3 Ways Blockchain is Impacting Analytics
As a revolutionary technology, blockchain has a potentially significant impact on data and analytics organizations that can incorporate it into their processes.
- By Troy Hiltbrand
- March 14, 2022
When most people think about blockchain technology, they automatically associate it with cryptocurrency such at bitcoin. The reality is that blockchain is so much more than a component of this and other decentralized currencies. It is a protocol that describes how transactions are defined, connected, transmitted, and collected. Inherent to its design are the processes needed to establish consensus when updating a data store in a way that guarantees its non-repudiation.
As the blockchain technology breaks free from its early beginnings, companies are finding new and innovative ways to put it to work to enable business processing. The realm of data and analytics is no different. These teams are also looking for ways to integrate blockchain into their processes to further support their businesses.
Three ways data and analytics teams are using blockchain are in data monetization, data and document integrity, and fraud prevention.
Smart Contracts and Data Monetization
When a company recognizes that it has data valuable to others, it looks for ways to monetize that data. This requires establishing a relationship between the seller of the data and the potential consumer.
As part of the blockchain concept, there has arisen the notion of a smart contract -- an entry in a blockchain that contains a snippet of code. When an action occurs in the blockchain, that code is executed. These code snippets are limited to reading from the blockchain and writing to the blockchain, but with these two features, complete transactions between parties can be automated. If the smart contract needs to interact with information from the physical world, a blockchain oracle can be used. A blockchain oracle is a third-party intermediary that provides information that can be connected to the smart contract, providing knowledge about activity in the physical world.
With these smart contracts, data consumption can automatically trigger a payment for access. These smart contracts create near-certainty of a trusted exchange of value between two parties, who may be known or anonymous. The smart contract is immutable and irrevocable. This concept minimizes, or even eliminates, third-party intermediaries as the gateway to your data and allows for a consensus-based transaction, even when the parties are not known to one another.
Because the payment for data services uses cryptocurrencies, it eliminates the concept of national borders and their associated currency. This greatly enhances the ability to sell your data in an international environment without the need to support cross-currency transactions or currency conversion fees. Eliminating the steps in monetizing your data can reduce its cost, which can lead to lower costs for the consumer and higher profits for the provider.
Non-fungible tokens, or NFTs, are one of the most popular examples of data monetization. An NFT is defined as unique, non-interchangeable information that represents a piece of digital media. This could be any digital asset, including fine art, videos, songs, photos, documents, or even data sets. These assets could be digital-only, or they could be digital representations of physical assets. In the end, the digital representation of the asset is just another form of data and NFT creators have successfully leveraged the blockchain to create a booming segment of the economy.
Document and Data Integrity
In environments such as the legal or health care industries, it is vital that documents can be created in such a way that they cannot be modified. Using a blockchain, companies can ensure the data integrity of these documents. As part of the initial digital creation of the document, its hash can be written to the blockchain to establish its permanence. The mechanics of the blockchain ensure that once the hash is written to the ledger, it cannot be changed. Any modification to the document can be identified because the associated hash would be different from the one stored on the blockchain.
In the area of data, a document could be a digital representative of a document in the physical world, or it could be a representative of a set of attributes as defined by a document-store NoSQL database. In either case, records that need undeniable proof of their integrity could leverage the hash value on the blockchain with its associated timestamping as a digital guarantee. This process can become a fundamental component of data lineage and a team’s ability to show that the data being sourced is accurate and viable.
As more transactions are taking place in the arena of decentralized finance using cryptocurrency, businesses are often forced to identify where fraud is occurring among these pseudo-anonymous transactions.
Transactions in a blockchain happen in real time, increasing the potential of fast decision making, but also increasing the risk of fraud. It is often the responsibility of financial and data analysts to go back after the fact and identify what activity appears to be fraudulent, as well as to identify ways to predict and prevent potential fraud in the future.
The nature of the blockchain gives these data analysts a very effective audit trail of the transaction and how it fits into a larger picture. Although transactions are pseudo-anonymous on the blockchain and not all participating parties are known, the information about the transaction is all publicly visible. Representative data about the parties involved in the transaction and the medium and value of the transaction are both available data elements. Data analysts can track where each transaction fits in a chain of transactions and start to see patterns that represent fraud. With these patterns established, they can help the business to establish guardrails to prevent future fraudulent activity.
With the introduction of NFTs as investment vehicles for companies, analysts need to be able to verify the legitimacy of the assets and the sellers pitching them prior to a transaction. Unlike cryptocurrency, where all tokens are essentially equivalent, NFTs are tied to a specific piece of digital content. The nature of that content is what drives its inherent value. Fraudulent actors in the space seek to inflate the value of their NFTs before dumping them on an investor, who is then left with an overpriced asset that no one else wants.
Analytics teams need to be able to assess the patterns of transaction activity using the public data available as part of the relevant NFT blockchain. They need to be able to evaluate what drove the price of the digital asset to its current level, know who the participants in those transactions were, and be able to inform decision makers about whether the price is justifiable and sustainable. Just as analysts are tasked with value determination of physical assets on their company’s books, they will be tasked with activity surrounding digital asset valuation as well. This task will become critical not only after the purchase but also as an input into the buying decision. Companies with strong analytical processes in place will be positioned to not only avoid fraud but also have the potential of exercising very wise investment strategies and gaining from this lucrative market.
As the potential for blockchain extends beyond merely being the technological backbone of the cryptocurrency, companies across the world will continue to find new and innovative ways to leverage it. Data and analytics teams plugged into this technology trend will be able to find better ways to execute on their strategies as well. They will be able to leverage it to drive profits, reduce risk, and augment business processes to streamline operations.