Opportunities for Big Data Analytics
Recently, a consortium of 47 Japanese banks signed up with a blockchain startup called Ripple to facilitate money transfers between bank accounts using blockchain. The main reason behind the move is to perform real-time transfers at a significantly low cost. One of the reasons traditional real-time transfers were expensive was because of the potential risk factors. Double-spending (which is a form of transaction failure where the same security token gets used twice) is a real problem with real-time transfers. With blockchains, that risk is largely avoided. Big data analytics makes it possible to identify patterns in consumer spending and identify risky transactions a lot quicker than they can be done currently. This reduces the cost with real-time transactions.
In Industries outside of banking too, the main drive for adoption of Blockchain technologies has been security. Across healthcare, retail and public administration, establishments have started experimenting with blockchain to handle data to prevent hacking and data leaks. In healthcare, a technology such as blockchain can make sure that multiple “signatures” are sought at every level of data access. This can help prevent a repeat of events such as the 2015 attack that led to the theft of over 100 million patient records.
Possibilities in Real-Time Analytics
Up until now, real-time fraud detection has only been a pipe dream and banking institutions have always relied on using technologies to identify fraudulent transactions retrospectively. Since the blockchain has a database record for every single transaction, it provides a way for institutions to mine for patterns in real-time, if need be.
But all of these possibilities also raise questions about privacy and this is in direct contradiction to the reason why blockchain and bitcoins became popular in the first place. Several industry experts have expressed concerns that a technology that can provide a record of every transaction can be exploited for everything “from customer profiling to other less benign reasons”.
From another perspective however, blockchains greatly improve transparency in data analytics. Unlike previous algorithms, the blockchain design rejects any input that it can’t verify and is deemed suspicious. As a result, analysts in industries such as Retail only deal with data that is completely transparent. In other words, the customer behavior patterns that blockchain systems identify are likely to be a whole lot more accurate than it is today.
Uncovering Transactional Data
The data within the blockchain is predicted to be worth trillions of dollars as it continues to make its way into banking, micropayments, remittances, and other financial services. In fact, the blockchain ledger could be worth up to 20% of the total big data market by 2030, producing up to $100 billion in annual revenue. To put this into perspective, this potential revenue surpasses that of what Visa, Mastercard, and PayPal currently generate combined.Big data analytics will be crucial in tracking these activities and helping organizations using the blockchain make more informed decisions.
Data intelligence services are emerging to help financial institutions, governments, and all kinds of organizations delve into who they might be interacting with on the blockchain and uncover “hidden” patterns.