In the early days, when computer networks and processing power of point-of-sale and payment terminals was limited by then current-technology, the focus was on efficiency. Payment transactions generally only contained a few data elements of data required to process the transaction, and implemented technically in a manner that saved as many bytes as possible. This was important so all of this would work over a dial-up line and only send required data for transaction processing. Much of the payment message formats are tied to this legacy heritage to this date.
In the world of ‘Big Data’ there is a growing trend of providing fatter transactions, and providing more data in these transactions. These transactions consist of more then the final amount of the transaction and payment information, but now with market basket data and line item detail.
What does this involve from a payments system perspective ?
1) Expanding message formats and APIs to include list of skus and UPCs and other meta-data of market basket items.
2) Processing against a catalog to perform various value added services and processing.
3) Parallelism in transaction processing as certain items require processing that would take too long if processed in a serial manner.
4) Development of systems including robust engines and processing logic leveraging Machine Learning techniques to mine and process such data.
This isn’t new in concept as it has been performed locally in retailers for sometime now, as well as in some level-3 purchase/commercial cards. Now there is a trend of more value added services to enhance payment processing such as item based loyalty rewards, when such data is available you have more options and capabilities to enhance the payment transaction.