Practical Approach to Blockchain Analytics and its Future

Satoshi Nakamoto first proposed the notion of blockchain in a foundational article titled "Bitcoin: A Peer-to-Peer Electronic Cash System" a decade ago. In the ten years since then, blockchain has successfully escaped the confines of bitcoin miners' jargon to establish itself in new fields such as banking, retail, automobiles, logistics, healthcare, crowdfunding, and voting. Special blockchain-powered phones and a computer will be available soon.

Because of the buzz surrounding blockchain, many organisations are racing to learn more about it, even if they are unsure how it will affect their operations. According to a global IBM research from last year, one-third (33%) of businesses across all sectors and countries are contemplating or actively using blockchains. The fact that the majority of respondents believe blockchain offers an opportunity is even more encouraging.

Why Blockchain Analytics?

The answer lies in the two intrinsic abilities of a blockchain network: immutability and unparalleled transparency. When a firm employs analytics, it has to rely on data to get desired insights. But what if the very data is unreliable or corrupt? Analytics will yield no useful outcome, rather the risk of messing things up by using that outcome will stare it in the face. Blockchains can bring a huge difference here by verifying the integrity of data. Hence, it stands to reason why blockchain analytics is emerging as one of the disruptive forces that proudly shares the dais with Artificial Intelligence and Machine Learning.

Practical Approach to Blockchain Analytics

The biggest feature that makes blockchain analytics different from all the other current technologies is that it brings end-users, data owners, developers, and analysts together to the same environment. But that causes a new set of challenges. The data existing on-chain and off-chain need to be integrated and data federation practices have to be put in place in order to get the best out of the blockchain data. So, a practical approach would be to find a solution that takes the context of both on-chain and off-chain data and ensures security, query federation, and optimization.

Here are some of the advantages that blockchain analytics offers -

1. Preserving Data Integrity

Quality data is a prerequisite for efficient analytics. Blockchain is basically a shared or distributed ledger of transactions, where a copy of the same information is stored at every node present in the network. This makes it possible for all the participating nodes to validate the transactions and thus data integrity is maintained. In other words, the incidence of falsification of data is ruled out by design. The IBM study mentioned above states that over three-fifths (61%) of companies think blockchain will ensure data quality and accuracy.

2. Data Security

In the world of Big Data analytics, blockchain technology brings in an additional layer of security. Due to the complex network architecture of a blockchain, hacking it is close to impossible. This makes blockchain analytics incredibly safe - and that is what businesses are looking for today.

3. Total Control Over Data

The decentralized nature of blockchain facilitates in establishing a robust data control regime. Connecting to a particular node provides visibility across the whole chain. Though theoretically, a blockchain is unhackable, yet on a rare occasion, if a node is compromised or simply goes haywire, data sitting on it can't be altered. Because in that case the compromised node or access point will be at variance with the remaining nodes.

4. Development of a New Approach to Data Monetization

Some of the biggest players in tech today are earning megabucks by selling consumers' data. But sadly, consumers have little control over the data they generate. Blockchain holds promise for turning this lopsided model on its head. Users worried about their privacy and feeling that data guzzling tech giants are giving them a raw deal can now join data marketplaces like Wibson, or Datum. On such platforms, data owners get financial incentives when they share their anonymized private information with buyers. On the other end of the spectrum, businesses that buy data can rest assured that they are getting the data not without sellers' consent. Data acquired this way is validated for accuracy and so more valuable for blockchain analytics than traditional data.

5. Speedy Audit

Keeping track of data in blockchain is easy as it puts consensus-driven timestamping into operation. A chronological record of transactions is always there to check. This ensures that the analytics is precise and accurate.

6. No Place for Middlemen

The absence of trusted third-parties is an amazing proposition in the future of blockchain analytics. Taking services of a middleman or third-party intermediary to validate transactions is costly and may cause unnecessary delay. Blockchain's unique way of handling validation makes the process faster and cheaper. Upwards of half (51%) of respondents in the IBM study has said that blockchain is capable of reducing transaction cost by eliminating intermediaries.

7. Unbridled Access to Data Through Data Exchanges

Many organizations that don't have a mammoth data repository like some of their counterparts face severe data crunch when embarking on analytics. More often than not, prohibitory cost of accessing data from external sources also throws a wrench in the works. Independent researchers are in the same boat too. Increase in the number of decentralized data exchanges will allow data seekers to address the unavailability issue. So, the future of blockchain analytics is linked to changes in the flow of data.