In part 1, we have explored the differences between blockchain and data science as well as the ways they can be used together. In this part, we continue in that manner, with blockchain use cases in data science.
How Can Blockchain Help Big Data?
While Big Data focuses on making predictions from large amounts of data, the blockchain is involved in validating that data.
Blockchain is bringing a whole new way of management and operations — there is no longer a need for a centralized place where all data must be stored. Decentralization allows data to be analyzed right off the edges of individual devices. Also, everything can be integrated with other new technologies such as Artificial Intelligence, Cloud Computing, and the Internet of Things with the use of Blockchain.
Blockchain Use Cases in Big Data
There are several ways blockchain can help data science in general.
Ensuring Data Integrity
Data recorded on the blockchain are immutable and verified, which makes them trustworthy. An additional plus is a transparency, as everything that takes place on a blockchain network can be traced.
Most times the data integrity is ensured with the details of the origin. If those details are stored on the immutable blockchain, they can be automatically verified or validated.
Preventing Crooked Activities
Since the consensus algorithm is used in the blockchain, it is impossible for a single machine to pose any threat to the network. A node that starts acting strangely can be easily detected and expelled from the network.
Since the network is decentralized and distributed, it is almost impossible to gather enough computational power to change the validation criteria. To do so, 51 percent of the nodes have to be pooled together in order to form a consensus. This is extremely difficult to achieve, making the blockchain very safe and secure way of validation.
Blockchain data can be analyzed to find valuable insights into the behaviors, trends and predict future outcomes. The blockchain structures the data from individual devices and persons, making the analysis way easier.
In the predictive analysis, data scientists use large sets of data to determine the outcome of social events with accuracy. Social events can involve customer preferences, dynamic prices and similar things related to business. This, however, does not exclude the predictions of other types of events. Social sentiments and investment markers are just some of the things that can be predicted.
Due to the decentralized nature of blockchain and the huge computational power that comes with it, data scientist even in smaller organizations can partake in expensive analyses. By using the power of thousands of computers connected to the blockchain it is possible to analyze social outcomes in a scale which wouldn’t be possible otherwise.
Blockchain is one of the biggest innovations in the financial industry. The potential of technology also lies in real-time settlements of huge sums despite geographical barriers.
In the same way, companies that need real-time analysis of data on a large scale can use blockchain. Banks and other fintech companies can follow changes in data in real time, allowing them to make quick decisions — anything from blocking a suspicious transaction to tracking abnormal activities.
Data stored on blockchain can be easily shared. Project teams do not need to repeat analysis already carried out by other teams, they can simply use the results of their colleagues. Blockchain can also help data scientist monetize their work by trading analysis outcomes on the platform.
Blockchain has a lot to offer to data science. Depending on the ways blockchain will evolve big data can be disrupted more or less. It remains to be seen.
Still, the potential of this technology is undeniable.