By Joey Withers, DBA & Senior Database Developer at Rockpool Digital

It almost seems like the beginning of a sci-fi movie when people talk about machine learning. It refers to systems that learn and adapt to data, rather than follow pre-programmed instructions. While we won't be facing Skynet's Terminators anytime soon, companies are now starting to understand the advantages of intelligent systems.

Data has become one of the most coveted assets in the world of business. It can be used to provide a better product or service by helping companies to understand what customers want. The systems that analyse this data are powered by complex algorithms. Historically, these algorithms were pretty set in stone and if a company wanted a different output from its analyses, the algorithm needed to be changed. Now, the systems can adapt its algorithm depending on the data that it receives.

Take Facebook for instance, algorithms used to automatically tag people in a photograph have a higher success rate as it 'learns' if it was correct based on interactions from its users. The more times a person uses the feature, the more opportunities there are for the algorithm to learn and the better it becomes.

Although machine learning is improving, due to the specialist skills required to utilise the technology, its wider usage is still limited to programmers. However, that could soon be changing.

Microsoft now has Machine Learning functionality on its Azure cloud computing platform. The platform allows users to build predictive models via a graphical drag and drop interface, without the need to write any code. With easier access to this technology, businesses can identify its most influential clients, ensure that inventory is in the right place at the right time and predict results, therefore allowing the appropriate action to be taken.

Azure ML is designed specifically to make machine learning accessible to business users and more generalised developers. Classically, in order to benefit from Machine Learning, you would require an in depth understanding of statistics and mathematics and be skilled in writing code in R, the functional programming language used among statisticians and data miners to utilise machine learning. Azure provides an interface that allows users to leverage benefit without the otherwise required prerequisite skills and expertise.

However, as with most development tools that provide an abstraction layer, there comes a cost. While a graphical interface is easy to use and will reduce complexity, generally systems suffer a loss of flexibility, particularly when comparing what could be achieved by a teams of specialist developers. Mainstream use of Machine Learning is relatively new and on balance, the cloud tool will provide significant value to businesses and the benefits of accessibility will outweigh any restrictions for most companies.

Another use of machine learning is in the field of cyber security. Companies like Dtex Systems and Brighterion Inc. use software to monitor employee or user behaviour and create a digital profile that learns how the individual thinks and acts. As the software becomes familiar with the ways in which a person or system functions, it can spot different behaviour. This presents a huge opportunity for businesses across all sectors to benefit from machine learning.

Machine Learning allows users to explore 'what-if' data analysis techniques so the platform provides the tools to satisfy curiosity of users who want to tell a story with their data and predict the future without the investment of making changes to business process upfront.

Machines can sort and analyse huge amounts of data to create understanding. I predict that in the future it will become ever more important for businesses to use machine learning to make better predictions and smarter data-driven business decisions.