Blockchain is a decentralised digital public ledger that utilises a technology to record transactions across several computers connected through a peer-to-peer network. It was first intended for digital assets such as Bitcoin and Ethereum, but new prospects have revealed themselves in recent years.

Apart from the aforementioned applications, blockchain technology has enormous promise in the field of analytics. For some years, modern firms have benefited from data analytics. According to Forbes, business usage of data analytics surged from 17% in 2015 to 59% in 2018. Currently, just 10% of organisations have declined to adopt big data.

Predictive analytics is one subset of data analytics that is poised to disrupt and reshape the business. It is focused on forecasting future events using a huge quantity of previous data and machine learning algorithms. Enterprises will be able to foresee trends and behaviours using this sort of technology.

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Predictive analytics is far from ideal at the moment. A significant hurdle to overcome is obtaining and comparing high-quality data from diverse sources. Digital agencies and information technology organisations have their own data silos and collect data using a variety of different technologies. Additionally, there is the question of whether there is an enough amount of the appropriate data. When there is insufficient data to draw conclusions, the system’s predictions may be skewed and unreliable.
This article discusses how blockchain’s decentralised nature might aid in the security of linked devices and systems.

Convergence of data and Blockchain

Blockchain technology may be able to fill this need. Since blockchain uses several linked computers to compute power, it can appropriately construct the model to be evaluated based on a large number of datasets. It would utilise its computing capability to search for relevant data across computers and retrieve it.
The cloud equivalent of one physical supercomputer, blockchain may also be available to small firms. Currently, organisations using predictive analytics must depend on pricey supercomputers. With blockchain, such analytics tools will be more affordable.
Applications for blockchain analytics include marketing. Market data might help marketers plan future marketing strategies. The technology may be able to foresee cryptocurrency price changes. The convergence of data and blockchain technology will develop in the next years. According to a MicroStrategy 2018 survey, 25% of companies want to move their analytics platform to the cloud in the next 5 years. As developers continue to explore, blockchain may be able to show its promise.

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Benefits of Blockchain Analytics

  • Numerous marketing apps rely on analytics. For example, client segmentation used to be determined only by demographic and sociographic characteristics, but market realities today allow for the creation of additional categories.
  • Predictive analytics-based recommendation systems for the aforementioned areas may help businesses enhance engagement, revenue, and the customer’s view of the brand. Not only would such a strategy enhance sales, but it may also dynamically modify prices to maintain your offer competitive and improve profit margins.
  • While blockchain technology may be used to verify the legitimacy of documents, it can also be used to verify their integrity. In businesses where papers should be immutable, such as the legal and healthcare sectors, blockchain technology may help make documents and modifications to them visible and immutable, while also increasing the authority of the data owner to govern and manage them.
  • As for example, DocStamp has developed a revolutionary application for blockchain-based document management. Anyone may self-notarize any document with DocStamp. While the document owner retains custody of the document, a hash of the document is stored on the Ethereum blockchain.
blockchain register
  • One of the most prevalent uses of blockchain technology is in supply chain management. Blockchain technology allows for simple supply chain tracking both forward and backward. The ability to trace an object makes locating it simple. Provenance tracking allows for root cause investigation. Because the blockchain records all transactions, unlike conventional data systems that might delete data, several forms of analysis are simpler.
  • The Lenovo blockchain study explained how smart contracts replaced printed documents in Lenovo’s supply chain. The transition to blockchain-based process management reduced human error and eliminated numerous human-related delays. Using electronic transactions instead of human contact increases auditability and transparency for all parties. Lenovo’s supply chain become more efficient and transparent.
  • Transactions on the blockchain occur in real time, across national and international boundaries. Not only are banks and financial technology developers seeking blockchain for the speed it provides for transactions, but data scientists and analysts are also monitoring blockchain data changes and additions in real time, significantly improving the possibility for rapid decision-making.
  • Blockchain technology is frequently described as a disruptive technology, and that description is accurate. Blockchain changes many things. Blockchain affects data analytics in many ways.
  • If an analytics model uses a public or consortium blockchain, the sponsoring organization almost certainly does not control all the data. Not all data in a non-private blockchain is created by the entity who created it.
  • Blockchain can let project teams store data on a distributed network and make it readily accessible. Access to data simplifies the analytics process. There is still plenty to accomplish, but you can be certain that blockchain data is available and hasn’t changed since it was recorded. Blockchain facilitates cooperation among data analysts and other data users.

Conclusion
There are significant breakthroughs being made on a daily basis in new blockchain technologies, heralding the arrival of significant new innovations in the area of big data analytics. More and more people are relying on blockchain-driven services, which means that analytics must adapt in order to provide high-quality insights from the data that is accessible.

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