Blockchain for Big Data (Part 72)
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Welcome to the 72nd part of the 100-part series on Blockchain.
The big data era is upon us. Every single day a huge amount of data is getting generated. Facebook alone has more than 300 petabytes of data which contains personal profiles, pictures, videos, and messages of its users. The generation and availability of such a huge data set has led to the introduction of various new domains like Data analytics, Big data, etc.
Data collection and Data analytics have lead to the understanding of the why, when, who, how, and what of human behavior. And this understanding has fired innovation and development across many fronts, be it economical, social, political, or medical. Big data is undoubtedly a big asset for the global economy.
Big data consists of a massive volume of both structured and unstructured data, which is so humongous that it is nearly impossible for traditional systems to process and analyze it. Though companies are collecting data to give their customers a better experience, there are massive challenges around this data, like data privacy and data security to name a few. There have been many cases of data theft, and misuse of personal data that have lead to a sense of insecurity and distrust in this ecosystem. But at the same time, the potential of big data in making striking contributions to the development of global economy can’t be neglected.
And therefore, the need of the hour is to develop an ecosystem where the benefits of big data can be reaped without compromising the security and privacy of the users’ data. Enter Blockchain Technology. Blockchain is a perfect companion for big data that can complement it to mitigate these issues. Blockchain further helps in better data management of huge volumes and variety of data that is getting generated incessantly day in and day out. Let’s discuss the benefits of Blockchain for Big data in detail:
Economical Data Storage
As discussed earlier, big data involves massive volume of data and therefore it becomes very crucial to have the right infrastructure to store this big data. Besides, the volume of this data keeps on increasing day by day.
Storing such voluminous data using conventional cloud storage doesn’t prove to be an economical option for the businesses. Additionally, in many cases due to certain business requirements, multiple copies of the same data are maintained at different locations, which further leads to an extra burden for the businesses. But with the implementation of Blockchain these costs can be reduced as distributed file storage IPFS provides a more economical solution for data storage. The hash of the uploaded data is then stored on the Blockchain and accessed through the smart contract. Any modification in the uploaded file would change its hash. Anyone knowing the hash of the file can retrieve the file from IPFS and read its data. IPFS has been explained in detail in Part 18.
Trustworthy External Data
In today’s digital age, there are too many touch points that a consumer or a potential consumer interacts with. To get a better understanding of their customer, every business collects data from various sources like social media, external research, purchased lists, etc.. There is always a lack of trust for such external data as businesses can’t verify its authenticity. Even though the external data is fed into the internal analytics system but the insights generated are not foolproof and, therefore not reliable. But with the implementation of Blockchain, it is much easier to establish the veracity of this external data as the complete path of the data collected can be verified using the immutable record on the ledger. Therefore implementation of Blockchain in big data analytics brings in a higher degree of confidence for the data collected from external sources.
Data Traceability and Auditing
With the implementation of Blockchain, datasets become immutable and completely traceable. Blockchain technology provides data visibility and data consistency to all the stakeholders involved the network. The datasets can be audited as the complete data trail is available on the shared ledger right from its source. In particular, Supply Chain data analytics require this kind of data verification for generation of better business insights. Additionally, Blockchain technology rejects any dataset that can’t be verified and it is even marked suspicious. Because of which the insights are generated only from verified datasets thus making them more valuable and accurate.
Secure Data
Data security is one of the key concerns for any big data analytics operation. But with Blockchain it can be mitigated. And that is exactly why Blockchain has been getting accepted by many of the financial institutions. Additionally, Retail and Healthcare Industries are ripe for disruption by Blockchain as they are in dire need of a platform that can provide security to the sensitive information residing with them.
Real-Time Analytics
One of the most exciting use case of Blockchain in Big Data is its potential to detect real-time fraudulent activities. In the current scenario, banking and other related institutions have always relied on reactive or retrospective data analytics to detect any form of fraudulent activities. Simply put fraud can only be identified once it has happened, and there is no way to stop it from happening in real time. But with Blockchain, banking and financial institutions can check every transaction in real time. Thus, predictive analysis can be carried out on this up-to-date data to find patterns of risky or fraudulent transactions on the fly and prevent any possible mishap.
Scalability concerns
(i) Storing the entire data on Blockchain is very costly and will significantly slow the processing on Blockchain. Thus, distributed file storage IPFS can provide low-cost off-chain storage to store data, and the hash of the uploaded file is then stored on the Blockchain and accessed through the smart contract. Any modification in the uploaded file would change its hash. IPFS has been explained in detail in Part 18.
(ii) To make the Blockchain scalable, layer 1 (discussed in Part 15) and layer 2 (discussed in Part 16) scaling solutions will be required to be implemented.
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