What are the applications of machine learning?
By Katrina Wakefield, Marketing, SAS UK
Managing big data
It is no longer practical or feasible for humans to analyse and monitor the sheer amount and diversity of data available today. Data-orientated organisations routinely deal with massive volumes of structured, semi-structured and unstructured data. Even traditional analytics and software struggle to keep up because they simply do not possess the capability or capacity for extensive data analysis.
But, as big data has the potential to help organisations streamline operations based on a variety of data sets, more and more organisations are applying an intelligent and scalable solution to automate their data processing and analysis: machine learning.
What is machine learning?
Machine learning is a subset of computer science and a branch of artificial intelligence. It focuses primarily on the study and construction of algorithms that can learn and make predictions based on data, as well as overcome program limitations and make data-driven decisions.
The term ‘machine learning’ was initially coined by Arthur Samuel in 1959 and is defined as a “computer’s ability to learn without being explicitly programmed”. Machine learning has grown in popularity since then – and we have arrived at the point where it is profitable and possible for businesses to utilise machine learning to improve efficiency.
Machine learning was born from pattern recognition and the notion that computers can learn without being programmed to perform specific tasks. In helping machines to learn without being programmed to do so, the machine can adapt and improve over time.
By using algorithms, machines can learn – and different algorithms learn in different ways. As machine learning algorithms are exposed to new data sets they adapt over time and increase their ‘intelligence’.
What are the applications of machine learning?
Machine learning enables organisations to analyse complex data automatically at scale and with tremendous accuracy. It gives organisations the insight they need to make data-driven decisions about their operations. However, machine learning algorithms need to be taught and trained[AC1] to deliver this insight.
For example, when exposed to a large data set, the machine can detect patterns and use historical and real-time data to determine the best course of action or procedure that will deliver the best result in the shortest possible time.
Who uses machine learning – and why?
Machine learning is particularly useful for large and wide data sets with similar or closely related values. Manual analysis is highly impractical and inefficient, so machine learning algorithms that have been trained to identify specific information can achieve this in considerably less time.
Who uses machine learning?
The applications for machine learning are vast, and almost limitless from a business perspective, but here are just a few examples of how industries that are typically most concerned with data can use machine learning algorithms to deliver greater insight:
- Financial services – As the financial industry has grown in scale and complexity (due to more stringent regulations to prevent financial
collapse), financial firms have had to analyse increasingly large and varied data sets to meet the latest compliance regulations.
As regulations demand a more comprehensive and intricate approach to data analysis, financial firms have had to look to intelligent, automated solutions to drive productivity. They use machine learning to detect fraudulent activity, specifically related to money laundering, as well as identify investment and trading opportunities and calculate market risks.
- Marketing and Sales – The path to purchase is no longer linear because customers can engage with businesses through a variety of methods, from organic search and social media through to email marketing. Combined with the ‘attribution data’ digital marketing provides, this creates a plethora of customer data that needs to be analysed and acted upon to drive engagement and sales. While actionable and valuable insights can be gleaned from data at scale, without the solutions to parse that data it becomes difficult to develop new strategies. Many businesses have turned to machine learning, using algorithms to quickly interpret diverse data sets and build correlations. As a result, marketing and sales departments are able to analyse the path to purchase and understand how they can optimise the buyer journey.
- Data security – Cyber security has quickly moved to the top of the business agenda in recent years, with ransomware attacks such as WannaCry and Petya driving a renewed emphasis on digital security. The thing is, most malware tends to be based on previous architecture, with only slight technical changes and variations in code. However, as these changes are inconspicuous, it can be difficult for IT specialists to identify them right away – and time is of the utmost importance when responding to a security threat. But, with machine learning algorithms, IT specialists can teach the algorithm to analyse malware and look for patterns and variations in the code, enabling it to identify – and potentially stop – malware attacks with great accuracy. As the algorithms are fed more data, their ability to protect the business’ digital infrastructure improves. Ideally, a combination of both IT experts and machine learning algorithms would be in the business’ best interest to develop strong, firm-wide security.
Are you ready for machine learning?
Machine learning works best in environments where the problems are clear and defined, but where decisions need to be reached more quickly. High volume, routine activities that require an immediate or real-time response are good candidates for machine learning applications. So, while the prospect of machine learning might sound exciting – is it truly applicable to your business?
Moreover, do you have a strategy in mind for the continuous development of your machine learning algorithms? After all, in order for these algorithms to truly benefit your business, they need to be refined continuously based on data and adjusted according to objectives. If you do not have large quantities of data at your disposal, the automation of its analysis and processing may not benefit you.
Industries working with large amounts and varieties of data, that have incorporated machine learning into their procedures, are able to free up their workforce and benefit from real-time, intelligent data analysis. Machine learning, unlike traditional analytics, is scalable, automated and continuously learning. The more data a machine learning algorithm is provided with, the better its decision making becomes.
Organisations will face challenges in adopting machine learning and integrating it into business processes. Data availability and data governance, first and foremost, need to be addressed. If the organisation’s data is not in one central location – or if it is poor quality, it will be difficult to develop a ‘complete’ and accurate analysis. As machine learning algorithms learn from the data you provide, it is in the organisation’s best interest to ensure that data is of the highest quality – and these are all elements SAS can help with.