Master Data Management (MDM) Best Practices

One of the most important tools for managing and growing a business is working with data.

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

Main advantages

Companies practicing master data management have confidence in the information they track in their daily work reports and move faster. This is how good information solutions are created. For example, during a global pandemic, when companies had to change consumer behavior quickly, some companies were able to move faster to resume virtual interactions that traditionally took place in person, such as auctions, without creating a customer experience. isolated because they had a single source of reliable information. Data for their business data.

Today’s best practices in master data management are sure to transform tomorrow’s business and customer experience. To get the most out of your enterprise data, review Synopps Master Data Governance and Management Best Practices.

A true multi-domain MDM that brings together customer, product, vendor, location and employee master data, enables your business to:

– Use your current provider 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 adjust 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 core data types (or domains) on one platform means holistic understanding and better business results. Many companies store their core customer and product data, not to mention supply chain, asset, location, and employee data. Integrating all these data sources, be it transaction data or product data, into your MDM with Synopps governance allows you to find the hidden connections at the seams of your business.

Data management quality 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 lets you take advantage of master data management without the extra work required to ensure quality.

For rapid implementation, your MDM must be simple and intuitive. Even MDM platforms that use AI can be used by data scientists or business users to train meaningful models and improve data accuracy over time. Your master data management needs to be both organized and flexible for your MDM to reach its full potential. If you’re still focused on building and implementing a fully data manager-driven data management system, it’s time to update your MDM strategy and probably your MDM platform.

You need to start with a subset of data and get clear first results. This is a proven approach in the enterprise, but this approach can fail or create complexity if your MDM is not designed to scale.

It’s efficient to have a flexible data model that allows you to quickly make changes and add new attributes as needed. For example, demographics may not be good enough for segmentation and the company wants to add psychographics to customer profiles. Or they want to indicate whether the customer is a healthcare provider or a frontline worker so they can offer a special quote. The flexibility to make changes quickly is key to the digital economy.

You need a unified data management foundation to ensure project ROI and the flexibility to respond to business changes. Consider 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 a data foundation, you can be confident that the change will impact the entire organization, whether understanding or supporting business operations.

Companies make decisions with large volumes of data; everyone in your company produces and consumes data. Gone are the days when business users had to rely on computer data. If you want your business to be truly data-driven and agile, data cannot be kept in IT alone. A master data management solution should make it 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 master data types provides more information, separating data responsibility provides more insights. With modern, agile MDM, you can continuously improve support for real-time operations, customer centricity, and improved compliance performance.

Your baseline data should help you see a clear path to your goals and show you unexpected areas for growth. A legacy MDM system may not meet 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.

Ramon J. Espinoza