Developing a Master Data Management Strategy

See if this sounds familiar. Your organization wants to become data driven. It invests in a myriad of tools and technologies, data lakes and catalogs, all with the aim of generating more value from data. But you know that these efforts, while valiant, lack one thing: a master data management strategy.

Having a master data management strategy helps you align business goals with technical objectives, ensuring that the whole organization is on the same page about the definition of success. It will help you decide the best ways to spend your efforts and resources. And on a tactical level, a data mastering strategy also helps ensure that everyone is “speaking the same language” and agrees upon what the data is and is not. 

Without a master data management strategy, your organization will continue to fall short of its goal of becoming truly data-driven. And while developing a master data management strategy may sound daunting, it isn’t all that difficult if you know where to begin. 

How to Develop a Master Data Management Strategy

Every good master data management strategy starts by identifying the challenge and articulating ways to address it. Below, you’ll find the elements your strategy needs and the steps you should follow to create a comprehensive MDM strategy.

1. Business case: Job number one is to create a business case for master data management. Include elements like:

  • Why master data management is important
  • What happens if you fail to invest
  • Which tools and technologies you need to be successful
  • Who should be part of the project team, and
  • How you’ll measure success

Creating a rock-solid business case sets your strategy up for success. Not only does it help you to justify the investment of time and money, but it also helps you articulate the risk – and cost – of doing nothing. 

Further, it helps you secure buy-in from the right leaders across your organization. Buy-in is key to ensuring your organization is committed to making master data management a success. 

2. Deployment plan: Every master data management strategy needs a well-thought-out deployment plan. Consider all the elements you’ll need to take to make your MDM strategy a success. At minimum, make sure your plan includes the following three things:

  • People: bring together a team of people who can help make your strategy a soaring success. Consider colleagues who are passionate about data. Include others who are suffering because you don’t currently have a master data management strategy in place. And reach out to business partners who will ultimately benefit once the master data management strategy is fully-executed. 
  • Process: define the process you’ll follow to execute your plan. Create timelines. Assign roles and responsibilities. Define phases and deliverables. And articulate the outcomes you expect.
  • Technology: every successful master data management strategy has best-in-class technology to support it. So it’s critical that your deployment plan includes a technology assessment. 

3. Architecture scope: Like a solid deployment plan, it’s also important that you define the architecture scope as part of your master data management strategy. Start by prioritizing one or two use cases. Define their requirements. And evaluate which modern MDM solution meets your needs. 

Select a next-generation master data management platform that is cloud-native and takes a machine learning first approach. But also make certain that the technology doesn’t overlook the need for human feedback. Because while machine learning is critical, so is human feedback.

Once you’ve successfully rolled out the first use case, do a retrospective with the team. Understand what went well – and what didn’t. Learn from it. Adjust your plan. And then get started on the next use case.

4. Maintenance/DataOps: The final element of your master data management strategy is maintenance. And this is where you want to consider incorporating a DataOps mindset.

According to Gartner, DataOps is “a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization.” 

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

DataOps will help you maintain your master data management strategy in a way that is efficient and effective. And, it will help you continue to reap the benefits of your master data management strategy for years to come.

Blackstone: A Master Data Management Strategy Example

Leading global investment firm Blackstone implemented a master data management strategy to create golden records of client, property, and other key-asset data across its $600+ billion million portfolio. With over four decades worth of data assets that span systems, platforms, data vendors, and third parties, Blackstone found that not only did the pace of data accumulation accelerate, but the data itself became messier.

Blackstone embarked on a project to create “golden records,” or fully-mastered data, for its key entities, but faced numerous challenges including:

  • Duplicate portfolio company data mixed with client data, resulting from the rapid expansion of the portfolio-company universe.
  • A highly-manual data mastering process that made it difficult to manage the quality and consistency of data.
  • Difficulty in scaling, specifically related to the onboarding of additional, third-party reference datasets.

To overcome these challenges, Blackstone partnered with Tamr to master its customer data at scale. Using Tamr’s modern, cloud-native data mastering solution with human-guided machine learning, Blackstone was able to effectively curate and enrich its customer data at scale, allowing them to create the golden records they desired. They were also able to extend these capabilities to their real estate properties portfolio as well, in a unified, coherent, and scalable way.

Blackstone uses the cloud-native Tamr deployment on AWS which enables the firm to take advantage of the platform’s flexibility and scalability. Tamr is also natively connected with Snowflake, the cloud-based data warehouse, allowing Blackstone to replace the more than 110 SQL servers on-premise that the firm has maintained.

As a result of its master data management strategy and partnership with Tamr, Blackstone has access to accurate data that is readily available for business decision-making. The firm is also able to:

  • Speed up time to value and reduce human effort from data integration to drive higher accuracy.
  • Develop a highly-efficient workflow that enriches the company universe with PREQIN, Capital IQ, Pitchbook, Bloomberg, and other external data.
  • Use a single solution to master multiple entity types, including deals, funds, investment vehicles and properties.

    As you can see, investing in a master data management strategy is critical for data-driven organizations. Not only does it help you align business goals with technical objectives, but it also ensures the entire organization is aligned to the definition of success. 

    By taking the time to identify your challenges and goals, develop the deployment plan and architecture scope, and embrace a DataOps approach, you’ll set your organization on the path to becoming truly data-driven.