What is master data?

In general, the data captured by businesses falls into one of these three categories:

• Transactional data: Transactional data is data that is generated by various applications while running or supporting everyday business processes.
• Analytical data: Analytical data, as the name suggests, comes into being through calculations or analyses run on the transactional data.
• Master data: Master data represents the actual, critical business objects upon which said transactions are performed, also taking into account the parameters on which data analysis is conducted.

The three data types are best exemplified in the following sentence, which sums up a run-of-the-mill business transaction:

Buyer X placed an order for 10 of SKU Y, on DD-MM-YYYY for a total of $5000.

Within this, the buyer and the product are comprised of master data, as they are at the heart of the transaction; without them, the transaction process would not exist. The secondary data generated as a result of this interaction falls under transactional data (such as the amount, the date, the quantity purchased, the invoice number, or tax identifiers). Furthermore, information such as the average order sizes for this particular customer and the average order value, which is extracted through digging into an accumulated dataset, comes under analytical data. Note that all three data types are linked, and in the nature of day to day business transactions, an organization needs all three types working together seamlessly.

This example makes it easier to understand the definition of master data, data about the business entities that provide context for business transactions.

Master data forms one of the key data assets of a company. Some companies are even acquired for access to their customer master data set.

Categories and components of master data

As the above example suggests, the most common categories of master data, along with their components, are:

• Parties: Both individuals and organizations, plus the whole spectrum of roles nested therein: scouts, buyers, vendors, customers, suppliers, and employees.
• Products: commodities traded among the parties
• Financial structures: assets, accounts, documents, etc.
• Locational concepts: sales territories, branches, office locations

Master data is needed by several business processes as well as their IT systems. Therefore, it is imperative to standardize master data formats, synchronize values, and manage data properly to bring about successful integration into the system.

Master data is typically non-transactional in nature. The exception to this is where information about master data components such as parties or products is listed only on transactional documents like invoices and receipts and is not recorded separately (although it should be).

Master data is often grouped into master record datasets, which may encompass “reference data” associated with it. However, it is important to separate master data from reference data. Associated reference data is rather like a tag-along piece of data, like the zip code within an office branch address in a customer master record dataset.

Master Data Management

Master Data Management (MDM) is a unified data service that covers the technology, tools, and processes that are necessary to unify and coordinate master data from various sources, across the whole business organization. In other words, it is a tech-enabled solution designed to maintain the official cross-departmental shared master data set in a uniform, consistent manner—rendering it credible and accessible at all times.

Good management of master data is needed to ensure data consistency, completeness, and accuracy within a business entity and its associates. The tools should ensure clean and consistent data over the long run, not just the short term. Master data management helps prevent clutter by eliminating silos and duplicate versions of data sets, manual errors, and establishing a trustworthy timeline of events.

The fundamental role master data plays in captioning, organizing, and understanding operational data inspired the entire field of master data management. Companies needed to better organize and improve the consistency and quality of their core data assets. Data can become scattered and cluttered if not managed well.

Master data needs to be stored properly to facilitate good analytics and business predictions too. An organization needs to easily find the answer to the following questions:

• Who are our best customers and where are they located?
• What products yield the best margins that we should invest in?
• How many employees are needed for a certain transaction to go through?

Problems arising from a lack of Master Data Management

The typical problems and challenges that arise with mismanaged master data are as follows:

Data redundancy

The importance of master data for business processes is at the root of data redundancy, as various departments can maintain data in a number of non-uniform applications. For instance, sales staff will maintain information in customer resource management software, whereas the accounting department will maintain it in accounting software. The same customer information ends up being split over and over, increasing costs, and sometimes causing confusion.

Data inconsistencies

A part of this can be attributed to the manual error in data entry and maintenance, highlighting the need for automated data management systems. It can also stem from data redundancy and consolidating information from the various applications as mentioned above.

Business process inefficiencies

When master data is stored with redundancies, it can adversely impact the end-to-end process flow of an enterprise. For example, when different versions of master data exist, each of the different actions for order fulfillment such as order-to-ship, billing, and other process flows taps into a different master data set. This impedes successful execution: an item could be sent to the wrong address, or an outdated address appears on a bill. Having an integrated master data management system can help a company avoid all of these problems and their associated expenses.

Rapid changes in the business model

In this era of rapidly-evolving technology and business model tweaks caused by a multitude of factors, disruptive events are common and should be expected. Disruptions can escalate each of the issues discussed above.

The benefits of managing master data

Well-managed master data indeed confers an edge to every aspect of the enterprise and every stakeholder therein, ultimately improving business outputs:

• Well maintained and managed master data drives business initiatives and helps streamline processes within both B2B and B2C enterprises
• Master data management ensures better regulatory compliance and a smoother product onboarding process
• It helps design more personalized customer experiences, driving sales and improving customer relationships
• Master data management helps with better customer segmentation and reporting
• It brings about a greater degree of control over all data sets and subsets
• It optimizes resources by accurately tracking assets such as equipment, its location, usage, and maintenance logs
• When master data is well managed, the most relevant and recent product information can be made available to the right customers and trading partners.

When master data is stored as a single and granular master record with accurate details on every person, place, or thing associated with a business organization, it is a consistent and bankable source of business-critical data. It can then be used across the board for better-informed business decisions.