We are witnessing a paradigm shift regarding data.
The blockchain Ecosystem is expanding and is also one of the most secured networks conceived technology mechanism of trust. This series aims to discover ‘The potential of Blockchain’. This will range from the potential solutions, challenges and latest trends.
Blockchain can solve a lot of issues continually faced by banks and financial organizations nowadays. It could bring changes to everything from payments to online appointment scheduling software. It provides a high level of security in storing and transforming data at low costs through an open and transparent network infrastructure. The traditionally centralized, conservative, and restricted banking sector has now started using blockchain technology more than any other institutions. The change of the bank's clearinghouse or centralized ledger to blockchain's distributed ledger could redefine the banking industry.
For blockchain technology to achieve wide-scale adoption, a decentralised ecosystem has to be developed. To achieve a decentralised society, many more components need to be built, requiring global standards and large investments. Apart from the different industry layers that need to be developed, we will also continue to see new distributed ledger technologies and more exciting decentralised applications. With the blockchain ecosystem evolving, new applications and technologies come into play as well. Therefore, here are five blockchain trends that you should consider in the coming year.
Predictive analytics, as an evolving domain based on Big Data and AI, requires a considerable amount of information for training the model. Sometimes, locally, this is scarce or unscalable, but imagine putting together data from thousands of similar users. Suddenly, the lack of data is no longer a problem and patterns emerge more easily. This gives a real chance to smaller companies, like start-ups, to take advantage of the blockchain model for their operations and use the data generated in the process as a by-product to feed various prediction models.
Introduction Access to and control of data is one of the biggest challenges faced by data analysts and data scientists. Creative, persistent analysts find ways to get access to at least some of this data but doing that efficiently in a way that is also standardized and centralized for everyone on the team is difficult.
Introduction: Prediction is a tricky business. You have to step outside of your comfort zone, your fainted vision of the world and see it thorough across all possible dimensions. In this series, we will discuss the future of “AI”, applications that are yet unexplored.
Introduction: Humans are wired to make tough decisions bringing all the context and principles to bear. Similarly, can devices apply the available information to make the right judgment calls? In this series, we shall discuss some ethical dilemmas faced by emerging technologies.
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