What is Transactional Data?

What is Transactional Data

Data is created when users complete an action on your website or within your app.

Transactional data, in the synopsis of data management, is the information that is captured from transactions. 

  • Records the different time points of the transaction
  • The place where it occurred
  • The price points of the transaction
  • Transaction Amounts and Quantities
  • The users associated with the transaction 
  • The payment method used
  • Discounts associated with the transaction
  • Type of transaction
  • Business Unit, Department, Channel of the transaction

What is not considered as a Transactional Data?

The list of vendors or customers (with their account details) is typically not considered transactional data. Transactional data are usually logged into data stores, which are often considered to be time-series databases. 

The primary value of transactional data is to support reporting and analytical queries for specific trading periods. 

It can be used to fill in reporting gaps caused by business rules or data quality issues. Transactional data is largely used for financial reporting purposes.

Types of Transactional Data

Transactional data can be further categorized into three types – 

  • Transactional data
  • Master data and 
  • Analytical data.

Transactional data : Transactional data is generated from various applications that run or support a business. These applications include a multitude of Financial Management, Human Resource Management, Procurement Management, Supply Chain Management, Sales Operations, Marketing Operations, inventory management, and all other related applications. The produced data reflects the transactions generated by these applications.

Upon generation, the data is stored by the applications in a database or another storage medium.

Master Data: Master data refers to the actual, complex business objects in which transactions are conducted, taking into account the parameters under which data analysis is performed.

Analytical data: Analytical data came into existence through calculations or analysis that run on transaction data.

Why transactional data is highly relevant in Big Data Analytics

The defining feature of transaction data is that it contains a time factor. This means it is very unstable and loses its relevance over time.

Quick processing and understanding of transactional data is important to use to maintain competitiveness. Transactional data, when used properly, can be a key source of business intelligence.

In big data analytics, transaction data is considered important to understand maximum transaction volume, peak injection rates, and peak data arrival rates.

Also Read: 4 Types of Data Analytics for an Improved Business Decision Making

From a deep analytical perspective, a transaction is a term used to define the sequence of information exchange and the work associated with it, for example, database update. The whole thing is considered as a unit for all practical purposes. 

The transaction data along with the activity data associated with the business analysis are valuable; Transaction insights are transmitted back to the same core operating systems for continuous business process optimization. 

Therefore, transaction data is a valuable tool to increase the efficiency and effectiveness of business operations.

Examples of transaction data

Transaction data generally fall under the category of structured data. Some examples:

  • Financial transaction data: Financial data on things like Sales, Cost of items, Receivables, Payables, Net Income, Assets and Liabilities, etc.
  • Logistic transaction data: Inventory Amount, Production Amount, Ordered Amount, Delivered Amount, Backlog amount, etc. are examples of Logistic data.
  • Work-related transaction data:  Employee hired, Employee Headcount, Employee turnover, Employee Payrolls, Employee Leaves & vacations, etc. are examples of work data.

In this case, the transaction data records the reference data, including the time to document the specific transactions. It is recorded as part of information and application systems that automate the organization’s key business processes, such as online transaction processing systems.

Depending on the nature of the transaction, the data is grouped into master data like associated product, customer, employee, supplier, Department and Time, etc. related information.

Raw transaction data can be confusing and must be cleaned up for efficient analysis. Data enrichment tools are widely leveraged for this purpose.

Who uses transaction data in an organization?

In an organization, the Information Technology Operational Team and the Data Analytics Team are the main handlers of transactional data. The benefits are twofold:

  • IT Team monitors activities and transactions in real-time. They use data and streaming products to identify, diagnose and resolve any performance issues that may cause serious service disruptions. It saves both money and time.
  • Business managers and data analysts use real-time transaction data to understand any outliers and root cause in Financial, Purchasing, Supply Chain, Marketing, Human Resource functions.

In this context, transaction data provides valuable insights that can help improve service delivery. Transaction data can be used to improve Sales, Supply Chain, Employee Satisfaction, Regulatory Compliance, Marketing spend, Customer Experience and grow the business.

If all this sounds intimidating and you want to know more about transactional data analysis without having to learn Excel functions yourself, we would love to talk with you!

Reach out to us at [email protected]