One of the keys to success in any area, whether it is on the sports field or the business arena is knowledge, and knowledge has always been acquired from insights based on empirical information. In other words: contextualised data. Hence, so much of what we do is about enabling our clients to acquire a deeper understanding of their business and customers from data, and the ability to act upon this understanding.
In most situations there is a particular goal or output we wish to achieve. However, knowing which controlling factors, or variables, to change and how to change them can be difficult in even a simple system or process comprised of a limited number of variables. Understanding the dependencies between variables and outputs becomes increasingly important in today's world of 'Big Data'. Data is being generated on such a vast scale, at such a rate and in such an unstructured way that the full potential of it can no longer be realised, or even accessed, through the traditional techniques predominantly used until now. Hence, we are turning to a greater extent to the power of computing, specifically data mining (a subset of machine learning), as well as more advanced machine learning techniques, to extract insights from this sea of data.
Machine learning has come from a combination of computer science, statistics and mathematics. Broadly, it is the use of self-improving algorithms that will refine themselves through training and repeated use. In some cases the algorithms learn, much the same as we do, by trying different things and seeing which works the best.
Data mining is a term that covers several areas of machine learning. It is a way to discover relationships within a data set. The two main areas it covers are classification and cluster analysis. Classification looks for the key relationships within a data set and how the relationships affect the given problem being analysed. Cluster analysis is where the algorithm breaks down the data into smaller sets of data grouped by the relationships, or common attributes, they share. In creating these groupings the actual relationships are discovered. In both cases it is the discovery of the relationships within the data that will aid decision making, whether this is business or otherwise.
These basic techniques have become quite commonplace in businesses. Yet, it is the use of more advanced machine learning techniques which will fully utilise those of data mining to enhance the decision making processes. These more advanced techniques not only identify the relationships within the data but can start to carry out automated processes based on the relationships observed. Additionally, some of these advanced techniques are able to test different scenarios and solutions to a problem in an effort to optimise the solution.
The benefit of using machine learning to extract insight from big data is not restricted to a few industries or problem types. It can be applied across all areas from finance through to healthcare, whether you are optimising a supply chain or solving large social challenges. This is because underlying almost every problem is the relationship between the various factors, and where there are relationships between factors, machine learning can be applied to solve these relationships.
Machine learning can be applied to a variety of problems, whether this is using simulations to test the market response to new regulations; segmenting populations in order to carry out automated, tailored interactions; or analysing thousands of doctors' reports to solve complex medical conditions through the discovery of previously unknown relationships. As you can see there is not just one type of problem machine learning is applicable to.
Yet, despite this, machine learning is still in its infancy and many organisations have yet to realise, let alone act upon, the vast capability it holds in increasing business effectiveness.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
