Machine Learning

A Competitive Necessity for Enterprises

Algorithms Are The Language of Innovation

While many will debate the definition of Machine Learning (ML), very few will contest its increasing importance to those enterprises striving for a competitive edge. In its broadest form, Machine Learning is a technical discipline which aims to derive high value insights from low value and often raw data - with little to no human supervision. The defining factor being the level of human interaction that is required for learning to actually occur.

It’s important at this stage to make a distinction between Artificial Intelligence (AI) and Machine Learning which are often used interchangeably. AI is the pursuit and as yet, unrealised objective of recreating characteristics of human intelligence in machines. Machine Learning in contrast, attempts to create predictive models in the performance of specific tasks.

Despite its recent popularity Machine Learning is not actually a new science. Researchers interested in AI have for some time used pattern recognition in data, to determine if computers could learn to perform tasks - without being programmed specifically to do so. The surge in recent interest has a lot however to do with what many refer to as the Big Data Movement. The volume and complexity of enterprise data has now grown to a point where conventional data mining or analysis, performed by humans, simply isn’t effective enough. Machine Learning has effectively superseded terms like ‘data mining’, ‘business intelligence’ and ‘analytics’ as a part of the wider Big Data revolution.

The Big Data challenge has in many respects become insurmountable for humans alone. Traditional data mining comprised a team of analysts, who would pour over variables manually looking for patterns and relationships - that could potentially derive insights. Not only was this approach time consuming but it was often like trying to find a needle in a hay stack. Machine Learning in contrast, has the potential to identify data patterns in a fraction of the time it would take even the most skilled human analyst. In addition to increased velocity, Machine Learning also has the potential to identify patterns which would ultimately elude human analysis entirely.

In an enterprise context, Machine Learning is most likely to take the form of software or an application. The application or agent as it is often referred to, takes data as a low value input and in turn outputs a high value product which could comprise anything from patterns, equations and rulesets to program code or even fully formed insights. The output itself can range in composition from machine readable data, to that intelligible by humans or a combination of the two.

There are 3 widely recognised Machine Learning methods each defined by the level of requisite supervision; supervised learning, unsupervised learning and reinforcement learning. The fundamental difference between supervised and unsupervised learning is the amount of data labelling used. The former analyses labelled data (often in IO pairs) whereas the latter does not. Reinforcement in contrast, enables machines to learn using reward feedback provided by the environment.

The Big Data challenge has in many respect become insurmountable for humans alone. Traditional data mining techniques are not enough to derive real insights.

Performance Enhancing

ML is a Vehicle of Improvement

Predictive Analytics Is Now Incredibly Accessible

Machine Learning represents a significant opportunity for enterprises to solve a myriad of complex business problems and derive insights from data - which can then form the basis of better decision making. Conceptually, the more machines that can be deployed towards a Machine Learning problem, the better. Though the mechanics of Machine Learning vary quite considerably, making it difficult to define concisely and leading many to talk in terms of frameworks - when they describe how Machine Learning actually works. In excess of 20 recognised Machine Learning frameworks exist, with more likely to be added as the field evolves and its usage becomes more widespread. Each framework defines how the algorithms within them learn and a framework can itself be comprised of hundreds of differing algorithms. The algorithms themselves continue to be benchmarked by a host of different researches, making it easier to discern the appropriateness of any particular framework.

The availability of Machine Learning toolsets, in addition to cheap cloud hosting comprised of powerful GPU based hardware has made Machine Learning use more widespread. The tools, many of which have been developed by leading technology vendors, are designed to abstract away the most complicated components - making Machine Learning techniques available to a broader class of developers. In much the same way as a framework, these tools consist of a library of algorithms that can be applied to data.

Achieving a Competitive Edge
In an enterprise sense, Machine Learning derives its true value from a models ability to outperform competitors at specific tasks. The nature of the task will obviously depend on the nature and needs of a particular business. Performance however regardless of the task at hand is measurable. The objective of every business is to achieve performance which is demonstrably superior to that of the competition. Paradoxically, competing companies tend to perform the same tasks using the same ‘industry standard’ approaches. This limits the ability of enterprises to achieve competitive advantages in their marketplaces. Machine Learning offers an opportunity to develop ways of performing tasks in significantly better ways than your competitors. Whether this is measured in cost, revenue, quality or speed, it provides a powerful means of differentiation - by expanding the capability and performance gap between yourself and the competition.

Proprietary Data: An Unfair Advantage
Machine Learning is of course entirely dependent on data. Enterprises are often dependent on creating algorithms based on the same data that their competitors are using for their models. While competitive advantages can still be achieved using differing approaches, enterprises are often forced resort to tuning and tweaking - which limits their ability to better the competitions models. Proprietary or unique data in contrast gives an enterprise a very unfair advantage. Possessing data that only your company can use for Machine Learning purposes represents an obvious value proposition – the impact of which becomes dependent on the algos developed.

Enterprises who build intuitive ML strategies, which focus on addressing the most pressing business challenges, will ultimately leave those competitors who don't in their wake. Such divergence will only be compounded by those that build elaborate proprietary data sets - compared to those that simply share the same data and approaches.

Possessing data that only your company can use for Machine Learning purposes represents an obvious value proposition – the perfect unfair advantage.