Like what you've read?

On Line Opinion is the only Australian site where you get all sides of the story. We don't
charge, but we need your support. Here�s how you can help.

  • Advertise

    We have a monthly audience of 70,000 and advertising packages from $200 a month.

  • Volunteer

    We always need commissioning editors and sub-editors.

  • Contribute

    Got something to say? Submit an essay.

 The National Forum   Donate   Your Account   On Line Opinion   Forum   Blogs   Polling   About   
On Line Opinion logo ON LINE OPINION - Australia's e-journal of social and political debate


On Line Opinion is a not-for-profit publication and relies on the generosity of its sponsors, editors and contributors. If you would like to help, contact us.


RSS 2.0

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.


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.


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.

  1. Pages:
  2. 1
  3. 2
  4. Page 3
  5. All

This article is a sponsored post.

Discuss in our Forums

See what other readers are saying about this article!

Click here to read & post comments.

2 posts so far.

Share this:
reddit this reddit thisbookmark with Del.icio.usdigg thisseed newsvineSeed NewsvineStumbleUpon StumbleUponsubmit to propellerkwoff it

About the Author

Neil Dennis is a financial author and member of editorial team. is a global trading platform that allows users to trade a variety of financial instruments. Along with quick access to the markets, 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.

Article Tools
Comment 2 comments
Print Printable version
Subscribe Subscribe
Email Email a friend

About Us Search Discuss Feedback Legals Privacy