{"id":25436,"date":"2026-09-17T09:00:00","date_gmt":"2026-09-17T16:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/insidetrack\/blog\/?p=25436"},"modified":"2026-09-17T14:30:40","modified_gmt":"2026-09-17T21:30:40","slug":"transforming-the-data-foundation-behind-our-datacenter-expansion-at-microsoft","status":"publish","type":"post","link":"https:\/\/cm-edgetun.pages.dev\/insidetrack\/blog\/transforming-the-data-foundation-behind-our-datacenter-expansion-at-microsoft\/","title":{"rendered":"Transforming the data foundation behind our datacenter expansion at Microsoft"},"content":{"rendered":"\n

The team building our datacenters here at Microsoft needed to get organized.<\/p>\n\n\n\n

Microsoft Cloud Operations + Innovation (CO+I), our organization that plans, builds, and operates the datacenters behind Microsoft Azure, built OneMDM UniVerse to give us a centralized master data platform to manage the materials, suppliers, equipment, and locations behind our datacenter buildout.<\/p>\n\n\n\n

\"A<\/figure>\n\n\n\n
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\u201cEveryone is focused on building really good agents and really good workflows. But all of that begins to fail at scale if you don\u2019t have the foundation set up. Master data management is absolutely that foundation.\u201d<\/p>\nDikul Singh, principal product manager, Microsoft CO+I<\/cite><\/blockquote>\n\n\n\n

Built on Microsoft Dynamics 365, the new platform has a name that addresses our challenge and our solution.<\/p>\n\n\n\n

OneMDM, the first part of the name, is a single master data management system that replaces many of our disparate sources of data. UniVerse, the second part of the name, is the ambition layered on top, because our goal was never to master one data set in isolation. We needed to hold the full hierarchy of our datacenter in a single place, from the cloud it serves down to the individual co-location room.<\/p>\n\n\n\n

\u201cEveryone is focused on building really good agents and really good workflows,\u201d says Dikul Singh, a principal product manager in CO+I. \u201cBut all of that begins to fail at scale if you don\u2019t have the foundation set up. Master data management is absolutely that foundation.\u201d<\/p>\n\n\n\n

100 systems, 100 versions of the truth<\/h2>\n\n\n\n

As our Azure business grew, CO+I grew with it. More than 100 different data systems emerged across planning, supply chain, construction, and operations functions, each with its own copy and its own definitions of the data required to maintain and build new datacenters.<\/p>\n\n\n\n

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\u201cEverybody knew we had to connect everything together, but when you\u2019re running at breakneck speed, it\u2019s very difficult to put things together and standardize.\u201d<\/p>\nPraveen Nair, principal software engineering lead, CO+I<\/cite><\/blockquote>\n\n\n\n

Explosive growth meant our teams across CO+I were pursuing one goal via separate routes, and their data diverged in the same way.<\/p>\n\n\n\n

\u201cEverybody knew we had to connect everything together, but when you\u2019re running at breakneck speed, it\u2019s very difficult to put things together and standardize,\u201d says Praveen Nair, a principal software engineering lead in CO+I.<\/p>\n\n\n\n

Location became the clearest illustration of divergent data. One team would cite the physical address, another the Azure region, another the planning metro, another a position in the org hierarchy. Same datacenter, four different answers.<\/p>\n\n\n\n

\"A<\/figure>\n\n\n\n
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\u201cEmployees would ask, \u2018Who\u2019s the owner for this data? Who\u2019s the steward?\u2019 There was no governance and no data quality at all in what people were consuming.\u201d<\/p>\nKavitha Subramaniam, senior software engineer, CO+I<\/cite><\/blockquote>\n\n\n\n

Inconsistent definitions were not the only problem. Even when teams agreed on what a record should say, no one was accountable for keeping it consistent.<\/p>\n\n\n\n

\u201cEmployees would ask, \u2018Who\u2019s the owner for this data? Who\u2019s the steward?\u2019” says Kavitha Subramaniam, a senior software engineer in CO+I. “There was no governance and no data quality at all in what people were consuming.\u201d<\/p>\n\n\n\n

\"A<\/figure>\n\n\n\n
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\u201cWith Excel files, you don\u2019t know who the data owners are, and if you\u2019re aggregating across a whole bunch of them, it becomes very challenging.\u201d<\/p>\nKaren Albrecht, director of asset inventory management systems, CO+I<\/cite><\/blockquote>\n\n\n\n

