8 Best Practices for Getting the Most From Master Data Management

Data is your business’s most important asset in a digitized world. It drives decision-making at every level of business. It also powers your real-time operations across digital channels and human interactions such as call center and sales so you can deliver omnichannel connected customer experiences and boost NPS scores. Real-time data-driven decisions give your business the agility to adjust to market fluctuations, consumer preferences, and access to capital. Quality, insight-ready data reveals risks and opportunities from a business’s birds-eye view and drills down to the details for actionable strategies. It is equally important that your applications are powered in real-time with clean and consistent data. Your insights or business actions are as good as the data, so to realize all this power to drive business goals entirely depends on successful master data management (MDM).

Why Best Practices Matter for MDM

A great example of how insight-ready data was critical to success was during the global pandemic when companies needed to pivot quickly to changing consumer behavior. The companies with master data management best practices in place were confident in the insights they were monitoring on a daily business and pivoted faster. To illustrate, some companies were able to pivot faster to curbside pick up and virtual experiences that were traditionally run live in person, such as auctions, without creating a disconnected customer experience because they had a single source of truth for their enterprise data.

Best practices for master data management in the past have largely centered around master data governance: creating a ‘golden record’ and ‘matching and merging’ data that conflict with it or duplicates it. What data you use, how you structure it, and who can access it all hinges on preserving data quality in legacy MDM systems. With that said, modern master data management best practices power business transformation and the customer experiences of the future. To get the most out of your business’ data, read on to discover our list of master data management best practices.

MDM Best Practices for Business Success

#1 Always include more master data or multi-domain 

Try to think of one business problem that can be solved with just one master data type.  More master data types (or domains) on one platform means holistic insights and better business outcomes. Many businesses silo their customer and product master data, let alone their supply chain, asset, location, and employee data. Bringing all of these data sources, whether it’s transaction data or product data, into your MDM allows you to find the hidden connections in the seams of your business. 

The fewer the master data silos, the more connections you can make across functions that can power real-time operations at scale. For example, a truly multi-domain MDM that brings together customer, product, supplier, location, and employee master data, allows your business to:

  • See the ROI on a single customer segment of a marketing campaign in a particular region and shift the budget accordingly 
  • Use your current supplier networks for omnichannel or direct-to-customer fulfillment 
  • Create connected and hyper-personalized experiences for your customers across all channels, including digital or human interactions

The best practice to combine different types of master data goes far beyond your internal data sets. Using data to win in your market means using data that your competitors can’t access, like your business’ unique Big Data, IoT, and unstructured data in videos, chats, and audio. We made “combining more data types” our number one best practice for a reason: if your master data management strategy includes limiting data, it will limit insights, and can never be best-in-class. 

#2 Make data governance an integral part

Data governance and data quality should be part of your MDM. A good data governance framework includes workflows and guardrails to check for accuracy and redundancy and match new data entering the system with current records. A modern MDM platform automates a lot of this work using machine learning and AI. This allows you to leverage the benefits of master data management without the extra work needed to ensure its quality.

Data governance should not be limited to data stewards, of course, they are needed to establish rules to ensure master data quality and accuracy but business users are the ones who are actually using the data. Your MDM should be easy and intuitive for a business user for quick adoption. Even MDM platforms using AI can be used by data stewardship or business users to train the matching models and improve data quality accuracy over time. Your master data governance must be both organized and agile in order for your MDM to fulfill its full potential. If you’re still focusing on creating and executing a data governance framework solely dependent on data steward, it’s time to update your MDM strategy and likely your MDM platform. 

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#3 Build your MDM to drive business goals

Creating the structure and reference data for your MDM should not be solely on the shoulders of the IT department. Business leaders need to set clear objectives about how they want to use business-critical data and communicate business goals to your MDM team. 

To align the MDM with your business, start with KPIs, budgets, quarterly goals, and five-year plans. Identify the metrics that reveal your progress and blank spots in your analytics and work backward. Where will clean, consistent and connected master data have the biggest impact? For example, consider the following questions:

  • Will it improve customer experience and boost NPS scores,  revenue, and retention? 
  • Would it help you segment customers more effectively and boost conversion rates? 
  • Would it help you identify customers faster across channels and reduce issue resolution times? 
  • Would it help you detect fraud faster or revenue leakage? 
  • Would it improve the efficiency of processes? 
  • Would it accelerate reporting for compliance? 

Starting with the end goal in mind and always keeping MDM in a business context ensures that you continually reexamine it and evolve it with the business. Modern MDM platforms support internal Reference Data Management (RDM) and foster agility to power digital innovation. 

#4 Organize master data for simplicity and scalability

A lot of MDM best practice advice will tell you to start small, with one subset of data, organize it clearly, and gain early wins. Wading in like this is a tried-and-tested approach in business, but this approach might fail or create complications if your MDM is not built to scale. 

