What Is Transactional Data?

You can only have a transaction with a time dimension. Transactional data is like an onion with layers; instead of layers, it has timeliness. Transactional data is information directly derived as a result of transactions. Unlike other sorts of data, transactional data contains a time dimension, which means there is a timeliness to it, and over time, it becomes less relevant. It is essential to have some strategy for managing the timeliness of this data so that it isn't lost in time or becomes more difficult to use. We can divide transactional data into two categories: real-time and data with a time dimension. Real-time data are the data that is received and generated simultaneously and are often unstructured. On the other hand, data with a time dimension are usually structured data with timeliness to them and have value for a certain amount of time before becoming irrelevant. Real-time data is often unstructured and can be received at any time. Data with a time dimension can be structured or unstructured and is usually obtained at a particular time. To understand the concept better, let's take an example of a retail store. The POS system has several products, categories, and prices. The transaction data would be selling a particular product at a certain price and quantity at a specific date and time. Cannot use the transaction data to identify an individual customer; it's more of a description of the event itself. Transactional data is the bread and butter of the business world. It's what you must do to keep your organization operating smoothly. Transactional data will be at the core of your business practices, no matter what kind of organization you're running, whether a small business or a large corporation. For example, if you're running a grocery store, every time someone buys something from you, that's transactional data. Transactional data includes purchases, requests and insurance claims involving an interaction between two parties.