Thankfully Blockchain technology has emerged out of the shadows of bitcoin and cryptocurrency. Today Blockchain is looked at as an emerging technology that promises to revolutionize the way organizations conduct business. As the bitcoin hangover wears off from Blockchain, we realize the immense potential this technology has – not only alone but in conjunction with other technology trends such as data science.
The role and importance of data science in the enterprise landscape and how it powers business value has also now been established. While we might assume that Blockchain and data science are mutually exclusive, with each having its separate path and use cases, this would be a little off the mark.
So how do these technologies complement each other?
Blockchain essentially is a distributed, decentralized digital ledger that records every transaction and serves as a digital record of transactions. It employs a decentralized database managed by computers to a peer to peer (P2P) network.
Owing to the decentralized nature, there is no one single authority that makes the database transactions free of manipulations and ensures that the transactions taking place in this ledger are tamper-proof. Altering one block means changing all the following blocks making sure that nothing, no change, goes unnoticed.
The blocks in the Blockchain are versatile and can hold different kinds of information in a transparent, decentralized, and tamper-proof manner.
Data science, as we know it, is the science to extract valuable insights and information from both structured and unstructured data to solve real-world problems. The growth of data science can be credited to the meteoric rise of big data. Big data deals with humongous volumes of data that often cannot be managed by conventional data handling techniques.
What becomes obvious here is that both Blockchain and data science have data at the center. The focus of Blockchain is to record and validate data, while data science focuses on deriving meaningful insights from data for problem-solving.
However, sharing, securing, and ensuring data integrity has been a challenge for most data scientists. Since Blockchain manages to solve this core problem of data sourcing, it had managed to grab the attention of data scientists.
So, how what makes Blockchain and data science a match made in heaven?
Since Blockchain employs peer-to-peer relationships, it makes the ledger’s channels transparent. It also shows the user which data is reliable to use, where it came from, how it was changed etc. Blockchain technology makes it possible to trace the entire data history on the distributed ledger from the point of entry to the point of exit. This data traceability makes sure that data scientists get access to the right data and curate the right data sets to enable more accurate decision making.
Today data is more available than ever before. However, the data that organizations want to leverage lie scattered and can take weeks and even months to sort out. It is hard to ignore the negative implications on the time, effort, and resource wastage here. Data integrity can also get impacted owing to human error, which eventually impacts the end analysis. We cannot eliminate the risk of data getting compromised, especially when it is stored in a centralized location.
To deliver on its robust data analysis and predictive modeling capabilities, data science needs access to reliable and strong data sets. The decentralized nature of Blockchain makes it possible for data scientists to strengthen their capacity to manage data and also create a solid data infrastructure where they are sure of data integrity
What are amongst the most significant benefits of Blockchain? A decentralized framework, transaction transparency, and immutable recordkeeping. These factors also render Blockchain perfect for enabling real-time analytics as it allows organizations to detect anomalies in a database promptly.
Data transparency increases greatly when we use Blockchain for data analytics. Organizations can monitor changes in data in real-time using Blockchain. This gives data scientists tremendous opportunities to design algorithms to leverage these real-time changes to design predictive models. They can enable better decision making and prevent malicious activities (such as fraud in banks and fintech).
Data science is hailed for its predictive capabilities. However, the quality of the prediction relies wholly on the data in use. Data scientists can rest easy when it comes to this aspect if they start employing Blockchain data.
Blockchain data, like any other data, can be used to derive valuable insights into behaviors and trends and can be employed to predict future outcomes more accurately. Blockchain capably provides access to huge volumes of structured data for data scientists to play with. Additionally, owing to the distributed nature of blocking and the computational power available, it gives data scientists even in small organizations the capability to undertake extensive tasks involving predictive analytics.
Today security is on everyone’s mind and ensuring data security and privacy become non-negotiable for organizations. Organizations can be assured about the security of information and data within the Blockchain because of its decentralized nature. Because of this decentralization, no one person holds control over it. It is also impossible to change, use or manipulate data without the approval of those involved. While this infuses data transparency into the system and gives data scientists more assurance about the data, it also helps to alleviate risks of fraudulent activities.
Blockchains are ledgers that are maintained by nodes (a computer that contains a copy of the Blockchain data and stays up to date with any database changes). You have to either run your own node, pull from an existing node, or use hosted software that allows you to plug queries directly into the desired network to access the data. Owing to its system, Blockchain can ensure the security and privacy of data.
Blockchain also facilitates improved and more secure data access. Organizations can identify the right users who should be a part of the Blockchain post, which they can securely access data needed for analytics.
Blockchain is a relatively new technology. However, it has the potential to fundamentally change the way we treat and analyze data. The Blockchain ecosystem will only get stronger as it sees wider adoption, and it is quite likely that Blockchain will become integral to data science as well. With strong security protocols and transparent recordkeeping, Blockchain will help data scientists reach many of the milestones that were previously a pipe dream.