Blockchain and Big data are amongst the top emerging technologies tipped to revolutionize many industries, and to considerably change the way the industry affairs are run. As a result, one might think that asides being tools utilised to bring about the Fourth Industrial revolution, these technologies are mutually exclusive, with each running on parallel lines with no point of intersection or correlation. However, that would be off the mark.
Data Science, just like Blockchain, is gradually transforming the way several industries operate. Whilst Data science focuses on interpreting data and getting useful and actionable insights from it, Blockchain ensures that this data is secure by storing it in a decentralized ledger.
However, the big question is, how are these two fields interrelated? To answer this question, it is quite pertinent to understand the basics of these two technologies, independently.
What is Blockchain
Blockchain is a decentralized, immutable and transparent digital ledger, which is used for recording transactions. The technology became profound as a result of the growing interest in bitcoin and cryptocurrency and it has since then, found relevance in not just recording cryptocurrency transactions, but virtually anything of value. The seemingly endless capabilities of this emerging technology have resulted in its widespread demand, all over the global space.
What is Data Science
The mantra: ‘Data is the new oil’, is no longer news to anyone. Data Science is a field that seeks to transform data and the knowledge gotten from it, into actionable insights for making decisions that will drive growth to an organization. The big players in this industry are the GAFAs (Google, Amazon, Facebook and Apple). The Data science field encompasses Statistics, Machine learning, Artificial intelligence and Computer programming.
The Relationship between Blockchain and Data Science
Unlike other industries such as Fintech where Blockchain is now profound, the possibilities of the Blockchain technology have not been fully explored in the field of Data science, and quite interestingly, for some people, it is virtually non-existent.
For starters, both Blockchain and Data science deal with data. Whilst Data science extracts data and transforms it into actionable insights, Blockchain validates and secures this data. Both are actively playing a key role in the way Data is extracted, analysed, recorded and secured, hence, the theme: “Data science for prediction, Blockchain for data integrity”.
Similarly, Data Science, just like every other technological field, has its own challenges. According to a survey carried out in 2017, the biggest challenge to Data science, asides privacy issues, is dirty data (Wrong Data).
This loophole of erroneous information or duplicate data which the field of Data Science often battles with, is an area Blockchain comes into play. Through its decentralized system, Blockchain ensures the security and privacy of data. Most data are stored in a centralized database (which often make them vulnerable to cyber attackers); however, with Blockchain, the control of data lies with those who generate it. As such, it is often quite difficult for cyber attackers to access or manipulate data. Due to its decentralized system, blockchain brought about a new way of managing and operating data— regardless of the size of the data involved.
Furthermore, blockchain technology constitutes a viable resource in Data Science through the provision of efficient data sharing. Once data is stored on the blockchain, it is easy for project teams to identify data that has been used and to refrain from repeating data analysis already carried out by other teams. A blockchain platform can also help Data scientists monetize their efforts by claiming intellectual property rights on the results of their analysis without fear of theft or plagiarism.
In conclusion, although, blockchain technology is still in its early stages, there are seemingly endless possibilities already identified with it. It is expected that upon maturity, blockchain would be transformed from being an innovation solely utilised by the technologically savvy population, to becoming a daily tool for numerous people and industries— including Data Science.
