Master Data Management: Key Lessons for First-time Users

I recently completed a project that required the use of a master data management (MDM) tool for the first time. Reflecting on this experience, I’m struck by how vital MDM is for organizations dealing with complex data systems. Given its importance, I’m surprised it took me so long to work on a project that needed one.

Proper data management, including MDM, is crucial for ensuring data integrity, visibility, and accessibility across an organization. However, implementing MDM presents challenges, such as organizational inertia and the complexity of merging disjointed data repositories.

If you’ve also never encountered MDM, let me share my experience as a novice. I hope these insights will be useful regardless of your role, whether you’re on a technical team, a product owner, project manager, or someone focused on the big picture.

The global MDM market was valued at approximately $4.11 billion in 2023 and is projected to reach $8.79 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.8% over the forecast period​. According to IDC, the growing complexity of data environments is driving the increasing importance of MDM solutions. As organizations continue to adopt more distributed, diverse, and dynamic data systems, the need for robust MDM solutions that can reconcile multiple versions of the truth becomes crucial​

On this project, I found that the MDM tool served a useful and necessary purpose, but there were costs involved. MDM is needed when there is more than one set of data about an entity, relationship, or interaction distributed across multiple data sources. Without MDM, data discrepancies can emerge over time, leading to inconsistent information across systems.

Real-World Scenarios Requiring MDMIn scenarios like updating an individual’s contact information across systems or merging company databases, discrepancies can easily arise. Without MDM, these inconsistencies lead to miscommunication and operational inefficiencies. MDM tools like Reltio address these challenges by ensuring that all data reflects a unified and accurate view.

Achieving a “Golden Record” with MDM

The primary goal of MDM is to create a “golden record,” a single, authoritative source of truth for any data entity. This golden record is critical for maintaining consistency and accuracy across an organization’s data landscape. In our project, we achieved this with Reltio, a tool that excels in cleansing, enriching, and unifying data to create reliable records in real-time.

Cleansing Data

Reltio utilizes internal processes and external sources to standardize information such as addresses, names, and phone numbers. This step is crucial for ensuring that all data entries conform to a standard format, reducing inconsistencies.

Enriching Data

In addition to cleansing, Reltio enriches data by filling in missing information from external sources. This step enhances the quality and completeness of the data.

Unifying Data

Finally, Reltio unifies data by identifying and consolidating matching entities from different sources. It applies various survivorship rules to determine the most accurate attribute values for each entity, creating a reliable golden record at runtime.

The Technical Side of Using Reltio

As a technical resource, I found Reltio’s SaaS platform, hosted on AWS, straightforward to use, though complex queries often required multiple API calls. For non-technical stakeholders, Reltio offers an intuitive interface that enables them to manage data and workflows without delving into technical details, making it accessible to all team members.

The Costs and Considerations of MDM

While the benefits of MDM are clear—enhanced data quality and unified records—the associated costs cannot be overlooked. These include the financial outlay for SaaS subscriptions and the computational resources required for processing complex queries. Additionally, the challenge of ensuring data security and navigating the complexities of MDM implementation must be carefully managed.

MDM tools like Reltio are invaluable in managing complex data environments, but they should be viewed as transitional solutions. Ultimately, companies will benefit from eliminating duplicate data sources and addressing foundational data structure issues, which can reduce costs, enhance data quality, and improve system efficiency. Long-term reliance on MDM might signal deeper issues that need to be resolved to create a more streamlined and efficient data management process.