The four types of data

Data is an asset of extreme importance and value for nearly all businesses; therefore, treating it with the respect and care it deserves, is a significant factor in a company’s growth and success. Understanding the various types of data within your organization is a crucial step to proper and proactive management of your holdings and policies.

Within an organization's data holdings, there are typically four different types of data, each serving a different function. These types of data include: 

• Master Data
• Transactional Data
• Reference Data
• Freeform Data

Master Data

Master data consists of key information that is shared across the enterprise. Master data facilitates high level and critical business processes. “Master Data Management” is the practice of responsibly managing and distributing master data throughout the organization. 

Master data is the functional data for business entities. Often it consists of a ‘master list’ of customers, products, partners, etc. This type of data is often considered mission critical for the business, and consistently shares overlap with the business’s CDEs, or Critical Data Elements. Master data needs to be shared and accessible across the company, while remaining safe, redundant, and adherent to policies. 

Master data is specifically created, managed, and stored such that it can be accessed by various applications for specific business processes or functions. For example, employee master data is created and stored in a manner such that both the organization’s human resources, and time-tracking systems can access the same master data, but for different functions. 

 When executed well, Master Data Management is seamless for all parties involved. It is an important backend framework for nearly all businesses in the world.

Transactional Data

Transactional processes are foundational for any given business; all documentation of this core element is Transactional Data.

Transactional Data usually operates on a much larger scale than Master Data or Reference Data. This increased volume of data-flow requires greater system efficiency and is managed with care, to ensure customers’ PCI (Personal Credit Information) data remains private and compliant. 

Transactional Data is typically created, stored, and utilized in operational and/or transactional applications such as banking or purchase transactions. Storage of Transaction Data varies amongst companies, branches, and policies. For a company that is focused on selling a product or service, this usually includes the info of your purchasing and selling activities, such as products, price, sale amount, payment method, etc. 

Ensuring the privacy and security of Transactional Data and Personal Credit Information holds a heavy weight when responsibly managing the policies and processes surrounding it. 

Reference Data

Reference Data is stable and commonly accessed information that categorizes data, correlates it with consistent values, and follows internal and/or external data standards. 

It is usually standardized data that is governed by certain codification policies or rules. Reference Data is usually more uniform, and less volatile than Master Data.

Examples of Reference Data can include a list of countries, regions, customer segments, languages, currencies, and more. Generally, reference data stays the same or changes very slowly over a period of time.

Freeform Data

Freeform Data, often referred to as unstructured data, is not organized or formatted in a predefined manner. Freeform data consists of freeform text, dates, numbers, and essentially any data that is not stored into a cookie cutter spreadsheet that is easily referenced or understood by a computer. 

Freeform data can include written content on web pages or documents, journal articles, emails, surveys, contact center information, marketing research, as well as social media posts and comments.

Most user interaction driven applications natively produce freeform data. Microsoft Word for example, is simple for humans, but without a “translation”, produces data incompatible for computers to easily understand and accurately reference. Freeform data is always more difficult to analyze, and requires a connector solution like Data Sentinel to extract the necessary information quickly and accurately.

Conclusion

In the real world, the four main categories of data are (or should be) interconnected and symbiotic. This cross functionality offers great flexibility and benefit, but can be difficult to achieve. Understanding the data ourselves is one thing, but teaching that understanding and relevant association to the core systems handling the data, is a challenge few have solved. 

To quote GIJoe: “knowing is half the battle”. You can’t solve a problem, that you didn’t know existed.

To solve a data problem, the problem statement must first be fully understood. A full automated data audit, paired with our hybrid mapping process gives you the information you need, in order to solve those problems that remain hidden. 

The hybrid process is most simply explained by combining the strengths of:

1. our methodology and expert practitioners 

2. the efficiency and accuracy of our technology

By combining person and machine, we offer a solution that highlights the best of both worlds. Speed of the machine, insight of the analyst.

4 Types of Data Cliff Notes:

• Master Data

Key information that is shared across the enterprise. Master data is the functional data for business entities. Often considered mission critical for the business. Stored such that it can be accessed by various applications for specific business processes or functions. Seamless backend process when executed correctly.

• Transactional Data

Transactional Data is foundational for any given business. It includes all data related to the documentation of business transactions, both B2B, and B2C. It usually operates on a much larger scale than Master Data or Reference Data. Privacy and security is a critical factor of Transactional Data.

• Reference Data

Reference Data is stable and commonly accessed information that categorizes data, usually more uniform, and less volatile than Master Data. Generally, reference data stays the same, or changes very slowly over a period of time.

• Freeform Data. 

Freeform Data, often referred to as unstructured data, is not organized or formatted in a predefined manner. Freeform data can include written content on web pages or documents, journal articles, emails… Most user interaction driven applications natively produce freeform data.

‍