Analyzing Survey Data2
Table of Contents

The term “master data” refers to “data on corporate entities that give context for business activities.” Parties (individuals and organizations, as well as their roles, such as consumers, suppliers, and workers), goods, financial structures (such as ledgers and cost centers), and locational notions are the most prevalent types of master data.

Master data and reference data should be segregated. While both give context for business activities, reference data focuses on categorization and categorisation, whereas master data focuses on business entities.

By definition, master data is virtually always non-transactional. In some situations, an organization may need to regard specific transactional processes and operations as “master data.” This occurs, for example, when information on master data entities, such as customers or goods, is only found in transactional data, such as orders and receipts, and is not stored independently.

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The set of identifiers that offer context about business data such as location, customer, product, asset, and so on is known as master data. It is the core data that is absolutely necessary for the operation of a commercial firm or unit. Otherwise, there would be no way to compare data between systems in a consistent manner. All master data, however, is not created equal. The type of data labeled as master data varies by industry. Examples of master data might be distinct or have nothing in common even across multiple company units within the same sector.

In general, the information collected by corporations falls into one of three categories:

Transactional data: Transactional data is data created by various programmes while they are running or supporting daily operations.

Analytical data: As the name implies, analytical data is created by computations or analysis performed on transactional data.

Master data: Master data contains the real, key business objects on which transactions are done, as well as the parameters on which data analysis is performed.

The three data kinds are best shown in the following line, which summarizes a typical commercial transaction:

Buyer X made a $5000 purchase for ten units of SKU Y on DD-MM-YYYY.

The buyer and the product are master data inside this since they are at the center of the transaction; without them, the transaction process would not exist. The secondary data produced as a result of this contact is classified as transactional data (such as the amount, the date, the quantity purchased, the invoice number, or tax identifiers). Furthermore, analytical data includes information such as the typical order size for this particular consumer and the average order value, which is obtained by delving into an accumulated dataset. It is important to note that all three data kinds are interrelated, and in the nature of day-to-day business transactions, an organization needs all three to perform flawlessly together.

This example clarifies the notion of master data, which is information about corporate entities that provides context for business activities.

A company’s master data is one of its most valuable data assets. Some businesses are even bought in order to gain access to their client master data set.


As the above example illustrates, the most prevalent types of master data, as well as their components, are:

  • Individuals and organizations are both parties, as are the various roles that are nested inside them: scouts, buyers, vendors, consumers, suppliers, and workers.
  • Products are the commodities that are traded between the parties.
  • Financial structures include assets, accounts, paperwork, and so forth.
  • Sales regions, branches, and office locations are examples of locational ideas.
  • Several business operations, as well as their IT systems, require master data. As a result, it is critical to define master data formats, synchronize values, and appropriately manage data in order to achieve successful system integration.

In most cases, master data is non-transactional in nature. The exception is when information on master data components such as parties or products is only included on transactional documents such as invoices and receipts and is not separately maintained (although it should be).

Master data is frequently organized into master record datasets, which may include “reference data.” However, it is critical to distinguish between master and reference data. The zip code inside an office branch address in a customer master record dataset is an example of associated reference data.


Well-managed master data gives every element of the firm and every stakeholder an advantage, eventually enhancing business outputs:

  • Master data that is well kept and managed supports business initiatives and helps expedite procedures in both B2B and B2C organizations.
  • Master data management enables improved regulatory compliance and a more seamless product onboarding process.
  • It aids in the creation of more tailored client experiences, hence increasing sales and enhancing customer relationships.
  • Master data management improves consumer segmentation and reporting by giving you more control over all data sets and subsets.
  • It optimizes resources by precisely tracking assets like equipment, their location, usage, and maintenance data.
  • When master data is correctly handled, the most relevant and up-to-date product information is made available to the appropriate customers and trading partners.

When master data is maintained as a single, granular master record with correct details on every person, place, or object linked with a business organization, it becomes a consistent and bankable source of business-critical data. It may then be applied across the board to make more informed business decisions.

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The following are some of the most common issues and obstacles associated with unmanaged master data:

  • Redundancy in data

The relevance of master data for business operations lies at the heart of data redundancy, as different departments might keep data in a variety of non-uniform applications. For example, sales personnel will save information in customer resource management software, but accounting personnel will keep it in accounting software. The same consumer information is repeatedly separated, raising expenses and perhaps generating confusion.

  • Inconsistencies in data

A portion of this can be ascribed to human mistakes in data input and maintenance, emphasizing the importance of automated data management systems. As previously stated, it can also result from data redundancy and the consolidation of information from numerous applications.

  • Inefficiencies in business processes

When master data is kept redundantly, it can have a negative influence on an enterprise’s end-to-end process flow. When various versions of master data exist, for example, each of the numerous order fulfillment activities, such as order-to-ship, billing, and other process flows, taps into a distinct master data set. This impedes successful execution: an item may be delivered to the incorrect address, or an outdated address may show on a bill. Having a unified master data management system can help  a company avoid all of these problems and their associated expenses. 

  • Rapid Modifications to the Business Model

Disruptive occurrences are widespread and should be expected in this era of constantly growing technology and business model modifications driven by a variety of variables. Disruptions have the potential to exacerbate all of the difficulties described above and can assist a firm in avoiding all of these issues and their accompanying costs.

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