Master Data Management 101

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The management of master data can be described as managing the data that is critical to your business’s operations. Master data management (MDM) deals with managing data that is relatively stable and critical to the business’s operations. The concept of master data and its management came about in the late 1990s, as a way to deal with the large amounts of “disjointed data” being taken in. Master data is necessary for a modern organization to perform its daily operations efficiently, as well as analytical decision-making. 

Without master data, and the ability to manage it, businesses would struggle to function efficiently and effectively.

Knowledge of how important master data is, and how to manage it effectively, can provide organizations with the ability to streamline their work processes and improve efficiency. Master data management includes the software and processes needed to maintain, alter, and manage master data.

In general, master data takes up only a small portion of the business’s data storage, but in spite of this, master data is some of the most complicated data within the storage system.

What Are the Key Features of Master Data?

Understanding the basic features of master data is essential to effectively managing it and unlocking its full potential for successful business endeavors. The features of a well-organized master data management program support a flow of useful, consistent information as it operates. An understanding of these features helps in setting up a master data management program, and in working with it. The key features are listed below. 

1. Master data rarely changes. It is meant to be a single source of accurate data used across the organization. Complete and total accuracy is its reason for being. Master data changes much less frequently than other types of data, but it does change occasionally, and this is why master data management is important. Organizations must have a way of managing and updating their master data to ensure it continues to be accurate.

Mistakes happen when master data is not accurate – bills sent to the wrong address, employees receiving the wrong paycheck, etc.   

2. Master data is used primarily as reference material. It is non-transactional in nature (meaning an exchange of money or goods is not involved). For example, data that describes the inventory, customer, or point of purchase may be part of the master data, but it can be copied and then used in business transactions. 

Master data, as reference material, can be copied and used for various purposes.

3. Master data is very valuable to the organization. Without it, the organization “might” be able to survive for a month or two. Businesses use this data daily for a variety of tasks and is essential to accomplishing those tasks. It is important for an organization to place a priority on managing their master data to make sure it is accurate and reliable.

Maintaining and securing the master data is essential for the business’s day-to-day health.

4. Master data is typically more complicated than other forms of data. It normally includes large, complicated data sets. This makes managing and maintaining the master data a much more challenging process than might be expected. There are tools available that can be used in managing master data organizations to have robust processes and tools to manage it effectively.

Managing and altering master data can be difficult and time-consuming. 

How Do You Develop a Master Data Management Program?

A master data management (MDM) program uses the appropriate domains as part of its foundation. The domains that are chosen (or developed) have relatively stable data and have the greatest financial impact. In selecting data domains, it can sometimes be difficult to determine which data items in an organization should be designated master data. The five domains most used are:

  • Customer data 
  • Employee data 
  • Product data
  • Financial data 
  • Inventory data 

While these five domains are used regularly, some can be removed and other domains can be added, to better suit the organization’s needs. Also, if needed, organizations can add subdomains.

After determining the domains, organizations can use several steps to build a master data management program. Developing an MDM program is typically a long project and features several phases and tasks, including the following steps:

  • Identify all relevant sources of data, including departments within the organization storing data. 
  • Discuss and agree upon appropriate formats for the master data. 
  • Create a master data model that presents the model’s structure and maps data back to the various sources.
  • Determine the MDM architecture (which includes selecting the appropriate software). There are three basic types of architecture. 
  • Deploy the software and any new systems needed to support the MDM program.
  • Cleanse, consolidate, and standardize the data to fit the new master data model.
  • Match any duplicate data records from other departments and merge them to form single entries that become part of the master data list.
  • Adjust source systems as needed to provide access and use of the master data during processing operations.

After determining which domains have the most financial impact, the criteria listed below can be used to reduce the amounts of data that should be classified as master data.

Behavioral data: Often used for research purposes, behavioral data describes the organization’s interactions with customers and business partners, often in great detail. Behavioral data comes from help desks, call centers, websites, CRM systems, mobile apps, marketing automation systems, and billing systems. 

Lifecycle: This describes the various stages of a piece of data as it moves through its existence. It starts the initial collection and ends when the data is no longer useful and is deleted. 

Lifetime: The life of the data, or its existence within the organization, until it is deleted. (There are circumstances when data with a short lifetime is used as master data, but, currently, not often.) 

Data complexity: The measure of how complicated the data is. It describes large data sets taken from a variety of sources, which may mean using a number of resources to process it. Complex data can come from several sources, with each source possibly providing data using a different format, structure, size, and query language.

Data value: The value of the data comes from the advantages and benefits that a business can gain from their data assets. Data assets can be used to promote innovations, better decision-making, improved customer experiences, increased efficiency, and new sources of revenue.

Data reuse: The use of existing data that has been collected by “other” individuals or institutions for a new research purpose (third-party data). The term can refer to quantitative, qualitative, or statistical data.

What Are the Benefits of Master Data Management?

Master data management works hand in hand with effective data governance. MDM uses software tools and processes to provide uniform data and ensure the master data is centralized, organized, and up to date. A master data management program, combined with an effective data governance program, should provide highly streamlined business processes.  

Two of the most important benefits of using master data are an improved customer experience and faster deployments. Master data management can coordinate with customer experiences during every step of the transaction, providing accurate information for repeat customers. (Poor-quality data can have a negative impact on customer relationships.)

When a master data management data repository supports development units and apps, and the delivery pipeline is efficient, the end result is faster deployments (software deployment, data deployment). Master data management allows software that was developed today, to be deployed today.