The amount of data in the digital space is increasing, and big data technologies and practices are evolving at an exponential rate. Organisations are striving to evolve from descriptive to predictive analytics, which has given rise to greater use of machine learning (ML).
This phenomenon has given numerous organisations and institutions the opportunity to unlock data potential; machine learning has applications in Finance, Retail, Marketing, Healthcare and many more areas that apply artificial intelligence.
Machine learning gives "computers the ability to learn without being explicitly programmed." (Arthur Samuel, 1957). Machine learning is one way of achieving AI that automates analytical model building. It allows software applications to become more accurate at predicting results without the explicit, rule-based programming. This is because the system can automatically learn and improve from experience, as well as find hidden insights in the data.
This method became a scientific discipline in the 1990s when there were significant advances in digitisation. As a result of machine learning, data scientists no longer have to build finished models of systems and software—instead, they train machines to do the task.
With the complexity and volume of big data, the potential and need for machine learning services has risen and continues to rise.
The applications of machine learning for business are numerous. Banks and financial institutions, for instance, have built models that accurately forecast who will cancel service or default on their loans; it has also helped them plan how to best intervene with customer challenges.
Website “Contact Us” forms are getting shorter in recent years as well, as users no longer need to self-select and fill out limitless fields. Machine learning can already auto-fill the fields, as well as learn to look at the meaning of a request and route that request to the right place.
The technology has been useful in credit scoring and next-best offers, too; as well as in email spam filtering, pattern and image recognition, new pricing models, text-based sentiment analysis and prediction of equipment failure, to name a few areas.
Organisations can only best utilise machine learning, however, if they see it as a tool to craft and implement a strategic vision. At Brainpool, we offer a network of machine learning experts and work with you on getting started from idea to implementation, which helps prevent the technology from being lost amidst the organisation’s routine operations. Join our community and find the right people to build your vision by filling out our contact form.