The Relationship Between Data Quality and Master Data Management

Master Data Management (MDM) is the process of creating and managing quality data in such a way that an organization can have a single master copy of its master data; such as customer, product, items data etc.

Usually, non-transactional data are referred to as master within the organization and such data can include customers, suppliers, employees, products etc.

Why is Master Data Management (MDM) Important?

There are numerous examples of incidents where poor data quality has caused a lot of pain for many organizations. How many times have you found misspelt versions of your own name stored against two different accounts within the same company?

I have been in situations where I have booked multiple rooms in one single booking in hotels only to find that each room had a different spellings of my name and the operator could not find easily my bookings.

MDM is not a new concept. However, it has become increasingly important in a fast paced business atmosphere where companies can’t afford to lose customers because of data issues. MDM is important for large companies because it offers the enterprise a single version of the truth of their master data.

The Challenge of Maintaining Data Quality in Organizations

Data Quality is the issue where data captured in the systems is not of the highest quality due to human errors, in most cases. First of all, people in organizations doing data entry are not the highest paid people in the company. In addition, if they do not have incentives for capturing high quality data, they don’t care.

For example, capturing of incorrect email addresses, addresses, etc. is a common mistake people make in organizations and that is why so many data quality tools have been around for address correction and things of that nature.

The problem compounds (and it’s common as well,) when multiple IT systems within the organization maintain the same information differently. Now, fixing quality in one system doesn’t automatically fix the other.

To make the matters worse, think of a large company acquiring another large company bringing a lot of data which now needs to be integrated. This is a huge challenge from a data quality perspective and in being able to manage the quality of data after the merger.

Master Data Management for Single Source of Truth

Having high quality data is one thing. But being able to manage that data across systems and even when faced with mergers and acquisitions is another and that is where Master Data Management (MDM) comes into the picture.

The idea behind MDM is to create a single master data hub to provide a single, authoritative source of master data (Customers, Vendors, Products, and Employees etc.) that feed into other IT systems within that organization.

How Data Quality and Master Data Management Are Related

If I have not already put you to sleep, and you are still reading, I want to bring your attention to the fact that Master Data Management (MDM) initiatives and Data Quality are intimately linked.

Small organizations may not need an MDM solution, while large organizations with sizable master data must consider implementing it.

Having said that, while it is possible to have a data quality initiative without considering MDM, the reverse is not possible. Every MDM implementation initiative must have a data quality component to it.

I think that Data Quality should be considered as a prerequisite to any Master Data Management initiative if there is an MDM initiative on the horizon. The path to MDM begins with quality of data, and the starting point for any data quality process is a discovery of master data, profiling, and analysis.

With that in mind, here are three different ways Data Quality (DQ) and Master Data Management (MDM) can be implemented, depending upon the situation:

  1. Implementation of Data Quality independently where MDM is either not being implemented or not being considered in the near term.
  2. Implementing Data Quality as a first step towards MDM where Data Quality is implemented first and then MDM implementation begins as a separate next step. This allows data correction across all applications before MDM implementation status, which is a common approach in many companies.
  3. Implementing Data Quality in parallel with an MDM implementation. In these cases, cleaning up of data happens as part of the MDM initiative. To make this happen, many MDM vendors have started to come up with bundled data quality tools in their MDM suite of tools.

Whether the MDM solution is being implemented in a single master data object (e.g., customer or product,) or for all operational master and reference data, data quality is an important aspect of MDM initiative.

Also, an MDM project is not something the IT department of any organization can be expected to execute in isolation. It requires the full involvement of both IT and business personnel bringing Data Governance teams, Data Stewards and business teams in all phases of the data quality and MDM implementation.

Involvement of business is critical to any MDM project initiative simply because it is only business users who can truly understand and define what the Data Quality standard is for the company, what makes sense and what doesn’t. Yes, IT is to be heavily involved in making MDM happen. But it’s business who is to drive the implementation.


Data Quality is critical to every MDM implementation, although it can also exist independently within organizations that do not need MDM. At the same time, MDM solutions need IT teams and business teams to work closely to define data quality standards to truly harness the power of Master Data Management.

Questions: Have you participated in any MDM implementation? If you have, would you like to share some of the important lessons you have learned regarding data quality?

If you are learning about MDM now and have any questions, please feel free to ask your questions in the comment section as well.

Thank you kindly.


By |2016-10-31T14:21:27+00:00May 27th, 2015|Miscellaneous|0 Comments

About the Author:

Kumar Gauraw is senior IT professional with over 16 years of experience while serving in various capacities at Fortune 500 companies in the field of Data Integration, Data Modeling, Data Migration and architecting end-to-end Datawarehousing solutions. Kumar has strong experience in a wide spectrum of tools and technologies related to BI and Data Integration.

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