What is Transactional Data?
Transactional data is information that is captured from transactions. It records the time of the transaction, the place where it occurred, the price points of the items bought, the payment method employed, discounts if any, and other quantities and qualities associated with the transaction. Transactional data is usually captured at the point of sale.
In other words, transactional data is data generated by various applications while running or supporting everyday business processes of buying and selling. A large and intricate web of point-of-sale servers, security software, ATM, and payment gateway data exists, originating from every possible device used to complete a financial transaction.
Given the sheer number of touchpoints, the resulting data is often difficult to read or contains unnecessary extras like letters, symbols, or numbers. A clean capture of transactional data is helpful for running downstream analytics, preventing expensive customer support calls, or tracking down the facts in fraud claims.
From a process standpoint, each transaction that occurs gets assigned its own unique identifier, known as “trans ID,” which is accompanied by a list of items that are a part of the transaction.
Transactional data differs from the other main data categories, which are:
- Analytical Data: Analytical data, as the name suggests, comes into being through calculations or analyses run on the transactional data.
- Master Data: Master data represents the actual, critical business objects upon which said transactions are performed, also taking into account the parameters on which data analysis is conducted.
Why Transactional Data Is Highly Relevant in Big Data Analytics
The defining characteristic of transactional data is that it contains a time aspect. This means it is highly volatile and loses its relevance over time. Processing and making sense of transactional data quickly is important to use it to maintain a competitive edge. Transactional data, when used right, can be a key source of business intelligence.
For instance, in big data analytics, transactional data is vital to understand peak transaction volume, peak ingestion rates, and peak data arrival rates.
From an analytical perspective, a transaction is the term used to refer to a sequence of information exchange and the work related to it, for example, database updating. The whole thing is treated as a unit for all practical purposes. Transactional data, along with associated operational data, is valuable for business analytics; transaction insights are streamed back into the very same core operational systems for continuous business process optimization. Therefore transactional data is a valuable tool to maximize efficiency and efficacy of business operations.
Examples of Transactional Data
Transactional data typically falls under the category of structured data. Some examples include:
- Financial transactional data: insurance costs and claims data, or a purchase or sale; Deposits or withdrawals in case of banks
- Logistical transactional data: shipping status, shipping partner data
- Work-related transactional data: employee hours tracking
In this context, transactional data records the reference data, including time, to document a particular transaction. It is recorded as part of the information and applications systems that automate the key business processes of an organization, such as online transaction processing systems.
Depending on the nature of the transaction, the data gets grouped within master data with associated product information and billing information.
Raw transactional data can be messy and must be cleaned for downstream analytics. Data enrichment tools are now widely available for this purpose.
Who Uses Transactional 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 two-fold:
- Information technology operations monitor transactions in real time. They use the data and streaming products to locate, diagnose, and fix any performance issues that may cause serious service disruptions. This saves both money and time.
- Business managers and data analysts use real time transaction data to understand buyer behavior and get an idea of how their products and services are adopted. In this instance, the transaction data yields valuable insights that help improve the service offering. Transaction data serves to deliver better customer experiences, acquire new business, and boost profitability of the business.
Challenges in Managing Transactional Data
Sometimes, the lines between master and transactional data get blurred, when master data turns out to be rather transactional in nature. An example would be when a new record is created for a vendor’s new address, instead of changing the existing record. This could be accidental or intentional. The latter case is relevant if an enterprise opts to retain all addresses of their suppliers, should they wish to track and analyze the supplier’s movement.
It gets tricky at this point. The better option may be to treat this kind of data as transactional, and apply transactional solutions to any problems that may arise. This is more efficient management in the long run.
Transactional data hygiene and integrity is maintained by the database feature of only logging completed transactions. The system cancels a transaction that did not check all the proper completion boxes. This inbuilt screening mechanism ensures that the data recorded is either a successful transaction or a failure. This feature is not without its challenges; notably, it is sometimes difficult to scale up.
These days predictive modeling is a major function of data analytics, conferring agility to organizations that tap into it. However, predictive modeling that uses transactional data presents issues under certain circumstances, particularly if data quality is not up to scratch. It also impacts cohort and trend analysis, among other things.
Transaction Data and Machine Learning
These days, machine learning is utilized in a variety of transactional systems to make processes more seamless. Thanks to machine learning, a system could interpret patterns buried within customer’s purchase data, and predict any fraudulent transactions based on the premise of cognitive computing. It sets a higher confidence level and makes for the evaluation of multiple transactions in real time.
For machine learning to perform smoothly, the more transaction data available, the better. The models perform better exploratory processes, maintaining efficiency and integrity, provided the number of associated variables does not go too high.
Advantages of Transactional Data
Well-managed transactional data yields many advantages:
- Enhanced customer experience by delivering more consistent services
- Reduced transaction failures
- Optimized real time data gathering across a variety of payment processes or gateways
- Faster diagnostics and troubleshooting
- Reduced cost-to-serve
- Increased cash forecasting insights
- Optimized credit and debit card management
- Rapid detection of fraudulent transactions
- Improved threat detection
- More accessible insights
- Developed adaptive-behavioral algorithms
- Improved machine learning
- Reduced old, obsolete, and error-prone workflows
- Detected transaction anomalies, firewalls, blocking, and risk-scoring in real time.
Transactional data provides a unique, albeit time-sensitive, advantage in keeping business operations smooth and optimized. It is valuable to a company both for preventative maintenance as well as improving operational processes. Ultimately, insights provided by transactional data are intuitive and can be harnessed for delivering superior customer experiences.
Introducing TIBCO EBX® software
Businesses, and business innovation, thrive when high-quality shared data assets—master data...
Gartner Hype Cycle for Data Management
New innovations in data automation and augmentation are always revealing themselves and creating...
The Ultimate Master Data Management Implementation Guide
Once your organization decides that master data management (MDM) is a necessity, the challenges of...