What does blockchain mean for data science?

Since the internet is leading a transition into networks and platform-based social and economic models, blockchain is likely to become an answer to a demand of Internet-age market transactions. Its underlying solution is the technology’s intelligence layer which was initially designed for cryptocurrency transactions, such as Bitcoin, and applies advanced machine learning and systems to cryptographic platforms. It is therefore fundamentally a new paradigm for organizing any kind of transactional activity cheaper, with more efficiency, and at much greater scale.

Business network and shared ledger are two main concepts of blockchain. Business network results with the existence of decentralised IT architecture containing nodes – market participants who validate and run transactions to reach consensus on a shared ledger – a record of all transactions visible for every participant. It assures that transactions are duplicated and permissioned. Companies can own multiple ledgers for various business networks. Smart contracts include a set of business rules – rules that are embedded in the programming language of blockchain, and enable executing, validating and approving transactions. The concept of consensus demonstrates that each entry in the ledger is synchronized to all ledgers in the business network and assures their compatibility. It lowers the risk of fraudulent transactions as any interference would have to happen across several places at the same time. Privacy and confidentiality occurs through the ability to protect records through encryption using private and public keys – personal digital signatures.

The 2015 World Economic Forum, underlined the concept of blockchain as one of the six future megatrends for the next decade. A possible impact on transaction activities is that none of these are likely to become a reality unless governments and businesses adapt quickly to the new platform. This would likely facilitate automated processes using big data and analytics and improve the accuracy of resource planning.

An example of an organisation which uses smart contracts translated into limiting human input in its functionality is La’Zooz. It is a transportation platform which is a blockchained version of Uber. Owned by the community it utilizes unused vehicles’ space in order to reduce road traffic. The La’Zooz network is stored and used only on the phones and computers of users rather than servers, indicating the emergence of another type of information – consensus data. Consensus data is data with confirmed quality, accuracy and trusted public value. Since blockchain technology contains blocks with codes that include part of previous block it prevents from tampering and assures immutability of transactions. As the process is highly automated it also reduces the cost of maintaining and designing processes for the IT infrastructure.

A substantial limitation of blockchain in terms of its performance – enabling processing 1 transaction per second with a maximum of 7 -is considerably weaker compared to VISA Credit Card (upper limit of 10,000). This creates a serious obstacle for companies dealing with thousands of transactions a day. Since processing speed is slower than in a traditional database it might not provide with an inaccurate real time reflection of transactions. Transparency of the technology might also limit the range of businesses willing to apply blockchain in their IT architecture. However, the most worrisome issue against its security is a threat of 51% attack, where one mining party can take control over the entire blockchain and double-spend previously transacted assets into the party’s own wallet/account.

Further actions preventing occurrences of those issues for developers are improving performance of the technology, and for the governments to take into account controlling cyberspace, balancing data access with data privacy and reducing borders between information control and data.

About the Author

Paulina_FigolPaulina Figol: I am a Data Analyst working for IBM in Leicester, UK. I have graduated with a Masters degree in Applied and Financial Economics at the University of Nottingham. My master thesis was concentrated on macroeconomic factors influencing the price of Bitcoin in China. In IBM I am currently engaged in databases architecture, as well as data migration and a member of IBM Blockchain and Data Science communities.

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