Traditional MDM systems are typically built as a monolith and have trouble scaling. Modern MDM is built on a scalable architecture to support a phased approach or to enable agility to respond to changing market conditions. With the ability to add more data attributes on the fly you can scale MDM to bring in more data, which is best practice number one.

Let’s face it, your data model will change over time. It’s important to have a flexible data model that allows you to make changes fast 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 whether a customer is a healthcare provider or frontline worker to offer a special pricing offer. The flexibility that allows quick changes is the key to the digital economy. 

#5 Make master data your data foundation

We mirror best practice number one with this advice: the more business users have access to one single view of master data, the more consistent and real-time information and insights you will have. If you are still feeding data to your various business and analytic applications via siloed MDM systems and data is not updated in real-time, then you are not following modern MDM best practices. 

To be agile and achieve quick time to value when responding to business changes you need a single data management foundation. Think about a scenario if you have to add a new attribute to your customer data model, you have to go and add it to all the siloed master data feeding into various systems, instead if you have one data foundation you can rest assured that the changes will reflect across the organization whether it is for insights or to power business operations.

Graph technology captures relationships and provides fast searches. As businesses focus on transforming the customer experience, the ability to leverage the relationships and connections in data to make timely operational decisions and power them has never been more important. Modern MDM best practices integrate data into every element of the business, which brings us to our next best practice.  For example, consider a publisher has a customer listed as a first-grade teacher working in a certain school, she might also be a parent of a child at another school and serve as chair of a book club drive. Graph technology can connect all these relationships and transactions to help with target marketing for her kid’s book and upsell opportunities at the book club. 

#6 IT is no more the Data custodians

Businesses are making data-driven decisions, everyone in your business is producing and consuming data. Gone are the days when business users have to be dependent on IT for data. If you want your business to be truly data-driven and agile, the data cannot be solely held by the IT department. A master data management solution should be easy for business users to accesses data for insights and operational use and help define data governance rules. 

Business users are ultimately using the data so it is important to empower them to define the data based on their business need, it will improve both your MDM and their data literacy over time. Just as connecting different master data types brings more insights, making data everyone’s responsibility brings more ideas to the table. With a modern, agile MDM, you can continually improve support to real-time operations, shift to customer-centricity, and power regulatory compliance efficiency. 

#7 Continually update data for privacy management and security

GDPR and CCPA were just the beginning of data privacy regulations. As technology produces and incorporates more data, more regulations will arise to protect consumer privacy. Beyond that, critical data is a valuable asset to both hackers who will hold it for ransom and competitors who could use it to gain an advantage. Legacy MDMs with slow updating struggle to respond to customers’ preferences quickly, and they can also require hours or days of downtime for software and security updates.

The best practice for modern MDMs involves automatic background security updates and connected customer data that is continuously updated. Disjointed and disconnected customer data scattered across interaction, transaction and profile systems make this best practice nearly impossible. Modern SaaS MDM platforms make this a best practice by continually updating data for privacy management and security second nature. 

#8 Always look to the future

We center our MDM best practices for business success around including more data because it’s one of the easiest ways to look to the future. More data is coming every day. Seas of unused Big Data exist in your market segment, whether your industry is finance, healthcare, CPG, manufacturing, or B2B. The businesses that use modern MDM to connect master data with valuable unused data to gain new insights will leap ahead of their competitors. If your MDM makes it difficult to add new data sources, it doesn’t power digital innovation and isn’t best-in-class. 

Modern MDM platforms should power business transformation on the operational level as well. Your master data should help you see a clear path to goals and convey unanticipated growth sectors. A legacy MDM system can drag behind business goals because it doesn’t have the capability to integrate fast enough, isn’t built for cloud management, doesn’t incorporate machine learning, and is hard to understand. 

What a Best-in-Class MDM Looks Like 

We’ve set a high bar for the best practices in MDM platforms to power business success. We know many CIOs might read this list, nod their heads and think, “Absolutely. That would be nice to have in an MDM platform, but it isn’t a reality for my business.”

It can be. Here are some key elements that a modern MDM platform should have to be able to achieve these best practices and adhere to the changing master data management trends

  • Multi-domain: Integrates customer, product, location master data, and more.
  • Cloud-native: Not hybrid or converted but built for the cloud.
  • SaaS: To make security and scale automatic.
  • Machine Learning and AI Integrated: To make data governance automatic.
  • Employ Connected Graph: To establish relationships to infinite attributes.
  • Consumer-Quality Interface: to put data in hands of the business users. 

Reltio built a Connected Data Platform with all of these elements and best practices in mind. Our platform was purpose-built to be multi-domain, with a user interface that makes MDM governance intuitive. Machine learning with user verification allows the platform to continually improve matching and make more accurate predictions. We believe in the power of data and the right MDM platform’s ability to power your business transformation. 

To get started with an innovative master data management platform, contact us today.

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