Data categories to be aware of before starting a digital transformation

Analytical or Reporting Data

Analytical and reporting data is a collection of data that is used to support decision making and/or research. Therefore, it is essential that analyses are based on a complete, accurate and credible data foundation. Analytical and reporting data are typically numerical values and not descriptive.


Transactional or Operational Data

Transactional and operational data is changing frequently and is not static. They are created from business processes, including transactions with customers, suppliers or in manufacturing. The data is therefore strongly related to all departments involved in the business process (sales, marketing, manufacturing, purchasing, accounting, etc.).


Transactional data is the descriptive data (depending on events in a process - sales order, invoices, purchase order, shipping documents) of the business processes (in form of time, numerical values, etc.) related to objects such as customers or products. These data are typically grouped into transactional records, which include related master and reference data.


The following applications are typical for handling transactional or operational data: CRM, ERP, Marketing Automation


Master Data

Master data refers to company information that is required to describe transactional data. The transaction data mentioned under point 2 is always related to master data (for example, customer or product) of a company.

Master data exists in every application used in the company and is not often of high quality in the individual systems, frequently appears as a duplicate, and is not managed consistently.


In addition, applications often use master data only tailored to the use-case of the respective application (e.g. customer data in CRM of the sales department). Customer data is also used in other departments and different systems. That requires an MDM initiative to create a single source of truth.


Master data rarely changes. Typical master data domains are the following (completeness not guaranteed):


o    Party (Individual and Customer, Employee, Supplier, Organization, Business Partner, etc.)

  • The customer domain contains information such as: Name, E-Mail, postal address or individual preferences. Clean party data is needed, among other things, to successfully implement use cases such as customer experience or supply chain.

o    Product

  • Examples of data listed in the product domain: Description, SKU, costs, unit, images, product name


o    Finance (Account, Asset, etc.)

  • The financial domain contains topics such as costs, hierarchies, prices or rating factors.


o    Location (Geo, Postal address, Property, etc.)

  • By managing the location domain, linking the customer, product and location domain, for the analysis of customer behavior by region becomes possible. The same also applies to delivery quotas by region in the supply chain.



Metadata in a nutshell are "information about data ". For example, the metadata of a book is the title and the description, but also categories, the author, target groups or the price. In this case it would be the metadata of a product in the product domain.


Metadata is also a description and context of data, it helps to organize, find and understand data.


Reference Data

Reference data is used exclusively to categorize information or data. They appear as valid values, code lists, product types, currencies, industries, status codes, gender or demographic fields - it's always possible to standardize the lists and mostly also to understand them outside the company.

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