The term “master data” can be used in different ways but generally refers to the critical data assets of an organization. This article will focus on best practices for managing master data in a way that is fit for purpose, scalable, and sustainable. Keep reading to learn more.
What is master data management?
Master data management (MDM) consolidates and manages all the essential data for an organization in one place. MDM strategies can include processes for collecting, cleansing, organizing, and governing master data. The discipline of MDM arose in the late 1990s as companies struggled with the proliferation of data. The main problem was that data was spread across different systems, making it difficult to get a complete view of the data. In addition, data was often of poor quality due to inconsistency and inaccuracies.
MDM was created as a solution to these problems. MDM can be used for different purposes, like improving data quality. This is done by consolidating data from various sources and cleansing it of errors.
What are the benefits of master data quality?
There are many benefits of master data management. Master data is the source of truth for all other data in the organization. Therefore, having accurate and consistent master data leads to less data redundancy and inconsistency, which makes it easier to find, use, and trust the data. Accurate and consistent master data provides a solid foundation for timely and accurate decision-making. Well-managed master data can help organizations improve operational efficiency by reducing the need for manual data entry and ensuring data is standardized and cleansed before it is used in business processes.
Customers who interact with an organization expect their information to be handled accurately and consistently across all channels. MDQ helps organizations deliver a positive customer experience by ensuring that customer data is accurate and consistent.MDQ can help organizations improve compliance with government regulations and industry standards. Having authentic and consistent master data can help organizations avoid fines and penalties and protect their reputation.
What is a master data management strategy?
There are a few essential best practices for managing master data. Before implementing an MDM strategy, it’s necessary to define what business needs will be met by the project. Everyday needs include improving decision-making, reducing costs, or improving customer service. Next, identify which data should be included in the master dataset. Not all data is created equal, and not every piece of information is necessary or relevant to manage centrally.
Some examples of data types include contact information, location data, transaction data, demographic data, and behavioral data. Contact information includes name, email, phone number, company, job title, and more. Regarding location data, addresses, cities, states, and zip codes would need to be included. Transaction data is product SKUs, price, quantity, customer ID, etc. Demographic data is age, gender, income, race, and behavioral data is past purchase history, website visits, and social media interactions.
Once you have chosen which data to include, develop a process for collecting and cleansing master data. This step can be time-consuming but is essential for ensuring that the master dataset is reliable and consistent across departments and systems. It would help if you also implemented a governance framework to manage changes to the master dataset. A governance framework is a set of policies and procedures defining modifications to the master dataset. The governance framework should include a process for approving changes to the master dataset, guidelines for how changes should be made, a system for tracking changes to the master dataset, and a method for resolving conflicts between changes.
Lastly, use technology tools to support MDM processes. Technology tools can be essential in helping various aspects of an MDM strategy, such as data collection, cleansing, and governance.
