MDM (Master Data Management) Best Practices

business concept

One of the most important tools for running and developing a business is working with data.

Data analysis helps to choose the right solutions for discarding the excess and letting the business grow. It also powers your real-time digital operations and human interactions, such as in the call center and sales department, so you can deliver omnichannel customer experiences and improve NPS. For example, Synopps governance enables you to make decisions based on your data in real-time, giving your business the ability to adapt to market fluctuations, consumer preferences and access to capital. High-quality, insightful data reveals risks and opportunities from a bird’s eye view and drills down into the details for actionable strategies. Equally important is that your applications run in real time with clean and consistent data. Your ideas or business actions are only as good as the data, so realizing all of these opportunities to achieve business goals is entirely dependent on successful master data management (MDM).

Main Advantages

Companies practicing master data management have had confidence in the information they track in their day-to-day work reports and are changing faster. This is how the right information solutions are created. For example, during a global pandemic when companies needed to quickly change consumer behavior, some companies were able to move more quickly to pick up virtual interactions that traditionally took place in person, such as at auctions, without creating an isolated customer experience because they had a single source of trusted information. Data for their corporate data.

Today’s best practices for master data management are sure to transform the business and customer experience of the future. To get the most out of your business data, check out Synopps governance and master data management best practices.

А truly multi-domain MDM that brings together customer, product, supplier, location, and employee master data, allows your business to:

– Use your current supplier networks for omnichannel or direct-to-customer fulfillment 

– See the ROI on a single customer segment of a marketing campaign in a particular region and shift the budget accordingly 

– Create connected and hyper-personalized experiences for your customers across all channels, including digital or human interactions

Business Success with MDM Best Practices

More types of master data (or domains) on one platform means a holistic understanding and better business results. Many businesses store their core customer and product data, not to mention supply chain, assets, location, and employee data. Embedding all of these data sources, whether transaction data or product data, into your MDM with Synopps governance allows you to find the hidden connections at the seams of your business.

The quality of data management should be part of your MDM. A decent data management framework includes workflows and constraints to check for accuracy and redundancy and to match new data entering the system with current records. A modern MDM platform automates much of this work using machine learning and artificial intelligence. This allows you to take advantage of master data management without the additional work required to ensure its quality.

For fast implementation, your MDM should be simple and intuitive. Even MDM platforms that use AI can be used by data scientists or business users to train relevant models and improve data accuracy over time. Your master data management must be both organized and flexible for your MDM to reach its full potential. If you’re still focused on building and implementing a data management system that is completely data steward dependent, it’s time to update your MDM strategy and likely your MDM platform.

You need to start with one subset of data and clearly achieve the first results. This is a tried and tested approach in the business, but this approach can fail or create complexity if your MDM is not designed to scale.

It’s effective to have a flexible data model that allows you to quickly make changes and add new attributes as needed. For example, perhaps demographic data is not good enough for segmentation, and the business wants to add psychographic data to customer profiles. Or they want to indicate if the client is a healthcare provider or a first-line worker so they can offer a special quote. Flexibility to make changes quickly is key to the digital economy.

You need a unified data management foundation to ensure project ROI and flexibility to respond to business changes. Think about the scenario if you need to add a new attribute to the customer data model. You have to go and add it to all the disparate master data coming into different systems. Instead, if you have one data foundation, you can be sure that the change will impact the entire organization, whether it be to understand or to support business operations.

Businesses make decisions with high volumes of data; everyone in your business produces and consumes data. Gone are the days when business users had to rely on IT data. If you want your business to be truly data-driven and agile, data can’t be kept solely in the IT department. A master data management solution must be easy for business users to access data for understanding, operational use, and operational transactions. And also to help define data governance rules.

Businesses use data, so it’s important to empower them to define data according to their business needs. Over time, this will improve both your MDM and their data literacy. Just as combining different types of master data provides more information, separating responsibility for data provides more ideas. With modern, agile MDM, you can continuously improve real-time operations support, customer focus, and improved compliance performance.

Your core data should help you see a clear path to your goals and show you unexpected growth areas. A legacy MDM system can fall short of business goals because it doesn’t integrate quickly enough, isn’t designed for cloud management, doesn’t include machine learning, and is difficult to understand.