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Does Artificial Intelligence lead to the data revolution?

By Neil Dennis - posted Thursday, 12 October 2017


Equally, the ICO is viewing the increase in ML usage for 'business as usual' as a step change in regards to data protection. In a report on big data, ML and data protection, it discusses the potential difficulties around applying conventional data regulations to big data analytics and the need for new procedures when it comes to protecting personal data. This indicates that regulatory bodies expect something of a transformation in the way data is used by companies on a daily basis, and points to an imminent, large scale change.

Indeed, it seems that a revolution has already begun in some sectors. We mentioned above how JP Morgan dramatically reduced the time in which commercial loan agreements were interpreted by using a programme called Contract Intelligence, or COIN, but there are a number of other examples of industries using ML to improve processes.

In the manufacturing industry, firms are collecting larger amounts of data on their machinery and production processes, and using predictive technologies and analytics organise the service schedule. By being able to predict when equipment might start to fail and why, manufacturers can ensure it is serviced and potential issues rectified before losses are incurred. Of course, this results in greater efficiency and bigger profits.

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Meanwhile, ML is helping biological research by finding patterns in the huge amounts of data produced by experiments, and facilitating the formulation of scientific hypotheses. One of the advantages of the technology is that it is free from a bias towards the familiar, which opens up the possibility to find answers that humans may not look for. It is with the help of ML systems that some biological scientists are looking for new therapies to treat cancer. Data on normal and abnormal protein codes that would take humans months to go through can be analysed in far less time by an algorithm, thereby speeding up the process of finding correlations.

These examples suggest an already burgeoning use of ML in relation to large data sets. With the abilities of systems and technologies evolving all the time, the data that can be stored and used by businesses has the capacity to increase, with more complex findings and predictions coming from varying sources.

Conclusion

Although Machine Learning has its risks – not least those rooted in personal data protection and the accumulation of technical debt – the systems and technology that it uses are evolving all the time. As more businesses invest in it, ML systems will become more effective and 'smarter', enabling organisations to make progress in ways that haven't previously been possible.

Current examples of ML usage show that it is already beginning to revolutionise the way businesses handle and analyse data, and the kind of actions they can take as a result. For European companies, the possibilities may be tempered – or at least, regulated – by the introduction of GDPR in 2018, but it wouldn't be unreasonable to suggest that a large scale change in data management will occur across a number of industries as ML is adopted.

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About the Author

Neil Dennis is a financial author and member of Capital.com editorial team. Capital.com is a global trading platform that allows users to trade a variety of financial instruments. Along with quick access to the markets, Capital.com offers financial courses, a huge collection of industry terms, personalised content and more. For over 16 years, Neil was previously employed by the Financial Times for over 16 years. He left his career to become a freelance writer. His fortes are currencies and the Fintech industry.

Creative Commons LicenseThis work is licensed under a Creative Commons License.

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