In this article, I provide a simple, four-step process to improve business profitability using machine learning.
There is nothing magical about machine learning. Generally, it involves automatically fitting a model to data, with the goal of making useful predictions or decisions. You have been doing this your whole life, ever since at school you learned to fit a straight line on a graph with some data points. What is different these days is the sheer scale of the data, both in terms of the number of data points and its dimensionality, and the variety of forms it can take – numerical, text, audio, images, video, etc.
When people talk about machine learning or deep learning, they are talking about the ability of computers to learn objective functions and make decisions based on data. This is hugely important and already impacting practically every industry. Whilst machine learning still has a long way to go before it reaches full potential, we can already achieve an enormous amount with what we have today. This is where forward-thinking business leaders should be focusing.
Over the next five to ten years, the biggest business gains will likely stem from getting the right information to the right people at the right time. Building upon the business intelligence revolution of the past years, machine learning will boost existing pattern-finding abilities and automate value extraction in many areas. So how can your business incorporate it into daily decision-making and long-term planning?
First – catalogue your business processes. Look for procedures and decisions that are made routinely and consistently, like routing a customer support query to an agent. Make sure you collect as much data as possible about how the decision was made, along with the data used to make it – this is the kind of information that will be used to train a machine learning algorithm.
Second – to begin with, focus on well defined problems. Automation and machine learning work well where the problem is well defined and well understood, and where the available data fully characterises the information necessary to make a decision.
Third – drawing on Occam’s razor, do not use machine learning where standard business logic will suffice. Machine learning really comes into its own when the underlying business rules, although well defined, follow complex or non-linear patterns.
Fourth – if a process is very complicated, use machine learning to create decision support systems. If the objective is too unclear to define, try to create intermediate way-points that will help your teams become more effective in stages. By thinking of machine learning as part of the hierarchical decision-making process, it will drive a better understanding of the problem in the future.
The point is, there is so much that can be done without digging too deep. For now, the majority of your workforce will continue to have a job, and so you can help them to be more productive, working on more interesting and demanding tasks, by automating away the repetitive parts of your business.
It can sometimes be difficult to rethink your business processes, especially if they are “the way we’ve always done things”, but it does not hurt to try. Be patient, as the transformation will not happen overnight. If you get stuck, it may be worth talking to a third-party for a fresh perspective. However, once you have dipped your foot in the water and are reaping the benefits of your first successful project, you will be equipped tackle far more complex problems with machine learning.
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