Blockchain, according to Janexter’s Maria Weinberger, is the quality of a sizable quantity. This is in line with the idea that blockchain is primarily concerned with data verification, while data science and big data are concerned with drawing inferences from massive datasets.
Blockchain has introduced an entirely new method of data management and operation, shifting the focus from a centralized model in which all data must be consolidated to a decentralized one in which data may be evaluated at the very edges of specific computers.
In addition to being immutable and having a defined structure, data generated using blockchain technology is also validated. Due to the fact that blockchain verifies the authenticity of data through its interconnected chains, the data generated by blockchains can also be used to improve big data in the area of data integrity.
5 Blockchain Use Cases in Big Data
At a minimum of five distinct points of application, blockchain data can be of use to data analysts in general.
Data Integrity Is The Top Priority
The data that is recorded on a blockchain must first go through a verification process, which guarantees the data’s high quality. This makes the data reliable and trustworthy. The fact that all activity and activities that actually occur on the public blockchain can be followed back to their original source further contributes to its transparency.
During the course of the previous year, Lenovo demonstrated the use of blockchain technology that could identify fraudulent forms and documents. The technology known as blockchain was utilized by the industry titans in the PC industry to verify the authenticity of physical papers that contained digital signatures.
Computers are responsible for processing digital signatures, and the validity of the paper is checked using a record stored on a blockchain.
In most cases, data integrity is secured when specifics about the origin and transactions involving a data block are saved on the blockchain & immediately confirmed (or validated) prior to being acted upon. This ensures that the data can never be altered or corrupted in any way.
Protecting Against Destructive Behavior
Due to the fact that blockchain relies on a consensus method to connect with the acquisition, it is mathematically impossible for one unit to endanger the data network. If a node (or unit) in the network begins behaving in an irregular manner, it is simple to identify it and remove it from the network.
Due to how distributed the network is, it is nearly difficult for a woman party to create enough computational capability to alter the analytical tasks and let undesirable data into the system.
A majority of the nodes on the blockchain need to come to an agreement in order to modify the rules that govern the blockchain. It will not be possible for a single villainous actor to achieve this objective on their own.
Predictive Analysis Done Easier Than Said
Similarly to other data types, blockchain data may be studied to learn about patterns and forecast what will happen next. Moreover, blockchain delivers organized information collected from people or devices.
When it comes to business-related social events like consumer preference, lifetime value, dynamic rates, and churn rates, data scientists rely on massive datasets to make accurate predictions. Not only can social sentiments and investment indicators be forecasted through trading bots like bitcoin millionaires with the correct data analysis, but also nearly every event.
The decentralized characteristics of blockchain as well as the vast amounts of available computational power make it possible for data scientists in even the smallest of enterprises to take on significant predictive analysis projects. Data scientists may now examine social consequences at scales previously impossible by tapping into the processing capacity of thousands of computers connected in a blockchain network via a cloud service.
Actual Data Analysis
Transactions that take place across international borders can now take place in real-time thanks to blockchain technology, which has been shown by many financial and payment systems. Blockchain technology is currently being investigated by a number of banks and fintech startups because of the fact that it enables the rapid — in fact, real-time — resolution of significant quantities despite the existence of geographical boundaries.
In a similar fashion, businesses that require real-time data analysis on a big scale might achieve this goal by utilizing a system that is enabled with blockchain technology.
Banks and other organizations, thanks to blockchain technology, are able to monitor changes in data over time, making it feasible for them to make snap judgements regarding whether or not to obstruct fraudulent transactions or keep tabs on aberrant behaviors.
The Ease Of Data Sharing & Managing
In this sense, the results of data investigations might be recorded and kept in a distributed ledger called a blockchain. In this approach, project teams may ensure that they do not repeat the data analysis that has already been done by other organizations or inappropriately reuse data that has already been used.
Also, the use of a blockchain platform may assist data scientists in monetizing their work, most likely through the trading of results of analyses that are kept on the network.
Conclusion
As mentioned, blockchain is still in its early phases, despite the widespread attention it has received recently. More specific use cases, including data science, are expected to be developed and explored as the technology develops and innovations occur around it.
Nonetheless, certain concerns have been expressed concerning its potential influence on data science, and more specifically on big data, which necessitates the management of extremely massive datasets. The high cost of implementing blockchain solutions in this area is a major worry. This is due to the high cost of storing data on a blockchain compared to more conventional methods. When compared to the massive amounts of data acquired in real time for big data as well as other information processing jobs, blocks deal with manageable amounts of information.
As we have seen, blockchain has enormous potential to revolutionize how we maintain and use data. It will be fascinating to observe how the technology develops to solve these concerns and how it disrupts the data science area.
