Blockchain

Blockchain and big data analytics: How blockchain influences data science

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Blockchain has dominated headlines as the hottest area of technology development over the last couple of years. There are countless ways that forward-thinking organizations can utilize the technology to enhance business. Currently, there are several blockchain use cases in almost every industry. However, the leading areas where blockchain has made tremendous progress are crypto-startups and deep-pocket corporations.

As things stand today, blockchain technology seems to be out of reach for small and medium-sized businesses (SMEs). The digital divide keeps widening by the day. But, the good news is that some blockchain projects are emerging with a promise to democratize the technology for SMEs. Moreover, the same approach could benefit Big Data and analytics.

So, how does blockchain influence data science? There are numerous benefits of combining blockchain technology with big data, and this guide will highlight them. We also look at some real-world applications of blockchain and big data analytics.

Blockchain: A highly demanded skill nowadays

As the number of blockchain projects continues to grow, the demand for blockchain developers has skyrocketed over the last few years. Even freelancing platforms like Upwork have reported that blockchain is one of the most demanded skills. Professionals in other areas like law or IT also have a competitive advantage if they have blockchain skills.

What is data science?

Data science involves extracting knowledge and insights from both structured and unstructured data. This scientific field encompasses data analysis, machine learning, statistics, and other advanced methodologies in analyzing actual processes using data. Data is today considered the new oil. Consequently, tech giants like Google, Facebook, Amazon, and Apple are in control of enormous loads of data.

Data science is helpful in analyzing huge sets of data and drawing useful insights. Some common applications of data science include internet engine protocols, recommender services, and digital advertisement. In fact, data analytics is a key component of data science that has found relevance in the healthcare industry. It helps to track patient treatment and equipment flow. In the travel industry, it enhances consumer experience.

Data scientists are similarly in high demand. Every organization wants to get more insight into their data and solve problems. When considering big data, the need for data scientists is even more pronounced. The advanced aspect of data science deals with extremely huge amounts of data that you cannot handle with traditional data processing methods.

How blockchain can help big data

If big data refers to the quantity, then blockchain could be the quality. While big data analytics focuses on getting accurate predictions from large amounts of data, blockchain technology helps to validate the data. Blockchain has introduced a whole new way of managing and operating with data. Organizations no longer have to look at data from a centralized perspective. The decentralized nature of blockchain means that users can manage data right from the edge of their devices. Moreover, blockchain integrates with a myriad of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and cloud solutions.

Now, validated data that comes through blockchain is structured and complete. The platform is also immutable, so nobody can alter the data. Additionally, blockchain technology ensures data integrity by ascertaining the origin of data through its linked chains.

How blockchain influences data science

There are at least five practical benefits of blockchain technology in big data analytics. They include:

Enabling predictive analysis

Blockchain data can be analyzed to get valuable insights into the behaviors and trends that can be used to predict future outcomes. Moreover, blockchain provides structured data generated from individuals or individual devices. Data scientists apply predictive analysis on large sets of data to determine the outcome of social events with good accuracy. Examples include predicting dynamic prices, customer lifetime value, customer preference, and churn rates in a business. 

The distributed nature of blockchain technology and the massive computational power available through the network allow data scientists to execute extensive predictive analysis even for small organizations. 

Real-time data analysis

Blockchain enables real-time cross-border transactions, just like digital payment systems. Many fintech innovators and banks are now exploring the potential of blockchain in providing real-time settlement of huge amounts of money irrespective of geographical limitations. Similarly, organizations that require real-time analysis of data can benefit from blockchain-enabled systems. Banks can monitor the changes in data in real-time and make quick decisions like blocking suspicious transactions or tracking abnormal activities.

Ensuring data integrity/trust

Blockchain ensures that the data recorded is trustworthy by taking it through a verification process. It also enhances transparency since all the activities and transactions that happen on the blockchain network can be traced back. Lenovo successfully demonstrated this blockchain use case by detecting fraudulent forms and documents. The PC giant used blockchain technology to certify physical documents that were secured with digital signatures. They use computers to process digital signatures. However, the authenticity of the documents is verified through a blockchain record. More often, data trust is achieved when the details of the document origin and interactions can be traced. 

Preventing malicious activities

Since blockchain uses consensus algorithm to validate transactions, a single unit cannot pose a threat to the entire network. Any node that begins to behave abnormally can be identified and expunged from the network. Blockchain is a distributed network, so it is impossible for a single entity to hold adequate computing power to alter the validation criterion and execute malicious transactions. To make any alteration to blockchain rules, the majority of the nodes must be enjoined to reach a consensus. 

In conclusion…

Blockchain technology is surely revolutionizing the field of data science. While the technology is still in its infancy stages, the wider public adoption will make the ecosystem more robust. Developers will continue improving on the building blocks that are already in motion. There is no doubt that blockchain technology will take data science to a whole new level. However, the challenge is that we don’t have many blockchain systems on an industrial scale. The future is promising, though, as efforts to create blockchain as a service (BaaS) intensify.

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