When Business Analytics Meets Machine Learning

This article was last updated on April 16, 2022

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It’s not an overstatement to say machine learning and business analytics almost perfectly compliment each other. This is due to the fact machine learning is a technology that gets its fuel from data itself. Unlike algorithms or programs intentionally built with a certain, fixed set of rules, machine learning turns this process on its head. Instead of humans dictating how information is analyzed, we just feed a machine raw data and an intended outcome, which the computer can then synthesize on its own based on examples.  

There’s no denying the extraordinary nature of machine learning, as it completely challenges preconceptions about what can be done with data. When business analytics meets machine learning, the result is an incredibly powerful method of data analysis.  

Analyze Data in Real Time 

When thinking about the applications of machine learning in business analytics, the ability to analyze data in real time is certainly one of the most appealing aspects. Data is everywhere, all the time. There’s no stopping the constant creation of new data. Up until recently, when machine learning and other technologies supercharged the capabilities of advanced analytics, there was no way of really doing anything with this real-time data. 

Those days are now history. Machine learning gives organizations the ability to analyze data as it comes into existence. This opens up entire new field for analysis that would have been only theoretical just a few years ago. For instance, a manufacturer can use machine learning analytics to immediately spot anomalies on a production line.  

There’s an obvious, immediate benefit to this: Unintended issues can be addressed right away instead of having to wait until they become obvious through historical data. This can alleviate operational efficiencies in hours or even minutes instead of days, weeks, or months. Furthermore, this can have important implications on the bottom line of an enterprise. Fixing a critical operational issue right away as opposed to discovering it later can clearly save money upfront, but maybe longer term as well if you avoid regulatory issues.  

Uncover Insights More Intuitively 

There’s this notion that machine learning is a complex kind of technology that can only be used and understood by high-level experts. While there’s certainly some truth to this (it’s complex stuff!), a good business analytics platform can bring the power of machine learning to everyone within an organization.  

Machine learning, despite its relative complexity, is actually one of the advances pushing data democratization within enterprises. This means more people are getting their hands dirty with data, which in turn breeds a corporate culture where quantifying problems becomes the preferred mode of solving them. Platforms such as ThoughtSpot are ushering in this new era of analytics through machine learning-enabled analytics tools. 

For instance, search-driven analytics is a revolutionary new way for people without deep data experience to harness the power of machine learning. Users can run queries through a search bar the same way they would look something up on Google. This puts the power of analytics in the hands of more people, which in turn leads to more insights that require less time for action.  

Better Predictive Modeling 

While not knowing what tomorrow holds can add some spice to life, you always want to minimize risk and uncertainty at the corporate level. Unfortunately, this continues to get harder in our everchanging world. Research from McKinsey has shown the median age of S&P 500 companies has shrunk drastically over the past two decades—from 85 years in 2000 to just 33 years in 2018.  

Part of the issue here is that technology is increasing the rate of disruption. In the past, it was possible for companies to sit on their laurels for decades if they had a monopoly-like business model. Today, you’ll be dethroned in short order if you’re not in a constant state of innovation.  

Machine learning analytics can help organizations do a better job of modeling the future. Whether you’re trying to maintain your market share, or disrupt an entire industry, having machine learning by your side can catapult your enterprise toward success.  

There are clearly many reasons why machine learning is an important element to business analytics today. Leveraging platforms that feature this technology can help your organization stay ahead of the competition.