Tamr Insights
Tamr Insights
The Leader in Data Products
May 19, 2022

8 Master Data Management Best Practices

8 Master Data Management Best Practices

Your business is powered by data. Lots of data. And that data is growing larger, faster, and more diverse by the minute. But if we were to wager a guess, much of that data is unclean and inconsistent, living in multiple systems and swampy data lakes across your organization.

In order to free your data and make it usable across the business, your organization needs to embrace modern master data management (MDM). Modern MDM is defined as “a set of tools and processes used to define and manage an organization’s critical data and to provide an integration of data to a single point of reference, also known as Golden Record.

Businesses use modern MDM to have clean, curated, comprehensive data about business-critical entities such as suppliers, customers, products, and parts so they can better support analytics and business decision-making. Said differently, they want a single version of the truth.

Implementing modern MDM takes work. It requires a strategy. And leadership buy-in. But it also requires your organization to embrace a set of master data management best practices that set you off on the road to success.

8 Master Data Management Best Practices

1. Start with the business use case

Defining the business use case you want to tackle first is priority #1 when you’re getting started with modern MDM. There are many places you can start – from customer data to product data to data about parts.

Decide which use case is the best place for your organization to begin, and then build out the requirements. Talk to the business stakeholders. Understand their challenges and needs. And build relationships with them. After all, their support is key to your

2. Adopt a machine learning approach

Many master data management solutions employ a traditional approach to mastering data. But these solutions are rigid and inflexible. And they struggle to keep up with the accelerating volume, velocity, and variety of your data.

Instead, look for solutions that use a machine learning-first approach to MDM. Machine learning actually improves with more data. And it frees up your valuable technical resources by matching data in an automated, scalable way.

3. Ensure there is sufficient human input

This MDM best practice goes hand-in-hand with the one above. While machine learning is critical, so is human input. Make sure you are engaging your business users and subject matter experts in the process. They are invaluable when it comes to providing feedback on the machine learning models and ensuring accuracy and relevancy.

Not only will human input improve your machine learning models, but it will also foster tighter alignment between the data and business outcomes that require curated data.

4. Enrich the data

Data enrichment integrates your internal data assets with external data in order to increase the value of these assets. It adds additional relevant or missing information so that the data is more complete – and more usable. External enrichment data vary by industry, but include data from providers such as Dun & Bradstreet and Companies House for corporate and legal entity information and IQVIA and Verinovum for healthcare data.

By enriching your data, you’ll improve its quality, making it a more valuable asset to your organization. But keep in mind that enrichment should occur after your existing data foundation is in shape. Otherwise, you will not reap the intended results.

5. Employ an agile approach

When software development began embracing agile methodologies, its value to the business skyrocketed. That’s why we believe a MDM best practice is to embrace DataOps. hen software development began embracing agile methodologies, its value to the business skyrocketed. That’s why we believe a MDM best practice is to embrace DataOps.

DataOps acknowledges the interconnected nature of data engineering, data integration, data quality, and data security/privacy. It aims to help organizations rapidly deliver data that not only accelerates analytics but also enables analytics that were previously deemed impossible. DataOps provides a myriad of benefits ranging from “faster cycle times” to “fewer defects and errors” to “happier customers.” (source)

By adopting DataOps, your organization will have in place the practices, processes, and technologies needed to accelerate the delivery of analytics. You’ll bring rigor to the development and management of data pipelines. And you’ll enable CI/CD across your data ecosystem.

6. Don’t boil the ocean

When organizations first embark on a master data management initiative, they tend to want to “boil the ocean.” And we get it. Excitement is high. The possibilities of clean, curated, comprehensive data are endless. And you want to do it all.

But instead, we advise that you pause and take a deep breath. Follow best practice #1 and decide which use case you’ll start with. Clearly define its scope and secure buy-in from leadership to proceed. Focus your energy and resources on making this use case a success. And then expand from there.

7. Architect within a best-of-breed ecosystem

Historically, many companies wanted to use a single vendor for all things related to data. We believe this is a significant (and universal) blunder – and one you should avoid at all cost. Why? Because these single-vendor solutions lack the depth needed to solve your complex (and evolving!) data challenges.

Rather than relying on solutions from a single vendor, a master data management best practice is to look for solutions that are the best fit for the job at hand. When evaluating MDM solution providers, look for ones with an open and interoperable architecture. You want a modern MDM solution that complements your current technology stack through RESTful APIs and robust integration.

8. Fit into a cloud strategy

Our final MDM best practice helps to ensure that you are mastering data with an eye towards the future. We all can agree that the three V’s of data – volume, velocity, and variety – are becoming an increasingly important element to consider in your master data management strategy. That’s why you should look for cloud-native capabilities that utilize the built-in elastic and ephemeral cloud and compute benefits of cloud technologies.

The built-in advantage of cloud technologies and fitting into a cloud strategy is that you’ll reduce the total cost of ownership of your MDM project. In addition, you’ll reap the benefits of a highly-secure, scalable infrastructure that allows you to add additional storage and compute power without increasing physical and hosting costs.

By adopting these eight master data management best practices, you can feel confident that you’re setting your organization up to realize all of the benefits that modern MDM has to offer.