On the operations side, the gaps were filled by spreadsheets across roughly 350 campuses and more than 4,000 colocations.<\/p>\n\n\n\n

\u201cWith Excel files, you don\u2019t know who the data owners are, and if you\u2019re aggregating across a whole bunch of them, it becomes very challenging,\u201d says Karen Albrecht, director of asset inventory management systems in CO+I.<\/p>\n\n\n\n

For years, people filled the gaps by hand, reconciling systems record by record. At this size, that manual effort could no longer keep up. The answer had to be a system, not more people.<\/p>\n\n\n\n

Building a platform that teams would actually use<\/h2>\n\n\n\n

We built OneMDM UniVerse on Microsoft Dynamics 365, which serves as the platform’s business application layer. This meant the team did not have to build the fundamentals itself.<\/p>\n\n\n\n

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“There are industry-standard solutions on the market, but they didn\u2019t quite fit the way we\u2019re structured. They would have required a lot of customization, which would become harder to manage and maintain over time.”<\/p>\nVenkatesh Muthiah, principal software engineering manager, CO+I<\/cite><\/blockquote>\n\n\n\n

Security and permissions came built-in, along with data storage and configurable process flows the team could shape to match how each domain approves a record. Microsoft Dataverse sits underneath, making mastered data readable by other services without a custom pipeline for each one.<\/p>\n\n\n\n

That freed the team to focus on the master data problem itself. The packaged products on the market couldn\u2019t do that part for them.<\/p>\n\n\n\n

“There are industry-standard solutions on the market, but they didn\u2019t quite fit the way we\u2019re structured,” says Venkatesh Muthiah, principal software engineering manager in CO+I. \u201cThey would have required a lot of customization, which would become harder to manage and maintain over time.”<\/p>\n\n\n\n

Three principles shaped the platform: Scalability, reusability, and governance. Data enters the product through APIs, integrations, or the user interface. These patterns were built once and then reused as each new system came online.<\/p>\n\n\n\n

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“The most important part of my job is listening to how teams actually work and what they need the platform to do for them. We\u2019re not asking every customer to work exactly the same way. We\u2019re looking for the common requirements we can build into the platform while still supporting the business needs that make each domain different.”<\/p>\nRajesh Yerragunta, principal technical program manager, CO+I<\/cite><\/blockquote>\n\n\n\n

Building something reusable also meant understanding what different teams actually needed from it. Rajesh Yerragunta, a principal technical program manager on the OneMDM team, works directly with customers to understand their requirements and translate them into capabilities the platform can support.<\/p>\n\n\n\n

\u201cThe most important part of my job is listening to how teams actually work and what they need the platform to do for them,\u201d Yerragunta says. \u201cWe\u2019re not asking every customer to work exactly the same way. We\u2019re looking for the common requirements we can build into the platform while still supporting the business needs that make each domain different.\u201d<\/p>\n\n\n\n

That customer input helped the team build flexibility into OneMDM without losing the consistency it was designed to create. <\/p>\n\n\n\n

Governance was designed with each domain rather than imposed on it. We set up a data governance council that named owners and stewards for each domain and defined the approval workflows so that every domain used the same process. Rogue data dumps that previously went through without approval now underwent review, first through an AI screen and then with a human review as a final check.<\/p>\n\n\n\n

The team also used AI to help with ingestion. Exact-match validation had existed before, but it was imperfect\u2014when two pieces of data appeared to be similar but were not exact duplicates, the AI review wouldn\u2019t catch it.<\/p>\n\n\n\n

\u201cNow, the moment I try to create the record, even before governance kicks off, we check for duplicates and run a similarity check,\u201d Subramaniam says. \u201cThen it tells us, \u2018Dublin DC already exists. Do you still want to continue?\u2019\u201d<\/p>\n\n\n\n

Configurable quality rules run continuously, with dashboards showing business users which records are failing and why. Some are authored by the business; others are AI-generated.<\/p>\n\n\n\n

Getting the team aligned came down to agreeing to use the same language.<\/p>\n\n\n\n

\u201cWe had so many ways to label a location, and we still do, but OneMDM was able to connect all of them together,\u201d Singh says. “All of our leaders speak about Azure regions, but we speak about physical locations. Now, whether it\u2019s capital funding, planning, or procurement, there\u2019s no cross-talking.\u201d<\/p>\n\n\n\n

