Master data governance is the application of data governance in order to improve the management of a subset of data called master data. Master data is the core data domains that you use to run a business or organization. For example, you might buy materials, from suppliers, to build products, that you sell to customers, and deliver those products with partners. Having accurate and consistent material, supplier, product, customer and partner data helps you improve the efficiency and accuracy of your processes such as order to cash, procure to pay, and record to report.
Here’s a quick review of some key categories and best practices for master data governance.
Definitions: Master data governance describes a core set of attributes which are the basis of a common definition of master data that is consistent across the organization. For example, a customer master can contain name (full name of person or business name), address (shipping and billing), email, phone, payment terms, and any other attribute that is vital for your business processes. It is important to define which attributes are critical, otherwise you risk trying to master too many attributes—which will negatively impact the agility and success of your master data management (MDM) activities.
Policies: Master data governance ensures that internal policies and external regulations are addressed as part of the management of master data. Policies can be oriented around many aspects of master data governance such as data quality, privacy and protection, retention and deletion, and risk management. For example, requiring a separation of duty between who can create cost center master data in a general ledger system and who can approve the creation of cost centers is a risk control policy that helps prevent accounting fraud.
Rules: Policies define what you want to do, and rules define how you execute and enforce policies. Here’s how it works: you may have a policy that states you must obtain consent for processing before you use personal information. One rule might define the consent attributes that need to be part of the customer master data definition, such as billing, marketing, and third-party sharing. Another rule might enforce the collection of those consent attributes before a customer record can be created and approved. And a third rule might check for the marketing consent attribute before customer data can be sent and used in a marketing automation system. It’s not unusual to have multiple rules in order to address the requirements of a single policy.
Catalog: Master data governance involves several cataloging capabilities, including:
Understanding the master data you have, knowing where it is located, and clarifying how it conforms to your definitions and policies is critical for MDM. For example, mergers and acquisitions are a common strategy for growth and expansion into new markets. Understanding the master data present in the source systems of the acquired company and how it maps to your master data definitions can reduce integration costs, accelerate business value, and reduce financial reporting risk.
Process mapping: Just as a catalog documents where master data resides, process mapping shows how master data flows between sources as part of business activities. Understanding not only the sources of master data, but also how it flows through processes helps you better visualize things: how data is being used, compliance risk exposure, and where rules need to be embedded into process to enforce policies. To illustrate this, think of a clinical trials process. You need to understand where data is collected, which systems it flows to, and what third parties it is shared with so you can enforce standards and policies for clinical data acquisition and submissions.
People: Master data governance documents and provides visibility into the people across organizational functions who are key to the success of MDM activities. These key people include:
Workflow: Once you have defined your key people, you also need to document the workflows that will enable those people to collaborate:
Metrics: Master data governance also defines the metrics that you use to measure and manage master data. Common technical metrics include things like the number of duplicate records in an application, the accuracy and completeness of master data, and how many personal data attributes are encrypted or masked. While these types of metrics can help in the technical management of master data, leading organizations will frequently also try to further define how these technical metrics impact business outcome metrics. For example, understanding how quality and consistency of material and supplier master data impacts your ability to negotiate better procurement terms, mitigate supply disruption risk, and reduce inventory carrying costs.
From a governance perspective, technology is about defining the types of capabilities that are needed to scale master data governance, not to execute MDM. (To learn more about the difference between data governance and data management, you can read this blog.) These technologies include connectivity and metadata scanners to help catalog master data across sources, as well as lineage and process management capabilities that assist with process mapping and workflow.
Whether you need data cataloging, policy and rule management, data integration, data quality, MDM or data privacy and protection, Informatica provides market-leading capabilities for both governance and management of master data. Only Informatica offers end-to-end capabilities integrated into a comprehensive and modular platform, powered by artificial intelligence and machine learning. Learn more about data governance and its relationship with data management in our eBook, “Reimagine Data Governance.”