You may not realize it, but transactional data is a major part of our daily lives. Whether it’s going to the ATM to get out cash for the week or buying a cup of coffee, these transactions are actually part of an incredible scale. That’s why it’s important to have systems in place that make sure these transactions are handled safely and securely, while also providing an easy way for businesses to monitor in real time.
With more companies grasping on to data to better understand their business processes, you may be wondering ‘what is transactional data?’ Transactional data is information recorded from transactions, such as the time and place where it occurred, what was purchased, and what payment method was employed. In other words, transactional data is data generated by various applications from point-of-sale servers, security software, and ATMs to complete financial transactions. With numerous touchpoints, the resulting information needs to be of high data quality and secure lineage.
A clean capture of transactional data is helpful for running downstream analytics, preventing expensive efforts within this information collection. For example, it can cut time in lengthy customer service calls or tracking down facts in fraud claims for financial institutions. Transactional data breaks down into analytical data and master data. Analytical data comes into being through calculations and analyses, while master data represents critical business objects upon which said transactions are performed.
Examples of Transactional Data
There are a variety of examples of transactional data that fall under the umbrella of structured data. In a financial realm, transactional data can be used by the insurance industry to evaluate premiums and claims data for each insured person. Banks will use this data to monitor withdrawals and deposits from ATMs or different branches of their in-person operations. Logistical transactional data can help through the inner workings of delivery outlets to keep track of shipping status and shipping partner data. Work-related transactional data can be used by multiple companies for their payroll operations to track employee hours.
Transactional data implementation helps to document individual transactions, while also allowing for quicker searches into a particular transaction. This is recorded through a variety of applications systems that automate key business processes of an organization. Depending on the nature of the transaction, this information can get grouped into master data for a company with associated product and billing information. While these systems are highly efficient for customers, it’s important for efficient solutions to work for the companies that utilize them as part of downstream analytics.
Transactional Data and Big Data Analytics
The true focal point of transactional data is how little time it can take for businesses, vendors, and customers alike to get the information they need. Processing and making sense of business transactions quickly is needed for customer retention and staying ahead of the competition. This is a key source of business intelligence to gain valuable insight. This is done even quicker with the proper format to understand peak ingestion rates and data arrival rates. It’s important for small businesses, especially so that they may have a grasp of their software applications.
From an analytical perspective, this is a sequence of information exchange and the work related to it. This is meant to have a clear ability for the collection of information. Transactional data, along with associated operational data, is valuable for business analytics and insights. It’s a valuable tool to maximize efficiency in business operations. Through these transactional solutions, vendors and customers can get more out of data than just what’s on a spreadsheet. This will allow for those monthly sales numbers to become a tremendous asset and boost business logic throughout an organization.