July 14, 2020

Why Blockchain is Important for Data Science?

Data science has become the central part of almost every business and every government. It serves as the key decision-maker for the smooth running of organizations. Today, data scientists are finding ways to maintain data security and data transfer. That’s where blockchain technology has come to the rescue.

When you relate blockchain only with bitcoin, then the application of blockchain becomes narrower. According to the bitcoin circuit, both bitcoin and blockchain have a bright future. For more information visit the official site by clicking given below image

However, blockchain is not just limited to bitcoin or other crypto coins, it has a lot of other applications. If you look at blockchain as a distributed ledger technology, then you can find its true potential. Here is how blockchain is helping data scientists for big data analysis.

Blockchain Implications in Data Science

Well, blockchain is playing a vital role in the field of data science course by providing a secure and transparent platform for data integrity. Read on to understand why data scientists need blockchain technology.

Fosters Data Tracing

Blockchain fosters data tracing, so every user in the distributed ledger can review the data from the entry point to the exit. That means you can trace the data from where it comes when it comes, who entered the data into the network, and how you can use it in the right way. As blockchain is a peer-to-peer decentralized network, every peer can see the whole process of how data works in the network.

Real-Time Data Analysis

Have you ever worked in a google sheet or google doc sharing with your peers? If somebody changes something in the document you can see it in real-time. Now you can relate it to somehow the same thing in the blockchain network. If something changes in the network, you can see them in real-time. Mostly, banks and financial organizations are having trouble dealing with data in real-time. And this is the reason they are not able to detect fraudulent activities. With blockchain, it is now easy to access real-time data and detect anomalies.

Easy Data Sharing

Data sharing within organizations is one important factor. Conventional data sharing like handling paper records in offices are the most hectic task. Because sometimes it is very hard to provide the data in the right place at the right time. It is time-consuming, the data sharing process is very complex with paperwork, even the data can be altered at times.

However, with blockchain data sharing is easy and many people have access to the same data in real-time. More importantly, the data in the network is immutable that means can’t be modified by anyone. As a result, data sharing becomes easier, efficient, and transparent. That’s why data scientists are very much interested in blockchain technology.

Data Integration is Another Reason

As we are living in a data-driven society, companies now have to deal with numerous data. They need more storage capacity for storing data. However, companies were successful to resolve the storage problem by the end of 2017. But they are now facing a lot of problems for verifying those data.

Verification and authentication of data have become the most challenging task for data scientists. Because the data they have collected are from various government offices, data centers, and even from social media which may contain inaccurate information.

Now blockchain can help data scientists to verify data at every point of the blockchain network. Moreover, the data can be tracked at every stage, and can’t be easily hacked or altered. Because the data is protected by multiple signatures within the network.

Wrapping Up

Data science is an ever-changing field and it will continue to emerge over the future. Because businesses and organizations are in a constant strive to unfold new ways to maintain data. Data scientists have now accepting blockchain as a reliable medium for storing data. Despite the fact that blockchain is a young technology, it has proven to work in the initial trials.


About the author 

Imran Uddin

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