How does machine learning affect BI and analytics?

In BI, Blog by Jonathan Cowling

It used to be only pop culture and the movies that fuelled irrational fears of Artificial Intelligence (AI) and machine learning, but recently those dystopian predictions have been shared by news reports with headlines suggesting the late Stephen Hawking thought AI will replace humans. The topic has become an easy target for stoking fear, but the reality of AI is increased efficiency, convenience for consumers, and safer business. When it comes to Business Intelligence (BI) and analytics, AI offers significantly increased insight into data, allowing for more informed business decisions.

Machine learning is a subset of AI where algorithms are used to allow machines to autonomously learn from data and information. What’s interesting is that many people think of this as a new development, when in fact the first learning machine was developed in 1952 by Arthur Samuel, who invented a program that could improve at checkers the more it played. The progress of machine learning has produced some incredible technology since then, with recent developments meaning more industries can make use of it than ever before. For a business analyst, machine learning is perhaps the best assistant they could hope for.

If an analyst needs a quick look at the impact of a price change on a product, they can use software that utilises machine learning to find this out in minutes or even seconds. Before machine learning made this process almost instantaneous, analysts would need to run a process that could take hours. Now they don’t need to waste time working on mathematical equations and can instead devote more time towards the actual analysis of the information.

Machine learning has already made a huge difference to business analyst workflows, and it’s still making progress. The future of machine learning is likely to overhaul how BI and analytics work entirely. Software like Oracle and Azure has already transformed tasks that used to take hours into automated tasks, and with improvements being made all the time, it’s reasonable to assume that business intelligence will go through some massive transformations, including the depth to which it can analyse data.

The trick to mastering machine learning in analytics is to know when it is and isn’t best to rely on it. AI is excellent at crunching numbers and establishing patterns, but certain areas are still better off covered by humans. These include areas that require linguistic interpretation, such as customer satisfaction surveys, where qualitative words need to be analysed. It’s also important to recognise that machine learning and AI only deliver outputs, and it’s up to the human to decipher meaning from those outputs.

It’s true that machine learning is unmatched in pattern recognition, but there is little to suggest that AI is in a position to replace human analysts, much less the human race! Machine learning can only operate in the part of the world that humans can specifically prescribe to it, and it will always be up to the analysts to extract value. But the rate at which AI and machine learning are developing and the significant advantage they provide to enterprises mean that business intelligence is likely to evolve from a popular mechanism for increased efficiency into a crucial industry tool that’s required to stay competitive. Business analysts who are yet to look into machine learning in business intelligence can still get ahead of the curve. Feel free to get in touch if you’d like to know more; we’d be more than happy to help.