What is Master Data?
Terminology relating to data, master data, governance and master data management can be confusing, so it is often useful to take a step back in order to gain some clarity and, more importantly, to consider how they are applied in practice – and how we might ensure that everyone understands each other when these concepts are being discussed.
To this end, this article will cover the following:
- What is master data?
- Why is Master Data Management (MDM) important?
- How to define your MDM strategy
What is Master Data?
Master data refers to any collection of information that relates to global attributes for customers, products or suppliers and, as such, is destined to be used across multiple departments and applications to provide the context for other data types used within individual applications, such as transactional data.
Therefore, in terms of supplier master data, it will relate to information such as supplier name, address, or banking information. This is different from other types of supplier data, such as relationship data, which might describe products purchased or transactional data which relate to one-off events that take place as a result of the relationship, for example (see chart below).
Why is Master Data Management (MDM) important?
Gartner describes Master Data Management as, “a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets.”
To illustrate, let’s think about a bookcase, or more specifically for this example, a bookcase that contains rare first editions collected over the years. Every time a new book is purchased, it is put on the shelf in any available space. The end result is a high-quality collection; however, it is without order, sorting, or structure. To find a specific book is difficult. Every shelf has to be checked. Doing this every time is neither efficient nor effective.
However, sorting and segmenting them alphabetically will make the process much easier and quicker, as it is clear which section of the bookshelf a particular book will be in. The segmentation can also be done in different ways – by title, author or genre, depending on the needs. Putting in place logical and defined structures not only allows for finding what is needed, when it is needed, but it also makes it far easier to manage and govern going forward. How these rules are defined and enforced (and by whom) are core elements of Master Data Management.
As this illustration shows, due to the changeable nature and high volume of information being used by organizations on a daily basis, high-quality data on its own does not provide a competitive advantage, which is why the discipline of Master Data Management has become so important.
How to define your Master Data Management strategy
Gartner defines a data strategy as “a highly dynamic process employed to support the acquisition, organization, analysis, and delivery of data in support of business objectives.”
The steps required for defining a Master Data Management strategy for an organization include:
- Data governance
- Data migration
- Data cleansing
Governance and migration will involve defining the data standards and putting in place the Master Data Management tools required for transforming the data, so that is reaches the defined standards. For example, using the bookshelf analogy, any books added to the collection must be verified as first editions. Otherwise, the quality of the collection becomes lower, which defeats the purpose.
This is where governance really comes into play – ensuring that the quality of anything you add is high enough to justify its inclusion. The same goes for master data governance; you need to be sure that the data you’re adding is of a high quality.
Finally, once you’ve completed the above steps, you’ll be able to cleanse the data you already have to meet your newly-defined standards, now on an ongoing basis and, crucially as the data is added, which includes consolidation, de-duplication and so on.
To find out more about master data, Master Data Management, and data governance, click on the below resources:
Article updated November 2021