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Blockchain, Featured, Technology

Blockchain in Data Analytics: Combining Data-Centric Technologies for a Better Tomorrow

The need for data collection has elevated itself from research experiments to the current omnipresent excess. So much so, data analysis has become a separate field of study, its applications will very likely bring the next global revolution. Big data analytics and blockchain technology are the most disruptive technologies in the existing scenario, and it is interesting to see how they can interact and incorporate features from each other to deliver even better results. 

What is Big Data?

People in the current digital age generate a huge amount of data. The huge online data originates from smartphone applications, email, social media platforms, online purchases, and many more. Business corporations collect these huge unorganized data clusters, commonly known as big data. 

Why Big Data Analytics?

Big Data is gigantic in volume has multiple sources of origin. It is a backbone for business, and modern businesses acknowledge its importance. However, data is unprocessed information. There needs to be a conversion system for turning the big data into information with meaningful insights and actionable intel to help people make better decisions. This is exactly why big data analytics is one of the most in-demand jobs in the current market.

What is Big Data Analytics?

In simple words, big data analytics is a process to assess big data and extract useful information from it. Under useful information, organizations look to identify varying patterns, links, customer choices, and existing market trends. Data analytics assist businesses in the proper evaluation of data sets. And, with advanced options such as what-if analysis, predictive modeling, statistical algorithms, and more- big data analytics helps enterprises in making the most optimized business decisions. 

Blockchain Integration into Data Analytics

Another popular 21st-century innovation, blockchain is rapidly rising through the ranks in terms of usability. The underlying architecture behind cryptocurrencies has unique characteristics which improve documentation maximizing the operational efficiency of an organization. Blockchain is widely used in banking, fintech, healthcare, and supply chain management. The sectors make use of data immutability and transparency throughout the network. Experts are optimistic about blockchain’s possible impact on data analytics and for good reason. But before proceeding in that direction, let us discuss blockchain analytics.

What is Blockchain Analytics?

Blockchain analytics is a collective procedure that includes analysis, clustering, and identification of data stored within the blockchain network. Blockchain networks use distributed ledger technology(DLT) allowing the access of a cryptographic ledger to all their network members(nodes). The ledger is a properly time-stamped documentation of transaction records acknowledged by consensus. Blockchain analytics also include visualization and data modeling to provide vital information about the users and transactions. 

At present, the most widespread use of blockchain analytics is in the prevention of crypto-money laundering and fraud. Companies specializing in blockchain analytics ‘scrape’ and provide useful information from the publicly available blockchain data. While the transactions are anonymous in nature, the organizations are able to provide exact relevant data matching a transaction with the associated person or company. 

Also Read: Blockchain and Big Data: An Ideal Symbiosis

Impact of Blockchain in Data Science

Like all innovations in technology, data science has its own sets of drawbacks and limitations. The process becomes much more accurate and functions with higher efficiency. Major existing challenges include data inaccessibility, privacy security, and unclean data.

The implementation of blockchain technology addresses a lot of these drawbacks. Over 16000 data science professionals in a 2017 survey identified incorrect or redundant data as the main hindrance to analytics. The decentralized consensus mechanism combined with cryptography authenticates data- making manipulation impossible across the blockchain network.

The decentralized nature of blockchain also enhances security while upholding data privacy. Cybercriminals aim for centralized system servers as they are easy targets, and the trend is growing with each day. On the other hand, blockchain technology reinstates data control back to individual nodes, making it impossible for attackers to coordinate and organize synchronized attacks throughout the network simultaneously. This is why blockchain networks are known as immutable.

Also Read: Blockchain Integration Bringing out the Best in ERP Systems

Advantages of Blockchain Integration

The relationship between big data and blockchain is analogous to quantity and quality. Big data is used for prediction modeling from huge data chunks, while blockchain is ideal for data substantiation. The verified data churned from blockchain is structured, complete, and scores high in data integrity.  

If we dive deeper, blockchain big data analytics offer the following advantages: 

  • Data Traceability

Blockchain eases peer-to-peer sharing and collaboration. Members can easily access information on data accuracy, preservation, updates, source of origin, and utility through the open ledger channels. Blockchain facilitates data tracking from the start, all the way to the finish.   

  • Real-time Analysis

The decentralized nature of blockchain architecture allows data analysis in real-time, an invaluable feature for detecting dataset irregularities. Blockchain allows multiple persons to collaborate with the same data. 

  • Ensures Data Accuracy

Blockchain stores data in a digital ledger that is distributed across multiple nodes- both private and public. Members within the network cross-check and verify the data- guaranteeing its accuracy.

  • Facilitates Data Sharing

Easy access across blockchain networks saves up a lot of time through efficient data sharing. Thus blockchain optimizes operational processes even in big data analytics. 

  • Improves Trust and Data Integrity

Too much trust places organizations at risk. This is one of the primary reasons organizations refuse data sharing. Blockchain eliminates trust concerns with cryptographic signatures and provenance. Integrity is also safeguarded with a decentralized consensus mechanism.

  • Boosts Security

As explained above, the blockchain’s decentralized structure prevents concentrated central attack attempts for data breaching. Blockchain also ensures the elimination of leakage, as multiple signatures are necessary for data access. In addition, blockchain integration in data science features the following additions:

    • Transaction Encoding

Transactions registered in the blockchain ledger are encrypted with complex mathematical coding. The transactions transform into irreversible digital contracts.

    • Data Lakes

Data professionals track company information across data lakes. Blockchain monitoring records provenance data in unique blocks. A distinct cryptographic key is necessary for accessing the block, and the correct key from the originator further confirms the authenticity of the data.

Final Words

Both big data and blockchain center around data. One conducts analysis for actionable insights, while the other validates and stores the data. The use of algorithms is common in both cases. Blockchain adds value to big data analytics and other advanced technologies(Cloud, AI, IoT) by validating the data used, safeguarding its storage, and facilitating easy access.

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