Master Data Management – often abbreviated as MDM – already sounds outdated as a term. The MDM hype peaked more than a decade ago, but the underlying need has not disappeared: neglected master data management tends to resurface painfully in projects. AI in particular relies heavily on data, and its demand is not satisfied by volume alone, and the data must also be consistent and reliable. This is especially critical for master data, which is used across processes and to which key business events are anchored. Poor-quality master data has wide-ranging effects across the entire organization.
Let’s start with a few hard facts:- 93% of business decision-makers consider reliable and high-quality data critical (Experian).
- Yet only 46% of business decision-makers truly trust the quality of their data (Business Wire).
- 71% of organizations spend at least a quarter of their working time preparing data for reporting and decision-making (Business Wire).
- Organizations that combine MDM with a data governance model achieve up to three times the return on their data investments compared to those that implement MDM purely as a technology initiative (Kearney).
In this light it's clear that data quality and governance are essential. AI and automation only sharpen the challenge: if data is not in order, automation will generate incorrect conclusions at an even faster pace. A traditional master data project is not the only way to achieve sufficient quality, but in any case, both the builders and users of data environments must be quality-aware data consumers. A well-executed master data initiative shifts pressure from daily operations to the early stages, where remediation and investigation work can be prioritized in a more controlled and less disruptive manner.
Master data is not dead, but it has evolved
Master data refers to an organization’s core data assets, such as customer, product, and supplier data, which form the foundation for business processes, reporting, and analytics. Master data management ensures that data accurately reflects the real-world entities it represents, such as a customer or a contract. Poor master data quality is rarely identified early or even if it is, it often fails to trigger corrective action.
Quality issues typically surface too late, for example during application or system testing phases, and even then, fixing them is often deprioritized. Alternatively, they extend project timelines and increase costs – outcomes that could have been avoided with proactive quality management.
When AI is expected to operate across the enterprise, data becomes a strategic asset. Cloud-based and AI-driven data platforms make it easier to improve data quality, but the core of master data management is still fundamentally about understanding the business: whether human or AI-driven, one must understand how organizational data is structured, where it originates, where it is maintained, and what ultimately constitutes high-quality data.
Solutions such as Microsoft Fabric also enable distributed master data management. This reflects a clear shift away from massive, centralized MDM programs toward smaller, business-driven, and modular solutions. In a sense, master data management is unavoidable: if you choose not to implement an MDM initiative for customer data, you may still find yourself running a sales-driven “Customer 360” project that delivers essentially the same outcomes. When working with critical systems and core information assets, data quality issues are difficult to avoid.
Doing things in advance is not always wise, but in the case of master data, it often is. Time invested in clarifying customer, product, or contract data is rarely wasted, as this information will continue to be required across processes. By nature, master data cuts across systems and workflows. Investing in harmonized and high-quality master data therefore increases organizational responsiveness to both market changes and emerging technological opportunities.
Well-managed master data enables data-driven decision-making, innovation, and improved customer experiences. Efficiency gains, automation, and higher levels of self-service are also common reasons to invest in master data quality – especially now, as agents and AI are entering nearly every process. Conversely, poor master data quality manifests as delays, errors, and rising costs. Investing in data quality pays off: according to Kearney, organizations can achieve cost savings of up to 40% within 12–18 months when clear quality requirements are defined for master data.
Clear ownership is essential for master data
Master data requires strong ownership. Distributed ownership tends to disappear within organizational structures, and decision-making slows as data quality deteriorates. Each master data domain (such as customer or supplier data) needs a clearly defined owner responsible for data quality, timeliness, and proper governance. Clear boundaries are important even when they are difficult to establish, and responsibility should not be avoided: when “everyone owns the data,” no one truly does.
Based on our experience, data ownership should reside in the business rather than in IT. A business-oriented owner is best positioned to assess data quality and structure. They understand how data should evolve to support business objectives, minimize risks, and enable various use cases. This expertise and responsiveness support all processes and development initiatives that rely on the data. This is particularly important in complex organizations where the same data is widely used across multiple processes. Even a single poorly structured or weakly owned data domain can slow down development across the entire organization.
What is the new direction of master data management?
Master data management is now built on flexible and distributed solutions where data is integrated into everyday business operations and development. Governance should be decentralized to the business according to who actually understands and is responsible for each data domain. This is no longer about a one-off MDM project; modern master data management is a continuous effort to maintain situational awareness and competitive advantage.
Effective use of AI and automation requires high-quality, harmonized master data. AI can also support this effort by automating data quality monitoring and enrichment. Master data management is, above all, a strategic investment that lays the foundation for future success.
Success looks different in different organizations. In one case, sales gains a more complete view of customers, enabling critical deals to close in record time. In another, a coherent view of employee competencies improves resource allocation by tens of percent. A third organization reduces procurement costs by a quarter by gaining better insight into total material consumption and leveraging volume discounts to support production cost savings. In a fourth case, processes and automation accelerate significantly when agents, applications, and self-service channels can rely on up-to-date and reliable master data – especially as customers increasingly expect near real-time responsiveness.
In all of these cases, master data has provided a solid and scalable foundation that enables innovation and strengthens competitiveness across business domains. The benefits are appreciated by finance teams, report analysts, and agents alike.
Struggling with data quality? Not sure how to organize data governance? If you need guidance, sparring, or extra hands to ensure data quality, we can help. Norrin’s service portfolio includes both traditional master data management projects, the implementation of modern data and AI governance models, and the practical execution of data harmonization. Contact us or send a message to: myynti@norrin.com!
