Data Analytics and Machine Learning
How Can Data Analytics and Machine Learning Help Blockchain Solutions?
This raises the issue of how more value might be provided to these blockchain apps to meet the expectations of corporate users. The number of transactions is rapidly increasing as usage grows, resulting in a brand-new data lake of information. Taking advantage of this pool of timestamped data has enormous potential for technologies such as DA and ML. The prospects of the convergence of these technologies were investigated as part of an internship at Oracle.
Blockchain augmented with data analytics and machine learning
Blockchain has evolved into a technology enabler, serving as the backbone of an increasing number of business activities. As a result, while each domain may have a somewhat different focus, enterprise blockchain apps employ DA and ML to create additional commercial value. DA may enhance blockchain by visualising timestamped data as real-time timelines, stock heat maps, product information charts, supply chain connections, and other charts and statistics. These visualisations make the information on the blockchain ledger easier to interpret and more comprehensive.
ML, on the other hand, can be used later, once all the data has been collected for both training and prediction of ML models. It can aid in the discovery of interesting patterns, the prediction of future events, the detection of abnormalities, and the clustering of data. In the future, ML may even aid in the improvement of smart contract code production and the application of best practices. Although these technologies have the potential to complement one another, they are still in their early phases. Oracle has already added a special functionality to its blockchain offering to automate DA: ledger information and blockchain operations records are transmitted to Oracle Blockchain Platform’s Rich History Database.
Promising industries and use cases
Blockchain applications can be augmented with Machine Learning, Artificial Intelligence and DA in several industries. We will list some of them here.
A Blockchain network can aid in the secure and patient-authorized flow of data between care providers (for example, a medical laboratory and an external specialist who needs access to the patient’s test results). Patients can give permission for access to their medical files, whereas clinicians can securely share medical results via a Blockchain.
The primary benefits of blockchain in this context are management, full visibility, identity, and security. DA is used to visualise the data and illustrate the patient and doctor groupings per hospital/clinic. Statistics, anomalies, and reference lines improve data visualisation. ML could anticipate, for example, hospital/clinic occupancy rates that would have been an intriguing prospect during the COVID-19 pandemic.
Blockchain has tapped into the financial sector as well. It enables the secure handling and reconciliation of bills between different companies and business units. Blockchain provides a comprehensive view of these invoices as well as full traceability. ML can evaluate timestamped data in real-time to spot abnormalities and determine why an invoice was rejected or took a considerably longer route to payment. DA may be used to visualise data in real-time using heat maps and Sankey diagrams to depict flows.
When using Oracle Analytics, natural language interpretations of the data can be generated using AI/ML. This can speed up the process, reveal why particular bills are rejected more frequently than others, and remove inefficiencies.
Blockchain can aid in the development of a reliable supply chain with provenance. This ensures product authenticity for both businesses and consumers. Blockchain accelerates the process, enables real-time tracking based on irreversible data, and provides a comprehensive perspective of a multi-level distribution chain. DA can aid in the visualisation of timestamped data in an unchangeable supply chain timeline, which can even be viewed on the final product via a QR code that customers may scan.
Customers may view product composition and end-to-end sourcing across various providers, verify sustainability claims, dive down into recycled content sources, and much more. This is not only beneficial for sustainability management because customers and businesses are assured that a product is actually sustainable.
It also helps retailers increase their business because case studies show that when customers have access to such data, they are more likely to purchase from these retailers. ML can also aid in the optimization of supply chain routes and the prediction of possible bottlenecks or disruptions.
We feel that adding ML and DA to Blockchain applications adds more value, especially as we move toward Blockchain 4.0. Due to the disruptive potential of blockchain in terms of company processes, it is advised that enterprises first understand the Blockchain idea before enhancing blockchain applications with these technologies.
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