This blog is co-authored by Bharat Banhatti, MDM product owner, Sanofi.

Key takeaways

• MDM has evolved from a passive system of record into an active system of value.
• Pre-MDM preparation helps improve data quality before it enters the core MDM environment.
• AI success depends on trusted entity resolution, governance and reusable data products.
• Launch readiness should be designed into MDM architecture—not addressed reactively.
• A connected ecosystem across pre-MDM, core MDM and post-MDM activation helps organizations move faster, scale more easily and unlock greater value from their data.

For years, master data management (MDM) has been treated as a foundational but largely back-office capability. It helped organizations consolidate records, reduce duplication and create trusted views of critical entities such as healthcare providers, accounts and affiliations. But as life sciences organizations become more data-driven, that definition is no longer enough.

Today, MDM needs to do more than maintain the truth. It needs to make that truth usable: feeding analytics, enabling AI, supporting field execution and helping teams move faster during moments that matter, from launches to market expansions.

Bharat Banhatti, U.S. MDM product owner at Sanofi, and Sumeet Nisale, director, platforms and products at ZS, sat down for a webinar to talk about how Sanofi is advancing its modern MDM journey using Reltio and how ZAIDYN^® complements the ecosystem through pre-MDM ingestion, enrichment and downstream activation.

Their discussion made one thing clear: The future of MDM is not just about cleaner data. It is about building a connected, governed and reusable data foundation that turns master data into business value.

Below are highlights from the conversation. Watch the full webinar here.

Moderator: Life sciences organizations have invested in MDM for years. What does it mean to move beyond a traditional system of record?

Sumeet Nisale (SN): The traditional view of MDM was centered on creating and maintaining the golden record. That was important, and it still is. But the expectations from business teams have changed. They do not just want accurate data sitting in a central system. They want data that helps them make decisions, power workflows and act with confidence.

A modern MDM program has to connect the full journey—from how data enters the ecosystem to how it is governed, mastered and ultimately activated. If MDM stops creating a record, it creates only partial value. The real opportunity is to make that mastered data available to downstream teams in a way that supports analytics, engagement, planning and execution.

In that sense, MDM is evolving from a system of record to a system of value. It is not only about answering, “What is the right record?” It is about answering, “How does this record help the business move faster and make better decisions?”

Bharat Banhatti (BB): For Sanofi, the focus is on making MDM more actionable for the business. The goal is not for business teams to spend time worrying about definitions, data quality rules or whether the data is ready. The goal is for them to focus on outcomes.

That requires a stronger foundation upstream and a better connection downstream. When data is standardized, enriched and governed before it reaches MDM—and then activated effectively after mastering—the system becomes much more than a repository. It becomes part of how the organization operates.

Moderator: Why is the pre-MDM layer becoming so important?

BB: A lot of MDM challenges do not begin inside the MDM system. They begin before the data even gets there. If incoming data is inconsistent, incomplete or poorly structured, the MDM system has to absorb that complexity. Over time, that increases manual work, slows onboarding and creates friction for teams that need speed.

That is where the pre-MDM layer becomes important. It helps standardize and prepare data before it enters the core MDM environment. When quality is improved earlier in the process, organizations can load new data sources more quickly and with much less configuration.

For product launches, this becomes especially valuable. Launches often bring new HCPs, new affiliations, new target segments and new data sources into the ecosystem at the same time. If teams are still cleaning and reconciling data when the business needs to act, MDM can quickly become a bottleneck.

SN: Pre-MDM also changes the economics of scale. Instead of treating every new source as a custom integration effort, organizations can create repeatable ingestion and enrichment patterns. That makes the overall architecture more flexible, especially as companies grow across brands, indications and markets.

Moderator: How does MDM support AI and advanced analytics?

SN: AI depends heavily on the quality of the data underneath it. If the data foundation is fragmented, AI outputs will reflect that fragmentation. This is one of the hidden reasons many AI pilots struggle to move into production. The model may be sophisticated, but if entity resolution, governance and data quality are weak, the output will not be reliable enough for scaled business use.

A well-governed, unified HCP or account profile allows next-best-action models to personalize at scale. It is also what allows a generative AI assistant to surface relevant insights for a rep without relying on incomplete or inconsistent information.

Without clean and trusted entity resolution underneath, AI outputs are only as reliable as the data feeding them. That is often the hidden reason AI pilots do not make it to production.

The shift toward data productization makes this even more important. When MDM is designed around reusable, well-documented data products, analytics and AI teams can build repeatedly on the same trusted foundation instead of rebuilding one-off extracts for every use case.

Moderator: How do organizations prevent MDM from slowing teams down during launches or major market events?

SN: This is where early architecture decisions matter. If an MDM environment is built around batch processing and manual stewardship queues, a product launch will expose those weaknesses very quickly. Launches create pressure: new customers, new affiliations, new segments and new operational requirements can all hit the system at once.

Modern MDM needs to be built for that kind of intensity. Event-driven processing, automated onboarding workflows and reusable data pipelines can help absorb launch-scale volumes without slowing execution.

This is also where the investment in pre-MDM and downstream activation pays off. If upstream data is prepared correctly and downstream systems are ready to consume mastered data, launch teams can move faster with greater confidence.

BB: For Sanofi, the goal is to reduce the amount of operational friction that business teams experience. When data is prepared and standardized before entering MDM, teams can bring in new sources more quickly. That speed matters during launches, when delays in data readiness can affect field execution and decision-making.

Moderator: What does a strong end-to-end MDM ecosystem look like?

SN: A strong ecosystem has three connected layers. The first is pre-MDM, where data is ingested, standardized, and enriched. The second is core MDM, where matching, survivorship, governance and golden record creation happen. The third is post-MDM activation, where mastered data is made available to the systems and teams that need it.

The mistake many organizations make is treating these as separate steps. But value is created when the layers work together. If upstream data is poor, core MDM becomes overloaded. If downstream activation is weak, mastered data does not reach the business in a usable way.

A platform approach helps connect these layers more effectively. It gives organizations a way to scale, reuse and extend their data foundation without constantly rebuilding.

BB: That connected approach is critical. MDM cannot be isolated from how the business operates. It has to support the realities of launches, field execution, customer engagement and analytics. When the ecosystem is connected, MDM becomes part of the business workflow—not just an IT process.

Moderator: What should biopharma leaders take away from Sanofi’s MDM journey?

SN: The biggest takeaway is that MDM should be designed with business value in mind from the start. Clean records are important, but they are not the end goal. The end goal is trusted, connected data that can power decisions, support AI and help teams act faster.

Organizations should also think about scalability early. MDM programs built for one brand, one market or one-use cases often become difficult to extend. A modular, reusable architecture helps avoid technical debt.

BB: For organizations starting or modernizing their MDM journey, the lesson is to invest early in the right foundation. That means thinking beyond the MDM system itself and considering how data is prepared before it enters MDM and how it is used after it leaves.

The value of MDM is not only in the record. It is what the organization can do with that record.

Watch the full webinar here