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Leveraging Blockchain for Data Analytics

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Aditya Kaul, Sr. Director - Insights & Analytics, Isobar IndiaWith the potential to disrupt and transform every industry it touches, Blockchain technology is poised to drive the future. In simple terms, Blockchain is a distributed ledger that records transactions in such a way that they cannot be altered. Although best known for powering cryptocurrencies, Blockchain use cases spread across financial services, healthcare, music, governance, cloud computing, and even our identities. Ability to decentralize, and store anything of value in a secure and anonymous system that is controlled by no one entity can revolutionize data management.

Taking third-party out of the equation

Businesses are now transforming into platforms, which are collecting data and learning from them at scale to make strategic and economic decisions. A machine-learning algorithm can do much better job in making decisions as it can crunch more data. However, the mode of acquisition of data poses a significant challenge for intenders trying to obtain it for various purposes. Given the nature of big data, it requires high storage capacity servers for efficient storage. These servers are owned by a group of companies or individuals, which leads to a centralized data storage environment. These third-party data providers have the singular privilege to modify and expensively price the data in their possession.

Further, a centralized approach dramatically limits the reliability of data because of the single point failure associated with it. Blockchain technology may provide reliable data at no charge where all the decision-making depends equally on all connected nodes and hence, no single point of failure.
Sharing of data within nodes imply a significantly higher amount of data within the chain, which can be fed directly and freely to machine learning models without any third party assistance.

The ultimate aim of pre-processing data is to clean data, extract features from data, and normalize the data.



Consider Golem, potentially a decentralized alternative to today’s centralized clouds, which are run by large technology companies like Amazon. Golem aims to harness the power of billions of devices used daily to distribute computation. Users use Golem Network Token to pay and get paid on their platform. They are actively investigating in training machine learning models intending to provide developers with a set of tools to enable them to host their machine-learning stack on Golem.
Another example close to home is Isobar using Stellar Blockchain protocol powered by AdsDax to deliver India’s first Auto Industry Blockchain based campaign. The campaign aimed at creating a media ecosystem devoid of fraud and increase efficiency & transparency, Stellar was used to scrub down the master audience set based on relevancy. Real-time ad verification and fraud protection were achieved at scale resulting in a high viewability and engagement for the served assets.

Not just safety but ensuring the quality of data

The world is generating 2.5 quintillion bytes of data every day. However, it is not only the quantity of data but also the quality of data that matters too for driving insights. The decisions derived from data is as good as the quality of the information itself. In order to obtain an accurate result for a particular model, the data must be prepossessed to remove unclean, redundant, irrelevant, and noisy data. The ultimate aim of pre-processing data is to clean data, extract features from data, and normalize the data. Blockchain solutions can add veracity to 3Vs of big data (volume, velocity, and variety) and bring a high level of reliability and trustworthiness by addressing issues like human error, incorrect information, data duplication, and more. at the source.

Incorporating Blockchain databases in machine learning means having a shared, relatively much more significant and safer data, which in turns means better models with better throughput, thus making more efficient and reliable systems. Brands could reach out to a sharply defined audience without invading their privacy and ensuring that compensation of data goes directly to data providers.