Machine Learning and Artificial Intelligence (AI) often evoke thoughts of the cutting edge of technology. Self-driving cars, AlphaGo and talking digital assistants such as Amazon’s Alexa and Microsoft’s Cortana. These are perhaps the most recognisable and exciting aspects of Machine Learning, certainly for those who don’t work within Business Intelligence (BI). The real power of Machine Learning, though, lies in its application as part of data analytics or BI. It may not be as glamorous or as attention-grabbing as the previous use cases but the value Machine Learning can add in this avenue is more than significant. For those who work within BI, or use it in their business, the value of Machine Learning needs to be recognised, understood and exploited.
At its core Machine Learning is training a computer, using sample data, how to analyse and extract useful information from that data. It is an important part of AI and recent developments mean it is becoming more and more useful. The ability to apply complex mathematical calculations to gigantic data sets continually and in near real-time is transformative. More importantly, the learning part of the technology means that BI systems can make more decisions for themselves, extracting more actionable insights for those in the business that need them. Decision makers can now spend less time searching and querying datasets, and more time taking action.
The nature of Machine Learning dictates that it is continually learning and improving. Each analysis provides more insights and trends that themselves feedback into the learning of the system. Though in the not too distant past this would have been the preserve of those with the budget for some serious hardware this is no longer the case. Machine Learning is accessible and flexible and is being more commonly deployed by BI vendors.
Because Machine Learning allows BI to essentially think for itself it grants any BI system the ability to uncover far more and far deeper insights than otherwise. Effectively Machine Learning automates the process of ‘deep diving’ and querying data to provide insights that otherwise would have been missed. This has two effects. Primarily that the BI system delivers more value in terms of insights for the business. Secondly, it also frees up the time of the employees who would have been querying the data. They can now act quicker with better information delivered to them.
Machine Learning capabilities should be particularly exciting to those businesses who deal with ‘big data’ or unwieldy datasets. Previously, analysis of large datasets could take hours, even days before insights are delivered. Machine Learning turns that on its head. Because it can apply such complex analysis far quicker than humans ever could, real-time analysis is possible. Almost every business would benefit from having decision makers given insights in real-time, being able to spot anomalies and identify issues as they arise – rather than dealing with the aftermath.
Machine Learning is happening now. Both in the flashy cutting-edge developments and in the nitty-gritty of BI. The power and benefits of the technology are undeniable. Those who work in the tech industry are all too aware of how much AI will transform the way we work and our wider world. Machine Learning is one string of that bow, and its capabilities for handling and analysing big data are hard to overstate. BI systems with Machine Learning have already become common-place and, simply, it is hard to imagine how future BI systems that don’t include this technology will compete.