What Does Operational Master Data Management Involve?
The market for data management tools is growing quickly. The global market for master data management (MDM) systems had a total global value of $11.3 billion in 2020 and, for the last two years, has held a trajectory of reaching $34.5 billion – marking an increase of over 200% – by the end of 2027. As MDM continues to become a critical business discipline across industries, subsets of practices are emerging. One of particular importance for organizations needing a single view of master data in core systems – a high priority for healthcare and financial institutions – is operational MDM.
The rapid growth of MDM derives from two recent global business trends. The first is the shift to remote work during the COVID-19 pandemic exacerbated the challenges many companies faced managing overlapping and often irreconcilable data stores across different systems. For many organizations, these challenges indicated an emerging need for an authoritative, single view of integrated data from the top-down. The second is increased competition in the MDM platform market has significantly lowered the cost of effective data management technologies, drawing in more mid-sized organizations.
In a market of expanding options, organizations adopting MDM practices and shopping for platforms to enable them should consider what model or subset of MDM best suits their needs. In this guide, you’ll learn what operational MDM is in the broader context of generalized data management and what differentiates this subset of MDM.
- Growing disparities in organizational data storage systems have created an increased demand for master data management (MDM) tools and practices.
- Master data management helps organizations create a single source of truth (SSoT) in their systems, making data more reliable and accessible.
- Operational MDM is a variation of MDM that focuses on integrating data and resolving disparities in the systems that create data rather than downstream in consolidated applications.
What Is Master Data?
Organizations capture data in a variety of ways. Different departments such as sales, marketing, and IT, among others, have databases of information fed by different processes and operations spanning websites, customer relationship management (CRM) software, third-party software-as-a-service (SaaS) providers, and social media channels.
When customers and partners interact with an organization, the final storage destination of the data created in that interaction often depends on which part of the organization processed it. Sales teams field quotes and other queries, storing information in their databases. Data captured in ad-clicks and social media interactions goes to marketing. Updated client contact information likely enters organizational systems through billing or finance departments.
Over time, the emergent effect of these disparate data capture methods is that data redundancies and discrepancies proliferate throughout the unintegrated systems that contain an organization’s data. Without a hierarchy among data sources, data disparities become functionally irreconcilable, stripping large data sets of their practical value.
Image Source: https://blog.hubspot.com/website/master-data
Applying strategic data management tools and practices to resolve data disparities and reduce redundancies creates a single, authoritative version of data called master data. Refining organizational data into master data puts everyone in the organization on the same page by reference to what data scientists call the golden record of information. Achieving this golden record involves integrating isolated databases, establishing a hierarchy of data sources to resolve discrepancies, and configuring platforms to integrate data sources to deliver master data throughout the organization.
Master Data Management
Master data management (MDM) is the IT process of deriving master data from multiple, overlapping data sources. Research firm Gartner ascribes five purposes to MDM for data quality.
- Uniformity: Eliminating disparities
- Accuracy: Ensures disparities resolve to the more accurate version
- Stewardship: Assigns roles for coordinating data management throughout departments
- Semantic Consistency: Defines formatting and validation standards for data reporting
- Accountability: Clarifies who is responsible for data management practices
In practice, achieving these goals requires identifying the unique data domains within an organization. These will vary widely by industry. Some common examples include:
- Manufacturing: customers, products, suppliers, and materials
- Healthcare: patient records such EHRs and EMRs, providers, facilities, and health plans
- Finance: customers, accounts, and financial products and instruments
Establishing domains helps organizations define roles and hierarchies effectively, with each creating the golden record for its own areas of responsibility. For this reason, master data generally does not include transactional data. Rather, it provides organizations with master files for all critical data objects, such as:
- Client Information: names, contact information, system IDs
- Product and Service Information: specifications, capacity, service types
- Internal Organizational and External Partner Information: personnel, authorizations, user IDs
With each domain reporting its golden record of the data it captures and stores, organizations can distribute and reference each in their systems as the single source of truth (SSoT), allowing individuals throughout the organization to use data confidently in any relevant application.
Operational Master Data Management
Image Source: https://qmetrix.com.au/operational-or-analytical-master-data-management/
Operational MDM, typically distinguished from analytical MDM, is a variation of MDM practices that focuses on creating a single view or SSoT in the core systems of an organization, for use within those systems rather than downstream for decision-making and by external third parties and applications. MDM handles data in three phases.
Operational MDM prioritizes data integration in the first two phases, while analytical MDM prioritizes the latter two. For visualization purposes, one could describe the main difference between operational and analytical MDM as where data consolidation and integration occur. In operational MDM, each domain integrates before data is sent downstream.
Thus, operational MDM connects major existing applications such as CRMs and enterprise resource planning (ERP) platforms. Because operational MDM happens internally, it creates better data quality and transparent auditing trails. As such operational MDM better suits organizations with significant data privacy and regulatory compliance concerns in fields such as healthcare and finance.
Healthcare Master Data Management with Coperor by Gaine
Coperor’s healthcare-specific MDM platform enables a profile-based Operational Data Store (ODS) that integrates organizational master and affiliate data in real-time. With Coperor, your organization has comprehensive visibility into data across the entirety of your systems and contracted partners.
To schedule a live demo of the Coperor MDM platform, contact Gaine today.
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