Master Data Management: Common Misconceptions You Should Know

When most people think of master data management, they first think of customers and products. This is logical, as the core mission of any company is to develop products and services, find the right customers, and consistently deliver excellence.

But master data encompasses so much more than data about customers and products. It includes information about suppliers, employees, and target prospects. It requires reference data such as geographical subdivisions and market segments. It includes financial data such as a chart of accounts, cost centers, and price lists. The business must also manage locations, including warehouses, offices, and subsidiaries, not to mention the various addresses associated with virtually every data element the business manages.

Controlling and governing this broad array of information enables your organization to:

• Make data-driven decisions
• Improve operational processes
• Manage risk and compliance. 

As data sources grow more diverse and complex, and as the volume and velocity of data increase exponentially, effective master data management becomes imperative.

Challenges of Master Data Management

A decade ago, master data management (MDM) was a much simpler proposition than it is today. Four main challenges make MDM complex.

1. Poor enterprise-wide data literacy: This problem may manifest itself in various ways, but it can be as simple as inconsistency in the terminology used by different groups within your organization. If two departments use the same term to refer to two completely different data elements, for example, that prevents a clear, common understanding of what the data means.

2. Lack of data readiness: As modern MDM practices have emerged, the standard for true data readiness has grown higher. In the past, it was enough to have data that was complete and that conformed to the defined standards for specific business applications. Today, data must be trustworthy, timely, and readily accessible to the right people.

3. Data risk exposure: When organizations lack adequate control over their master data, they will likewise have an incomplete view of data lineage. They don’t know who changed a record, or when it was changed. Organizations may not know what data needs to be protected, or in what ways. They may not know which data needs to be retained, and which should be discarded to comply with data regulations.

4. Unsustainable processes: Manual processes and complex workflows create delays. Data updates that lag behind reality can hamper your ability to operate at the speed of business. To achieve sustainable data processes, organizations must draw clear lines of responsibility with respect to custodianship (who maintains the data) and ownership (who makes decisions about the data). Without clear lines of demarcation, the problem of unsustainable processes gets even worse.

These four challenges are inextricably linked to one another. Poor data literacy, for example, leads to a lack of data readiness. That, in turn, exposes your organization to risk. As the volume and velocity of data increase, those challenges grow worse and contribute to the problem of unsustainability.

Common Misconceptions About Master Data Management

Most people think of MDM as a means of systematically matching and deduplicating records across multiple databases and applications, but modern MDM plays a far more meaningful role.

Ultimately, MDM initiatives should be driven by business value, rather than being a purely technical exercise. MDM impacts products and services, supply chain management, and business operations. As more organizations tap into the value of advanced analytics and AI, MDM has emerged as a vital element for trusted data and confident decisions.

Yet misconceptions about MDM persist. Here are some of the most prevalent myths:

#1. “We don’t need MDM because we have an ERP.”

Enterprise-level organizations have an ERP system, and for most, it serves as the so-called “system of record” for much of the company’s master data. Alongside ERP, most have a CRM system for managing prospects, customers, sales quotations, and the like. With either of these serving as the single source of truth, why would a company need MDM?

MDM is necessary to align these multiple systems and bring order to the overall landscape. An ERP does not do data quality very well. CRM’s, likewise, does a poor job of undating data according to consistent standards. MDM serves a critical role in ensuring that these systems perform their core jobs effectively.

#2. “MDM is just (fill in the blank with a prevailing theory).”

Very often, key business users conflate MDM with various tasks or components of data science and data management. Some, for example, think of it as a tool that facilitates integration. Others regard it as a data modeling platform. Still others think of MDM as a merge-and-match exercise, a data quality tool, or a workflow engine. While MDM touches on all of these, it takes on a far greater role in modern data-driven organizations.

#3. “MDM is a purely technical exercise.”

Organizations that approach MDM as a purely technical exercise risk falling far short of their potential, failing to involve business users in the process of defining data quality rules, placing MDM properly in the organization, and sustaining a long-term commitment to making it successful. MDM will pay for itself in the course of a single project, but its true potential is much greater. When internal users understand the potential value and reach of MDM, ROI can be sustained over the long term.

#4. “MDM is expensive.”

If internal champions are successful in communicating the real value of MDM, then stakeholders will understand that it’s a great investment. Applied to specific projects and initiatives, MDM improves results.

While those benefits may be indirect, they should nevertheless be quantified to the greatest extent possible, demonstrating the value added to each new initiative along the way. Viewed from a different angle, the most expensive course of action is to neglect master data management, undermining data integrity and creating a barrier to confident data-driven decisions.

#5. “MDM is just for large organizations.”

Generally speaking, economies of scale benefit large organizations. But in a world that increasingly competes on data competency, smaller organizations can use effective data management as an equalizer. Their initial investment may be smaller and more focused, but MDM adds value that will rapidly produce benefits for organizations of all sizes.

#6. “MDM will fix all our data problems instantly.”

MDM is a journey. So too are data quality and data integrity. In fact, any effective data initiative requires organizational commitment, backed with the right enterprise-grade technology. The early stages of a company’s MDM journey typically call for a steady commitment, followed by increasing momentum and growing benefits.

#7. “MDM is another downstream data warehouse.”

MDM is operational, not analytical. It doesn’t incorporate transactional data, and it’s not a foundation for reporting and analytics. MDM is real-time. Many stakeholders seem to believe that MDM is downstream from operations. In fact, it is a key enabler of efficient, accurate operations.

#8. “MDM is for customer and product data only.”

This myth is often paired with misconception #1. Those who believe MDM is just about products and customers are unlikely to understand the full range of benefits because they simply can’t see the bigger picture. MDM ensures effective control over a broad array of enterprise datasets.

The MDM Imperative

MDM goes hand-in-hand with data integrity. To achieve your goals, you need maximum accuracy, consistency, and context that can support confident data-driven decisions.

Data integrity doesn’t happen overnight; it’s a journey. The specific roadmap is different for every organization. It depends on a firm’s strategic priorities and the business initiatives that matter most. In every instance, however, MDM is a vital ingredient in the process.

Want to learn more about how to roll out an effective MDM program in your organization? This ebook explores how data can be siloed across various domains which holds you back from maximizing its value. Here’s a MDM checklist you need. Master Data Management (MDM) Checklist: 3 Keys to Success.