How To See Data Science Intersecting With Blockchains?

Data Science is one of the hottest tech industries right now and is seeing many innovations in its various sub-domains like Descriptive Analytics, Diagnostic Analytics, and Predictive Analytics. Recent estimates by IBM research put the growth in Data Science related jobs at around 28%, the highest among any field. Around 59% of these jobs will be related to Finance and Insurance, Professional Services, and Information Technology, according to IBM research. Therefore, it is quite natural to ask how Blockchains, another revolutionary FinTech, can work together with Data Science to develop both these areas? Here’s an in-depth look at both technologies.

Data Science – Basics

Data science refers to the use of a variety of scientific methods, processes, algorithms, and systems to extract knowledge or insights from data sets. Sometimes referred to as Big Data analytics, Data Science essentially provides insights that are easy to miss from just looking at the data. Insights were collected using Linear Regression, Logistic Regression, Pattern Recognition and other sophisticated mathematical techniques. It has a wide range of real-world use cases, like voice recognition, self-driving cars, and spam recognition. One of the core goals of Data Science includes using Big Data analytics to make machines more autonomous so that they can function without any human intervention.

Blockchains and Data Science

Blockchains are essentially the internet’s “trust protocol,” which means they help secure sensitive information on the internet. However, keeping information secure means there is a cost to storing data on the blockchain, which is forbidden for Data Science. This is because Big Data analytics relies on analyzing huge amounts of data to come up with models. Instead, Blockchains can help by making data stored in other servers more secure using timestamps and proof of ownership systems like Factom. In addition, Blockchains must have the potential to improve several key elements of Data Science related to data collection, distributed computing, and predictive analytics.

    • Collect data – Data is the mainstay of Data Science models. For example, to create a model for self-driving cars, Data Scientists require millions of hours worth of real-life driving data. Collecting and organizing this data accounts for a large proportion of the total work. Since these Artificial Intelligence models are based on the principle of “Garbage in, Garbage out”, it is essential to ensure that the data is authentic and untested. This is one of the main uses of Blockchains as they can help bypass intermediate sources of error. Using data integrity services like Factom, we can provide data driving directly to data scientists. This ensures the quality and authenticity of data while significantly increasing speed and reducing overall audit costs.
  • Computer distribution – After the relevant data is collected and processed, it still has to be analyzed. Analyzing this huge amount of data requires a huge amount of processing power. Individuals rarely have enough computing power at their disposal and thus depend on expensive cloud computing platforms such as Google Cloud and Amazon Web Services. The Ethereum-based Golem project is working on the implementation of the world’s largest decentralized supercomputer that will give individuals the right to purchase computing resources directly from those with idle computers. Decentralized computing not only reduces costs for individuals, but also makes the process more secure because no third parties are involved.
  • Predictive analytics – Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to determine the likelihood of future outcomes based on historical data. Existing machine learning methods have proven ineffective when it comes to predicting complex social outcomes such as election results. Predictive analytics, using the wisdom of crowds, has proven to be quite accurate when it comes to accurately predicting social phenomena. Several studies related to predictive analytics have shown that since each person in a crowd has his or her own biases, collecting a large sample of a crowd eliminates all members of the crowd. competitive individual opinions, thus providing very reliable, accurate predictions. Augur and Gnosis are two blockchain-based projects that already have an active platform for staking related to such a phenomenon.
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