How does an MDM project improve your company’s management and productivity?

In an increasingly digitized business ecosystem, the proliferation of business platforms has exacerbated an insidious problem: data fragmentation and silos. Often perceived as a dry or purely technical subject, Master Data Management (MDM)—or reference data management —is, however, emerging as the cornerstone of operational performance, regulatory compliance, and artificial intelligence projects.

Why is this topic gaining so much attention again? What are the signs that an organization’s governance is breaking down, and where should one start in practical terms? Colin ICHAC, Senior Manager of HR Transformation, and Steve NOZAR, Manager of HRIS & HR Data at SQORUS, share their real-world insights and recommendations.

Do the proliferation of SaaS/cloud tools and the rise of AI make MDM even more strategic?

The topic of MDM is not new. And it would be a mistake to believe that this challenge applies only to heterogeneous architectures consisting of dozens of SaaS tools: the problem arises just as much in integrated environments (SAP suites, HCM Cloud, ERP, etc.) whenever the master data is not aligned across different business domains. Two factors are currently accelerating this realization:

• Generative AI and predictive models are now finding their way into every aspect of business. However, the input for AI remains data: feeding an algorithm with unprocessed, outdated, or contradictory data inevitably leads to hallucinations or biased decisions.

• Creating numerous complex interfaces to enable systems to communicate with one another is a financial and technical drain. In contrast, an MDM approach makes it possible to identify a few shared master data records (legal entities, cost centers, individuals, locations) and define logical propagation rules. With a limited and tightly governed foundation, information flows smoothly and automatically throughout the entire application ecosystem.

What are the typical symptoms of a company suffering from “MDM pain,” and which areas are most affected?

A lack of cross-functional governance rarely results in technical alerts that are immediately apparent; rather, it creeps into the day-to-day operations and manifests as operational inefficiencies, particularly between finance and human resources. On the ground, several recurring symptoms indicate a lack of MDM:

• Workflow blockages and emergency processing: This is the classic scenario where a legal entity is created in the financial ERP system to address an immediate business need, but HR was not informed. As a result, People Ops managers find themselves unable to post job openings or generate contracts in their Core HR system. This leads to time-consuming chains of managerial escalations, where local teams have to reach out to a CFO on the other side of the world to unblock a process that should have been anticipated.

• The dilution of data’s functional purpose: without a designated owner, a field gradually loses its original purpose. We regularly see cost centers that have been repurposed over the years to identify physical addresses, logistics sites, or organizational units. Since the data repository is no longer “pure,” it becomes unusable for financial management or HRIS purposes.

• Semantic misunderstandings that cause roadblocks: two departments may use the same word to refer to two incompatible concepts. Example: In a large corporation, a discrepancy in the definition of the term “cluster” (viewed as a geographic grouping of countries by HR, but as a set of cost centers by Finance) was not identified until a year later, and called into question the reliability of the KPIs produced by HR.

• The proliferation of forms and the deterioration of the employee experience: in some organizations, a simple job change requires filling out dozens of fields, most of which remain blank because they were created “just in case.” This digital overhead creates unnecessary mental strain and genuine distress at work. More than just a physical office, the digital environment shapes an employee’s experience: rigid systems and incomprehensible processes are now a direct cause of disengagement among many key managers.

• Audit and compliance risks: Whether it’s social security filings, NIS2 requirements, the GDPR, or sustainability reports (CSRD), auditors now review headcount, FTE, and diversity data. Attempting to reconcile these metrics using makeshift solutions in Excel exposes the company to non-compliance that could result in penalties.

How can we get business teams on board without making the topic seem like a purely IT-related constraint?

To be successful, a reference data project must never be presented as a technical requirement imposed by the IT department. It affects the inner workings of departments and requires a profound cultural shift. To engage all stakeholders, three pillars are essential:

• Establishing sponsorship: MDM requires balancing sometimes competing priorities—between a Finance department focused on its accounting schedule and an HR department committed to protecting employee confidentiality. Only senior management can provide the big-picture perspective, set common goals, and authorize the partial “relief” of operational staff so they can devote time to the project.

• Start with the pain point: rather than approaching the subject through theoretical modeling concepts, it’s better to focus on everyday frustrations—such as constant follow-ups, hastily cobbled-together Excel spreadsheets before each closing, or rejected workflows. Showing that MDM frees up time and puts an end to crisis meetings is the best way to gain buy-in.

• Rethinking the role of IT as a facilitator: IT is responsible for infrastructure, security, and overall compliance. But the meaning of the data lies with the business units. Establishing shared governance that brings together representatives from HR, Finance, Procurement, and Operations helps create clear ground rules that everyone accepts, thereby moving away from a culture of secrecy.

What advice would you give to a company that is aware of its data silos but doesn’t know where to start?

Attempting to map the entire data estate using a “Big Bang” approach is the surest way to derail the project. Feedback from past experiences consistently points to a gradual, agile, and value-focused approach.

Here are our practical recommendations for laying the foundation:

• Adopt the “Light is right” engineering principle: always design a data model that is simple, lightweight, and scalable. Complexity arises naturally as the business grows (new subsidiaries, international expansion, mergers and acquisitions). Don’t create fields without a clear purpose: trim away what’s superfluous and focus on the essential common foundation.

• Distinguishing between the central and the local levels: the central system is not intended to absorb all local specificities. Each level must retain its own relevance. The example of gender management in the workplace is emblematic: distinguishing in the reference framework between legal gender (required for group consolidation and reporting obligations) and self-identified or declared gender allows organizations to meet both legal requirements and local inclusion policies without creating friction for managers during the hiring process.

• Involve frontline staff in the project team: don’t make the team up solely of managers or consultants. Having a payroll manager or administrative manager who enters data on a daily basis is crucial: they highlight hidden constraints and become key facilitators of data literacy.

• Establishing sustainable roles: deploying a tool is just one step. The real challenge lies in keeping it operational. Appointing Data Stewards to ensure the relevance, timeliness, and accuracy of the data, as well as HR Data Managers who collaborate with HRIS managers, guarantees the model’s long-term sustainability.

What metrics or arguments can be used to concretely demonstrate the ROI of an MDM project?

Although initially viewed as an invisible infrastructure investment, Master Data Management generates tangible and measurable returns on investment at various levels of the organization:

• Real-time management for the Executive Committee: A robust MDM solution provides the executive committee with a unified dashboard featuring reliable, comparable, and instantly available KPIs.

• Operational excellence and the reduction of hidden costs: eliminating data entry errors, removing duplicates, and automating workflows between ERP, CRM, and HRIS systems drastically reduce the time spent resolving urgent issues. Business teams can finally refocus on tasks that deliver real added value.

• Confidence in the face of audits and non-financial requirements: the ability to immediately demonstrate the traceability and accuracy of headcount, total payroll, or ESG metrics to auditors strengthens corporate governance and enhances the company’s reputation.

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