3 Use Cases of Blockchain in Analytics

The mix of Blockchain and Analytics could open up plenty of endless opportunities. So, what’s the connection? If Blockchain is the quality, then Analytics is the number. According to Gartner, blockchain will generate $3.1 trillion in new business value by 2030. Here we discuss how Blockchain combined with Analytics has benefitted various sectors in different ways.

Use cases of various industries using Blockchain with Analytics

Blockchain has proven its ability to revolutionize various industries due to its decentralization, transparency, and immutability properties, which are helpful to businesses all over the world. Many businesses have risen to tremendous heights as a result of combining blockchain technology with analytics in this fast-paced digital era. Here are a few real-world Blockchain with Analytics use cases from varied sectors.

How Blockchain makes an impact in Healthcare Analytics?

Blockchain is used to improve Customer Experience as it promotes customer trust by increasing openness in business and providing on-demand data visibility.

Take an example. According to NCBI, Personal Health Records(PHR) have lately begun to be created using data from wearable sensors or medical IoT devices. Patients, physicians, pharmaceutical researchers will have access to real-time Artificial Intelligence (AI)-powered healthcare Analytics. For Blockchain service providers, the complete PHR service trajectory is becoming a rich source of data.

Developed on the Blockchain, distributed or decentralized applications (Dapps) enable physicians and patients to easily participate in telemedicine with no middleman costs other than the Ethereum network’s minimum fees, improving patient empowerment.

From the real use case discussed here,  it reduced the usage of middlemen and provided an enriching customer experience for the patients.

How does Blockchain help in Fraud detection in the financial sector?

Blockchain allows real-time information exchange via a network of connected devices, with all nodes in the system verifying the transaction. Since every user on the network has a copy of the entire data on the Blockchain, it is almost impossible to modify the transaction.

Consider this example discussed from KDnuggets. A group of 47 Japanese banks struck an agreement with a blockchain firm to use blockchain for simplifying money transfers between bank accounts. The key motivation for doing so is to do real-time transfers at a much lower cost. With real-time transfers, double-spending (a type of transaction failure in which the same security token is used twice) is a serious issue. This risk is greatly avoided with blockchains. Big data analytics allows for far faster identification of patterns in consumer spending and dangerous transactions than is now achievable. With real-time transactions, this lowers the cost.

When Blockchain is used with Analytics, the previously prone error of Double Spending got greatly avoided thus helping in fraud prevention in the above-discussed case.

How Blockchain combined with Analytics helps in Agriculture?

With Blockchain combined with Analytics, it helps in boosting your business thereby eventually building up profit margins.

Take an example. Ripe.io, an agri-tech company, uses tomato production traceability data to link the flavor of ripened tomatoes to growth circumstances. The company was able to grow different sorts of tomatoes based on the needs of different customer segments thanks to predictive analytics. To enhance the performance of predictive analytics, the firm used blockchain to obtain reliable data from customers.

In the case discussed above, the firm used both Analytics and Blockchain to help in better-spoiled management.


Analytics and blockchain technology when combined can have vast potential to revolutionize how we process and understand data. We can expect to see an increasing number of organizations striving to tap into this powerful partnership which involves a decentralization approach, to staying ahead of the pack in the race to gather more high-quality and secure data.


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