What is master data management (MDM)?
Most businesses today operate hundreds of different applications and systems – like ERP, HCM, and CRM – that cross many different departments. Because so many people touch the data in these systems, it’s easy for it to become siloed, duplicated, out of date, or even contradictory. And as everyone knows, bad data leads to bad decision-making. To meet the need for timely, accurate information – even as data sources increase – businesses turn to master data management (MDM).
Master data management definition
Master data management is the process of creating and maintaining a single master record – or single source of truth – for each person, place, and thing in a business. Through MDM, organizations gain a trusted, current view of key data that can be shared across the business and used for better reporting, decision-making, and process efficiency.
What is master data, anyway?
- Customer master data: As the name suggests, customer master data includes all the core data needed to do business with your customers – from contact information to purchase history and payment terms. Managing master data for this domain includes cleaning and standardizing data across ERP, CRM, and other systems.
For example, the same customer’s name and address could exist in both an ERP and CRM system but be entered in two different ways, like 1030 Sandy Court, 1030 Sandy Crt., or 1030 Sandy Ct. MDM reconciles these differences and provides a single view of each customer that can be used to personalize marketing campaigns, deliver better experiences, and more. In some organizations, customer master data also includes data for employees, healthcare patients, and suppliers.
- Supplier master data: Supplier master data includes data for vendor accounts, contracts, policies, pricing, and more. It lies at the center of all important procurement activities, from planning and sourcing to contracting and purchasing. Cleansed and trusted vendor master data is critical for answering questions about supplier spend, pricing, or performance – for example.
- Location master data: Key attributes related to physical locations of agencies, corporate offices, distribution centers, and stores are contained in an organization’s location data. Once connected to other data domains, this information can help fuel location-based decision-making – such as determining the right product assortment for a particular store.
- Product master data: Product master data management covers attributes such as product number, category, price, features, bills of materials, and all other necessary data points. With uses across processes for sales, marketing, supply chain, and the product development lifecycle, this data needs to be reliable and accurate.
- Asset master data: Asset master data describes a business’ fixed and intangible assets, such as inventory, equipment, and trademarks. It usually includes attributes such as depreciation terms and values, asset classes, leasing information, and more. Inaccurate asset master data can lead to sub-par asset utilization and management. For example, equipment master data is used in predictive maintenance processes, so if it’s incorrect, then the predictions would be as well.
Why is master data management so important?
Besides helping an organization make good decisions and answer key questions, master data management has a host of other benefits. Here are just a few:
- Reduced errors and redundancies in data across multiple applications. If the same information, say a customer record, is entered by different teams in an inconsistent way, MDM will merge and reconcile the duplication.
- Better analytics insights and data-driven decisions. The saying “garbage in, garbage out” holds true here. If the data being analyzed is not accurate, then the results won't be either.
- Streamlined business processes and greater efficiency. High-quality, consistent master data can be used to speed up and automate end-to-end business scenarios such as lead to cash, source to pay, and design to operate.
- Improved transparency and compliance with data privacy and other regulations. The GDPR, for example, gives people more control over how their information is collected, managed, and shared. Without MDM, records can be fragmented across multiple departmental silos, making compliance difficult.
- Support for mergers and acquisitions with a streamlined process for merging and reconciling multiple data assets.
Data is crucial for powering digital business processes, and master data is the foundation that all other data relies on. Organizations that have accurate and consistent master data are better positioned to succeed.
How it works: MDM framework and processes
A framework for master data management works to ensure that all information is accurate and semantically consistent. It’s made up of two parts: Creating an initial master data record and then maintaining it.
- Creating a master data record: This process begins by identifying the databases and applications that hold data that should be included in the master record. Then all attributes or characteristics – such as product color, size, and materials – are defined. Finally, data is matched, reconciled where inconsistent, and merged where multiple records exist.
- Maintaining master data: This process involves cleansing, transforming, and integrating new data as it’s added to the master list to maintain the consistency and quality of records. Some master data management solutions today can automate and accelerate many aspects of this process.
It’s important to note that any process for managing master data should be carried out according to an organization’s data governance principles and policies. They outline how all business data is to be stored, managed, and safeguarded, and by whom – and that includes master data.
Best practices for a winning master data management strategy
A modern master data management strategy includes tools, technologies, and best practices to ensure all stakeholders can have confidence in the quality of data-driven insights – as well as the speed to act on them.
Here are some ways to create a winning strategy:
- Create a data governance framework if you haven’t already. As mentioned earlier, MDM activities should be carried out and applied according to data governance guidelines in order to ensure data stewardship and compliance.
- Harness artificial intelligence (AI) and machine learning. Some vendors offer MDM software and tools that use these capabilities to automate the process of checking new data for accuracy, matching it with current records, and reconciling them.
- Manage multiple domains in one instance. You can find solutions that focus on managing only one domain, such as customer master data or product master data. The best software, however, is capable of integrating and managing all of a company’s domains in one system – often in the cloud. This helps to lower costs, help staff be more productive, and support flexible use cases.
- Manage master data in the cloud. Cloud platforms can help increase productivity and efficiency by allowing data from different applications to be brought together, matched, merged, and cleansed – quickly, easily, and at scale. They also feature robust security and automated updates.
- Integrate master data management and data governance. By combining a single source of master data with centralized data governance in one solution, it’s easier to enforce policies and compliance – and define, validate, and monitor business rules and master data attributes.
Master data management is an ongoing endeavor, not a one-and-done project. As your organization grows, the volume of data will too. But with cloud-based, intelligent technology, tools, and workflows, these MDM processes become far more efficient.
Master data management FAQs
Data management refers to the overarching processes involved in collecting, organizing, and accessing all data within an organization. Master data management is a subset focused on core business entities and their characteristics – details for customers, suppliers, products, assets, and more.
Data governance is a set of practices, policies, and rules that ensure data is consistent, trustworthy, and compliant – and that it doesn’t get misused. A governance framework creates an organizational structure and names the people who are responsible for safeguarding specific types of data. Data governance and master data management are complementary: the rules from data governance are attached to the master data (as well as all other business data).
Master data management is focused on creating and then maintaining master data across the enterprise. It covers the process of enhancing, merging, and removing duplicates in order to improve data quality. Master data integration, on the other hand, is tasked with moving master data around and harmonizing it (regardless of quality) so that it can be viewed holistically across all applications. A master data integration layer allows for end-to-end process integration, gives line of business applications a consistent view of data, and ultimately, reduces the cost and effort of sharing data.
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