Today’s ‘big data’ economy commands speed and value, so the need for analytics is critical. Advanced analytics, comprising of varying techniques, including machine learning and artificial intelligence (AI), can extract insight from data on a level that far surpasses human ability.
Many businesses are already using data and analytics to get to a certain outcome and have been in various formats for some time. That said, we’re still yet to fully understand the methodology and implications of using machine learning and AI at scale.
When you combine analytics and machine learning you get to the complex capabilities today’s businesses require in order to solve scalable problems. It has the power to underpin every KPI, enrich any task or solve any problem.
There are many ways this can happen. Every few years there is a new buzzword or trend, usually to do with technology. It was the internet, then mobile. Today, many people are talking about AI. You only have to look at the Government’s Industrial Strategy – which advocates the use of AI to support everything from schooling to decisions – to see how invested we are in it as a country. And some people are even likening it to the coming of electricity.
Before electricity, at the beginning of the industrial revolution, all power was centralised around the factories. Outside the factories, there was no power because it couldn’t be transported. Electricity brought the ability to decentralise the power. You could get that power to different factories, different towns, even the home eventually. It changed society as we know it.
Unlike many other technologies, machine learning also has that capability. It can both automate and optimise most business processes using localised data – something deemed impossible until now. But we also need to question the amount of hype surrounding it and understand it for what it is – an innovation that, if used right, can transform many things.
Even today, most algorithms tend to be quite generic, meaning you’ll take a lot of data through linear models, take some sort of average and the machine will work. On the current models, it will make some average decisions. With machine learning, you don’t need to do that. You can specifically look at the set of data and make a decision that is more accurate. There are very few processes in your organisation that you cannot change with machine learning.
Adapt and learn
Machine learning can look at localised data in a specific point in time and point of data. Why is that different from what we were doing before? In common with AI products, a product using machine learning creates what we call AI virtuous circles. This is basically a locking loop, or a self-feeding mechanism.
Many of the world’s biggest technology companies have invested huge amounts of money on this technology. If you think of a traditional IT system, you install it and then live with it. You might make enhancements – such as automaton or augmenting it as your needs arise. But, the version you install, at large, remains there, unchanged, for years.
Machine learning isn’t like that. You deploy the model, and the model will adapt and learn. The more data you get, the better the product is. More users produce more data, and, if used intelligently, data analytics can start producing signals of information. Perhaps even signals that you weren’t looking for originally.
The value of machine learning
So, why use machine learning? The argument for this to put forward is improving customer experience. With machine learning and advanced analytics, you can make fine-tuned decisions on the spot. You can respond quickly. More importantly, you can switch the question around.
Rather than pushing each product individually, you can say: ‘Let’s identify the customer, understand the customer and then make the decision.’ When machine learning allows you to do that, you can start having proper, relevant and more informed conversations.
The second purpose is for fraud prevention. With machine learning, you can start analysing transactions through the ecommerce site. Fraud is quite a specific use case, because it is very unbalanced. You will get peaks in the models. But unlike a traditional, rules-based system, the machine – eventually – will pick out the fraud. It will adapt and become more and more sophisticated. It will also change, through self-learning, to stop the fraud.
And finally, let’s return to the concept of the virtuous circle. Once you start getting all these transactions and doing analytics at scale, you’ll see things you weren’t looking for. You could, for example, do clustering on a segment, so you can understand the type of customer; how they shop; how they behave; who prefers to shop in the morning or afternoon. And much more.
What’s important about machine learning is it benefits both businesses and society.
Today, with the advent of technology we are in a position where we can better understand a person through data. But importantly, we wouldn’t be able to make best use of this data if it wasn’t for advances in analytics.
Machine learning and other advanced analytics techniques have the power to crunch huge volumes of data, from disparate sources quicker, and more effectively than ever before. More effectively than a person can do. However, it’s only those who are quick to embrace these new tools who will set themselves apart from the rest. Getting this right will only maximise their chances of success.
Javier Campos, Head of DataLabs, Experian UK&I and EMEA