision-making goals
Image source:


The Two Types of Analytical MDM

There are two types of implementation strategies to consider when moving to analytical MDM. Although analytical MDM is an easier implementation than operational MDM, you should remember that it wouldn’t provide you with better quality data by itself. The source systems will still author the data, and the data warehouse will need to provide the insights for improved operational and administrative decision-making.

The two types of analytical MDM strategies are:

  • Registry – The MDM hub pulls data from source systems and runs harmonizing algorithms to identify duplicates and cleanse the records before assigning a unique identifier to each. In this approach, the MDM hub is read-only to users without requiring stewardship of different datasets.

Consolidation – Similar to the registry approach, consolidation adds a stewardship layer in the MDM hub before releasing the data to downstream applications. With stewardship, a human element is included in the analytical MDM implementation, where golden records go through additional validation and verification before becoming available for reporting and analysis.


Video: What is Master Data Management (MDM)?


Deciding what the best approach depends on the organization’s current technological capabilities and buy-in from current decision-makers.

Moving to Ecosystem Master Data Management Platform

The source data originates from an ecosystem of providers, insurers, practitioners, and external partners in healthcare settings. This ecosystem consists of extensive datasets from multi-domain disciplines serving payers, providers, and members using a patient-centric approach.


A diagram showing the patient-centric healthcare ecosystem of the future
Image source:


An Ecosystem MDM (or E-MDM) approach is the latest innovation in healthcare analytics and reporting, making it possible to extract valuable insights from internal data sources and external parties.

Coperer E-MDM helps improve patient outcomes and reduces the administrative costs associated with MDM implementations. You can also improve your regulatory compliance by having a cloud-based MDM solution specifically geared towards the healthcare and life sciences industry.

If you want to extract additional value in your organization with analytical MDM, get in touch with Gaine to discuss our Coperer E-MDM platform today.


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