5 ways blockchain is impacting the data analytics industry

What is Blockchain?

Cryptocurrency Bitcoin was the first Blockchain utility. Based on its huge success, 1000’s such blockchain-based mostly cryptocurrencies have been developed which can be generally known as alt-cash. This know-how is in comparison with the innovation of double-entry accounting. To be doubly positive is certain to deliver a revolution in the business world.

Blockchain is especially the distributed ledger that data financial transactions which anybody can join however can’t be manipulated.

Blockchains could be divided into two types – Public and Private. Private blockchains help to learn and write entries to established contributors with required permissions. On the different hand, a public blockchain can be an element of any node on the web, and understanding/transactions could be noticed by all the nodes which can be connected. Public blockchain doesn’t need any permission to enter the transaction information. Cryptocurrency falls beneath the public blockchain.

What is Data Analytics?

Data analytics is the process of studying the raw data to find trends that help in getting the solutions to make knowledgeable business choices. It pulls data and wisdom from structured and unstructured data with the help of machine studying and different superior methods to make use of and analyze the data.

The organizations are functioning on their progress equipment on the gas of data. This data is structured, mined, and cognitively analyzed in various business functions. For example, in the healthcare industry, data science is useful to trace concerned person remedy and gear circulation, in the journey enterprise, it is employed for bettering client support and expertise on plenty of extras.

Relation between Blockchain and Data Science

This space has not been researched a lot. However, the regular hyperlink between the two is that individually applied sciences have data at the heart. While blockchain data validates data, data science provides significant insights for downside-fixing and determination-making.

Both the used sciences use algorithms to perform together with different information segments. In a nutshell, blockchain is for data integrity and data science for projections.

How Blockchain Can Help Big Data?

You can say that if big data guides the quantity of data, blockchain refers to the quality of data.

With Blockchain, a new method of handling data is possible. It has eliminated the requirement for the data to be obtained together and has paved the way to a decentralized structure where data research is possible right from the edge of individual devices.

Additionally, data developed through blockchain is validated, structured and inflexible. Since the data supplied by blockchain is ensured data integrity, it improves big data.

Today, most companies are looking towards deeper, advanced analytics as data has become more affordable and robust. Presently, the data that businesses use is mainly scattered which demands weeks or months of effort to sort out.

The integrity of the data can be affected greatly by any sort of human error, affecting the end analysis. Data also faces the risk of being compromised when it is stored in one centralized place. There is also the chance of data centers being tampered with and getting fired to the public.

Everyone likes requirements, but it is a massive chore to ensure that it is correct and secure. For executing data analysis and predictive modeling, data science requires a functional and solid data set. With a decentralized blockchain, data scientists can strengthen their capacity to manage data and also set a solid infrastructure.

Did you know, that recently a consortium of 47 Japanese banks signed up with a blockchain startup called Ripple to use blockchain for enabling money transfers between bank accounts? The motive behind this action was to significantly decrease costs while performing real-time transfers.

As you know the traditional real-time transfers are a bit high on the cost side as the potential risk elements are huge. One of the problems with real-time transfers is double-spending.

This can be curbed by using blockchain technology. One of the causes traditional real- time transfers were expensive was because of the possible risk factors. Double- spending (which is a form of transaction failure where the same security token gets used twice) is a real issue with real-time transfers.

Other than banking fields, many enterprises have adopted blockchain with security in mind. Diverse companies, from retail, and healthcare to public administration have created their blockchain journey to prevent data leaks and hacks. Blockchain is the future of data science.

5 Blockchain Use Cases in Big Data

There are at least five distinct ways blockchain data can help data scientists in general.

Ensuring Trust (Data Integrity)

Data recorded on the blockchain are trustworthy because they must have gone through a verification procedure that provides their quality. It also supplies transparency, since activities and transactions that take place on the blockchain network can be traced.

Last year, Lenovo showcased this using point of blockchain technology to detect fraudulent documents and records. The PC giants employed blockchain technology to validate physical documents which were encoded with digital autographs. The digital signatures are formed by computers and the authenticity of the document is verified through a blockchain record.

Most times, data integrity is ensured when elements of the origin and interactions about a data block are stored on the blockchain and automatically verified (or validated) before it can be acted upon.

Preventing Malicious Activities

Because blockchain uses a consensus algorithm to confirm transactions, a single unit can't pose a threat to the data network. A node (or unit) that starts to act abnormally can efficiently be recognized and expunged from the network.

Because the network is so distributed, it makes it almost impossible for a single party to develop enough computational power to change the validation criteria and let unwanted data in the system. To alter the blockchain rules, a majority of nodes must be pooled together to make a consensus. This will not be possible for a single bad actor to accomplish.

Making Predictions (Predictive Analysis)

Blockchain data, just like other kinds of data, can be interpreted to reveal valuable insights into the behaviors, and trends and as such can be used to forecast future results. What is more, blockchain delivers structured data collected from individuals or individual devices.

In predictive analysis, data scientists based on extensive sets of data to decide with good accuracy the result of social events like consumer choices, consumer lifetime value, dynamic prices, and churn rates as it connects to businesses. This is, however, not limited to company insights as almost any event can be expected with the right data analysis whether it is social sentiments or investment markers.

And due to the distributed nature of blockchain and the large computational power available through it, data scientists even in smaller organizations can undertake comprehensive predictive analysis studies. These data scientists can use the computational power of several thousand computers linked to a blockchain network as a cloud-based service to explore social results on a scale that would not have been otherwise possible.

Real-Time Data Analysis

As has been exhibited in financial and payment systems, blockchain makes for real- time cross-border transactions. Several banks and fintech developers are now exploring blockchain because it affords fast — really, real-time — settlement of massive sums irrespective of geographic barriers.

In the same manner, organizations that need real-time analysis of data on large scale can call on a blockchain-enabled system to accomplish this. With blockchain, banks and other organizations can observe modifications in data in real-time making it possible to make fast decisions — whether it is to block a suspicious transaction or track abnormal activities.

Manage Data Sharing

In this regard, data gotten from data studies can be stored in a blockchain network. This way, project teams do not duplicate data analysis already taken out by other teams or wrongfully reuse data that’s already been used. Also, a blockchain platform can help data scientists monetize their work, probably by trading analysis results stored on the platform.

In Conclusion

Data Science is a field that is constantly growing. With the integration of blockchain technology, transparent record-keeping and robust security will develop into a reality, and thus, data scientists will be able to get several milestones that were formerly considered impossible. Though blockchain is relatively a unique technology, the primary results from some of the organizations that have been experimenting with them confirm that it can be used actually.

Currently, blockchain is always in its developing phase; this is not very clear due to the publicity that is surrounding it. As and when the technology develops and more innovations take place, there will emerge more substantial use cases and data science can be one of the areas that will greatly profit from it.

Having said that, some questions have been raised about its effect on data science, especially in big data where large volumes of data ought to be handled. One major problem is that executing blockchain applications in this respect will be expensive. This is because keeping data in the blockchain is costlier compared to the traditional means of data storage.

Relatively small volumes of data can be stored in blocks which might prove to hinder some as enormous volumes of data are collected per second for big data & data analysis tasks.

It remains to be seen how blockchain will develop to address these problems and go on to disrupt the data science space. One thing for sure is that this technology has huge potential to change how data is managed and used.