The immediate relief was evident.<\/p>\n\n\n\n

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\u201c<\/em>Every decision we make downstream is only as good as the data underneath it. Master data isn\u2019t glamorous work, but it is the difference between growing fast and growing well. Sponsoring OneMDM was about giving our teams a common foundation so they could stop reconciling numbers and start acting on them.\u201d<\/em><\/p>\nSameer Tarey, partner director, Cloud Infrastructure Engineering<\/cite><\/blockquote>\n\n\n\n

Teams that had spent their time reconciling competing versions of the same record got that time back, and coordination that once required a translator became routine. Now the connections held, because someone owned them.<\/p>\n\n\n\n

For Sameer Tarey, partner director of Cloud Infrastructure Engineering, that alignment was the objective.<\/p>\n\n\n\n

\u201cEvery decision we make downstream is only as good as the data underneath it,\u201d Tarey says. \u201cMaster data isn\u2019t glamorous work, but it is the difference between growing fast and growing well. Sponsoring OneMDM was about giving our teams a common foundation so they could stop reconciling numbers and start acting on them.\u201d<\/p>\n\n\n\n

Standardized equipment records and bills of materials now feed our integrated business planning engine, which tells planners what to buy, where, and when.<\/p>\n\n\n\n

In operations, Albrecht\u2019s team went from updating inventory records one at a time in Excel to updating 100% of the fleet in less than two days.<\/p>\n\n\n\n

\u201cMoving CO+I business data out of Excel and into modern data mastering platforms is transformative because it turns fragmented tracking into a trusted operating system for decisions,\u201d Albrecht says. \u201cIt gives leaders governed, real-time visibility across teams, reduces manual reconciliation, and creates the data foundation we need to scale automation, AI, and measurable business outcomes across global datacenter operations.\u201d<\/p>\n\n\n\n

The adoption challenge<\/h2>\n\n\n\n

Adoption turned out just as challenging as building the platform. Every team that needed to adopt OneMDM already had a working system, a backlog, and deadlines of its own. Asking them to stop, clean up years of accumulated records, and re-point their integrations meant asking them to spend today\u2019s capacity on a payoff that may not manifest for more than a year.<\/p>\n\n\n\n

The team’s first instinct was to standardize by decree. If everyone adopted one definition of region, the mismatches would disappear. But being told to abandon their established systems for something new landed badly.<\/p>\n\n\n\n

\"A<\/figure>\n\n\n\n
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\u201cI took a demo system, created sample materials, showed the entire flow to each group, and asked, ‘Is this what you want to see?’ Going from theoretical alignment to practical realization is what worked.\u201d<\/p>\nNirmala Kummathi, supply chain data manager, Microsoft Global Supply Chain<\/cite><\/blockquote>\n\n\n\n

Only after working through the objection with several teams did the pattern become clear: This was not resistance; it was how each group actually ran its business. The fix was not a single definition imposed from above, but a common level in which everyone could follow the same map.<\/p>\n\n\n\n

Nirmala Kummathi, supply chain data manager in Microsoft Global Supply Chain, describes a three-legged approach for solving this challenge, one that encompassed technology, process, and people. Through trial and error, Kummathi realized that agreeing in principle never worked as well as doing a demo and having a conversation face to face.<\/p>\n\n\n\n

\u201cI took a demo system, created sample materials, showed the entire flow to each group, and asked, ‘Is this what you want to see?’\u201d Kummathi says. \u201cGoing from theoretical alignment to practical realization is what worked.\u201d<\/p>\n\n\n\n

What\u2019s next in master data management<\/h2>\n\n\n\n

Adoption of OneMDM UniVerse continues across our organization, domain by domain. The foundation is already enabling us to accomplish things that weren\u2019t practical before: predictive maintenance signals, conversational access to equipment data, and agent-based workflows built on data the organization can finally trust.<\/p>\n\n\n\n

\u201cJust because I get excited by data doesn\u2019t mean everybody does. You have to figure out what\u2019s in it for them,\u201d Singh says. \u201cIf you want someone to build a ship, you don\u2019t get them excited about building a ship; you teach them to long for the sea. I was selling OneMDM, and everybody said it was boring. So we started selling everything it could enable instead, and people got on board.\u201d<\/p>\n\n\n\n

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Key takeaways<\/h3>\n\n\n\n

Here are some things to keep in mind as you consider a master data initiative at your own organization:<\/p>\n\n\n\n