
Over the last decade, data science and blockchain technology are the two main buzz words on the lips of everyone. These technologies, data science, especially, have impacted the operations of virtually every industry. The positive impacts are significant and more applications are being developed every day. Firstly, what is data science? What is blockchain technolog? Many people have heard these words a million times but have no clear understanding of what they’re all about.
Data science is a field that extracts knowledge and insights for making informed decisions from structured or unstructured data sets. It employs statistical tools, analytical tools, programming language, machine learning, etc. in carrying out these duties. Data science is applicable in every industry.
Blockchain technology, on the other hand, is a decentralized system that allows the recording of economic data such that they cannot be destroyed, manipulated, or stolen. The method of blockchain gains widespread acceptance through the boom of bitcoin and other cryptocurrencies. Now, it is applicable to anything of value.
Data science and blockchain technology have mutually impacted on each other. Most data scientists have developed a deep love for blockchain. They utilize the blockchain to improve many outcomes of data technology. Some of the ways blockchain improves data science include;
· Blockchain makes predictive analysis easy for data scientists. It provides a large structured data obtained from numerous devices. This translates into high computational power. Through this, even data scientists in small organizations can utilize blockchain as a cloud-based tool to predict the outcome of business events, with high accuracy.
· Blockchain can be used to verify the integrity of big data. The data stored on this technology must have passed verification processes. They are confirmed to be genuine and of good quality. Data integrity is a big issue for scientists. Thus, they so much prefer blockchain data to other conventional systems. This process also ensures transparency of operations.
· Another way blockchain improves data science is the prevention of malicious activities on the network. Blockchain is well decentralized and distributed. Conventional data systems may experience threats (validity, integrity, and so on) from a single or minimal portion of data sets. This impacts the other data, but this is not the case with blockchain. A suspicious network can be easily removed without corrupting different data sets. No single network has a significant or determinant influence on the whole network chain.
· Many organizations require real-time analysis of their data. They do not wait to compile, organize, or process data. Real-time analysis is very useful for banks and other financial institutions. Only blockchain technology can make this possible. It performs and records real-time transactions without any delay. Even transactions made over long distances and geographical locations, blockchain provides instant processing.
· Blockchain improves data science through data sharing. Many repetitive analyses are going on in various conventional data systems. With blockchain technology, data analysis can be shared with any member of the network. This prevents the loss of productive time and also allows data scientists to monetize their work.
With further advancements in blockchain technology, more ways by which it improves data technology will be discovered. The two technologies already work hand in hand for other developments. There are prospects for new use cases soon.
READ MORE
Starting a business during the coronavirus pandemic
Can Libra be Zuckerberg's latest goldmine?
Many Jobs that will go obsolete in the next decade
Current surge in cryptocurrency scams; old and new ways
Reason Fintech innovations might soon end banks domination
Impact of COVID-19 on cryptocurrencies
Major altcoins and their unique features
Success stories of cryptocurrency traders
How to become a good cryptocurrency trader
DISCLOSURE
Comments here are not of the author's opinion. Users are responsible for their comments.