Blockchain & Big Data - where do the trends converge?
Although being at different maturity stages, big data and Blockchain are two topics being highly debated, explored and adopted, and that present a high synergy for complementing each other in unforeseen ways with the potential of disrupting existing business models.
Big data can be defined as the heightened ability to convert events into data, which happens in a large variety of formats, from different origins and in asynchronous velocity.
Blockchain is a shared ledger for recording the history of transactions. It is a decentralized system that uses consensus mechanisms, such as proof of work, to ensure trust, accountability, and transparency in a peer-to-peer network.
Data Lineage and Provenance
One of the greatest challenges in big data is the reliability of the data itself. Understanding how each information was generated, under which conditions, and how accurately it reflects reality is extremely difficult, if at all possible. Blockchain has the potential of enabling data lineage, which is the clarity of the information lifecycle, how it was generated, transformed or used over time, until its eventual disposal.
As analytical capabilities are being increasingly embedded into products, data ownership is a growing challenge. (E.g.: by the moment you activate your GPS, how many different applications are collecting that data? Who owns that? Who can monetize or transform that information?) Blockchain can provide data provenance, which stands for the clarity of how ownership and permissions have changed over time.
Combined they can increase trust and clarity to a world that highly revolves around uncertainty and assumptions around data sets and analysis outcomes.
Data source for the analysis of underlying patterns
The Blockchain, is itself a powerful data source for the analysis of underlying patterns of the transactions recorded in it. Blockchain has the advantage of providing a cryptographic footprint for every transaction, which increases rastreability and security. Within this context, business models are already tailoring itself to the Blockchain, and specialized data services are emerging, such as performance monitoring of smart contracts, anti-fraud solutions, and trader oversight to identify potential identity theft, risk exposure or detect anomalous behavior.
Data triggered actions
Contracts are bounded by a multi-part obligation of meeting a set of criteria. As our ability to convert “real world” events into data increases, so does the possibilities of designing “touchless” processes. In an asset transfer scenario, the billing can be triggered when GPS data indicates that the product has reached its destination. Another example is to have data as the agreed condition to meet the proof of work criteria. For instance, the delivery of fresh meat from a butcher shop to a supermarket must be done so as the temperature does not increase to the levels of jeopardizing meat conservation. In light of this, a smart contract could be in place to specify that the proof of work of a given transaction is having the meeting going to the supermarket with refrigeration in the truck not going higher than a specified value, having it all monitored with sensors and GPS and the data being stored in the Blockchain.
Big data and Blockchain greatly work in a complementary way, augmenting its application in different scenarios, as they mitigate each other weak spots. Blockchain can add trust and provenance to big data whilst data services can enhance security and performance, or act as proof of work for Blockchain solutions. Combined, big data and Blockchain have the potential to leverage each other to unleash innovation and reinvent traditional models.