Master Data Management Best Practices

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master data management best practices

Modern businesses can stay competitive by using the best practices for master data management (MDM). These best practices promote the use of clean, accurate data about suppliers, customers, and products. This, in turn, supports better analytics and business decisions. A master data management solution supports the development of master data, which can provide consistent, accurate data about the business’s clients, suppliers, employees, partners, and prospects.

Generally speaking, master data acts as a trusted repository of stored data, which staff across the organization can query and use.

Master data management can be described as a combination of tools and processes that businesses use to integrate, manage, and define their master data. With master data, businesses can access accurate, detailed data for staff who need to execute their business functions. 

Businesses can also use master data to manage, centralize, and organize the data supporting short-term and long-term business goals. Conflicting and redundant information should be eliminated before being stored as master data.

Master data management tools are used to help coordinate and organize data throughout the business, while promoting the collection and storage of high-quality data. 

A Data Governance program and master data management are co-dependent processes, both necessary for a smoothly operated, efficient data-based business. Data Governance manages the quality, security, and availability of an organization’s data. Meanwhile, an MDM program provides detailed information needed by different departments. For instance, an accounting department will need detailed information about the price of sold merchandise, while a shipping department needs information about the addresses of the customers – all of which master data can provide.

Organizations without a master data management program may not have a master data system and may need to rely on data that is inconsistent, low-quality, and stored in various systems and data swamps.

9 Master Data Management Best Practices

Master data management supports access to clean, high-quality data that improves sales analytics, marketing efforts, and other factors that contribute to a company’s success. Businesses can use three basic steps to improve the efficiency of an MDM program: content consolidation, master data harmonization, and MDM object maintenance (sometimes confusingly referred to as “central master data management”). 

  • Content consolidation: The consolidation of data involves combining the data collected from multiple sources, while cleaning and verifying it to remove errors, and then storing it in a single place, such as a database or a data warehouse. Because the data is collected from multiple sources and using different formats, the content consolidation process makes unifying the data easier.
  • Master data harmonization: In simple terms, the harmonization of data increases its value and usefulness of data. The process transforms inaccurate and fragmented data into a workable format – allowing for new analyses and insights. Master data harmonization helps to reduce the time it takes to access business intelligence. It can also significantly help lower the overall costs of complicated data analysis.
  • MDM object maintenance: Periodically, specific information within the data must be updated (or perhaps eliminated) to support accuracy. Things change – businesses move, email addresses get replaced, people change positions, etc. The updates are applied to “data objects,” which are subsets within the database. For example, one data object might contain only vendor data, while another contains only customer data. A data object is a group of data that should be thought of as an individual file or collection of information. 

There should be a sense of perfectionism in building a master data management program, as opposed to moving fast and furious.

The use of master data management best practices involves using the best methods currently available to achieve business objectives efficiently. It requires staying up to date with the tactics used by successful competitors. Below are several additional best practices that can be used with master data management.

  • Creating master data: Master data typically includes information about products, customers, suppliers, compliance data, financial data, and the Data Governance program. By examining the master data needed for specific areas, a master data management program can be initiated. Work on one department or area at a time to allow for a focus on details.  
  • Automation: There are essentially two different types of MDM automation: data management automation and process automation. These two forms of automation can be used separately, or they may be used in combination to provide an automated master data management solution. This involves the use of event triggers, with target systems being determined according to the business context of the event. 
  • Machine learning: Both MDM and machine learning rely heavily on data, and support and enhance one another. Machine learning improves as it processes more data, and supports MDM with automation, reducing the workloads of data stewards and administrators. Master data management provides accurate, high-quality data for the learning process.
  • Analytics and Agile: Including DataOps considerations and the Agile philosophy can increase flexibility and business efficiency, and is considered a best practice in master data management. DataOps is used to provide high-quality data and accelerate the development of analytics. It is similar to DevOps in that both are based on the Agile philosophy. 
  • Using the right tools: Historically, businesses have preferred to do business with a single vendor for all their data-related needs. This simplified the decision-making process and minimized software conflicts. This behavior is, however, no longer the best way to access the most efficient tools. When comparing master data management solutions, look for platforms using an architecture that is open and compatible with a variety of tools.
  • Including the cloud: The goal of this best practice is to find a cloud that is a good fit for your system, not adapting your system to meet the needs of the cloud. The cloud you select should have the tools your business needs, and be flexible enough to adapt to your future MDM needs. Use of a cloud that supports your needs should reduce the overall costs of developing master data management. Additionally, the cloud provides an extremely secure, scalable infrastructure that offers additional storage and computing power. 

The Benefits of Using MDM Best Practices

By adopting the best practices for MDM, a business can streamline a variety of practices. MDM is part of a larger strategy that attempts to make an organization’s data infrastructure more efficient. 

Some benefits of implementing a master data management strategy for the organization include:

  • Promoting business agility: By working with reliable and accurate data, businesses can provide more flexible, more customer-friendly responses in the short term, and understand potential long-term opportunities and challenges.
  • Guaranteeing Data Quality: Master Data Management solutions can eliminate the use of inconsistent and inaccurate data. Ensuring data consistency and uniformity promotes business efficiency.
  • Streamlining business activities: Businesses typically find processing massive amounts of data without master data management difficult and time-consuming. Manually processing large amounts of data is extremely slow and clumsy. MDM can automate data processing, saving time and improving business processes.
  • Reducing security concerns: MDM applications and tools help ensure a business adheres to the regulations and policies that various countries have implemented. Additionally, it lowers the chance of security breaches.

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