Data Quality and MDM

The significance of Data Quality and Master Data Management (MDM) is apparent. If the information they use is accurate, people will only make the right data-driven decisions. Data is virtually worthless and often even hazardous without adequate data quality. But despite its meaning, the truth looks pretty grim in many of today’s organizations. In this article, we will discuss the importance and relationship between data quality and MDM.

Master Data Management (MDM) is the method of generating and maintaining quality data. An entity can have a single master copy of its master data, such as data on clients, products, items, etc. Usually, non-transactional data within the company is referred to as a master, and such data can include clients, vendors, staff, goods, etc.

Let’s discuss the importance and relationship between data quality and MDM

Importance of Master Data Management (MDM)

There are many cases of incidents where low data quality has caused many companies with a lot of pain. How many times have you noticed misspelled versions of your name stored inside the same company against two different accounts? MDM is not a new idea. However, in a fast-paced business environment where businesses can not afford to lose clients because of data problems, it has become increasingly necessary. MDM is important for large organizations because it offers the company a single version of the reality of their master data.

The Complexity of Maintaining Data Quality in Organizations

Data Quality is the concern where, in most situations, data collected in the systems is not of the largest quality due to human errors. First of all, individuals are not the highest paying individuals in companies doing data entry. Furthermore, they don’t care if they don’t have incentives to collect useful quality data.  Capturing incorrect email emails, addresses, etc., is a common mistake that individuals make in organizations. And that is why there have been so many data quality tools around for address correction and items of that nature.

When several IT systems within the company maintain the same information differently, the problem is compounded. Now in one system, correcting consistency doesn’t correct the other automatically. To make matters worse, think about a big company purchasing another big company. The other company may carry a lot of information that needs incorporation now. This is an immense task from a data quality perspective and controlling the quality of data after the merger. This is an introduction to know about the importance and relationship between data quality and MDM.

Master Data Management for Single Source of Reality

It is one thing to have high-quality data. But there is another way to handle the information across structures and when dealing with mergers and acquisitions. And that is where MDM comes into the equation. MDM’s principle is to establish a single master data hub to provide a single, authoritative source of master data. The master data feeds into other IT systems within that organization (customers, vendors, products, employees, etc.).

How Data Quality and MDM Are Related?

Initiatives for Master Data Management (MDM) and Data Quality are closely related.

An MDM solution cannot be required by small organizations, whereas large organizations with sizeable master data must consider implementing it. Even without considering MDM, it is possible to provide a data quality initiative; the reverse is impossible. Any initiative to implement MDM must have a portion of data quality to it.

If there is an MDM drive on the outlook, data quality should be regarded as a requirement for any Master Data Management initiative. The road to MDM starts with data quality. And the discovery of master data, profiling, and analysis is the starting point for any data quality process.

Depending on the case, there are three different ways that Data Quality (DQ) and Master Data Management (MDM) can get implementation:

  • Implementation of data quality separately where either MDM is not implemented or is not in consideration in the near term.
  • Implementing Data Quality as the first step towards MDM, first implementing Data Quality, and then implementing MDM as a different next step. This enables data correction before MDM implementation status across all applications, a popular approach in many businesses.
  • Implementation of data quality following the implementation of an MDM. In these instances, as part of the MDM initiative, cleaning up data occurs. To make this possible, in their MDM suite of tools, several MDM vendors have begun to come up with bundled data quality tools.

Data quality is an essential feature of the MDM initiative. The MDM solution is in the application in a single master data object or all operational master and reference data. An MDM project is also not something that any organization’s IT department can be expected to conduct in isolation. It needs the full participation of both IT and business staff. It takes all data quality and MDM implementation stages, Data Governance teams, Data Stewards, and business teams.

For any MDM project initiative, business involvement is crucial simply. As it is only business users who can understand and identify what the Data Quality Level is for the organization. And what makes sense and what does not? Yes, to make MDM happen, IT needs extensive involvement. But it’s the organization that is pushing the implementation.


Data quality is essential to any MDM implementation, even within organizations that don’t need MDM; it may also occur independently. To fully leverage the power of Master Data Management, MDM solutions need IT and business teams to work together to identify data quality requirements.


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