How Machine Learning Is Changing Business Analytics

Below is the transcript of a Webinar hosted by InetSoft on the topic of What Machine Learning Means for Company Analytics. The presenter is Abhishek Gupta, Chief Data Scientist at InetSoft.

Seeing Machine Learning

We're seeing machine learning in organizations now. This isn't coming out of the blue. This has a long history, and so we wanted to spend a little bit of time here. One good thing to do at first is of course to define machine learning, and that's really tricky. I think for better or for worse, in a certain sense machine learning has taken on sort of a pop culture, meaning it's just the rebranding of analytics or data mining.

Then there is this other academic definition because machine learning has been studied so long within computer science departments at universities. We are going to have to straddle that definition today because at InetSoft, and in other places we sort of use machine learning in both of these ways sort of as rebranding of analytics, but to me a true branch of computer science also.

To define machine learning I'm going to contrast it with statistics, and I'm not saying that machine learning is better than statistics. I'm just saying that that machine learning is different than statistics. I think this is one of the easiest ways to define it. Machine learning techniques tend to make less assumptions about data.

We typically look for, in statistics, for normality of the data or for the data to obey certain distributions. With machine learning we can often relax those expectations on the data, which is really nice. Machine learning methods also tend to sacrifice interpretability to promote greater accuracy. Most statistical methods are designed to be highly interpretable and parsimonious, whereas machine learning methods are designed to squeeze the most possible signals out of data.

I also like to put something in the definition of machine learning about systems that are making decisions automatically. Automation is a big part of machine learning. I would also mention the inherent feedback loop which is how the algorithms can learn without manual intervention. We'll talk a little bit more about that when we get into more automation topics later on.

I think we had a similar Tower of Babel 10 to 15 years ago when data mining was a hyped topic. I think machine learning is experiencing the same kind of hype, and we need to be cognizant of what people are talking about when they're talking about different types of machine learning. Is it a specific algorithm set? Is it creating that feedback loop? Is it allowing algorithms to act independently of human interaction? I think all of these can be different definitions for machine learning. I think we have something to say in most of those definitions, and we'll try as we can during the WebEx today.

Machine Learning and Anomaly Detection

Machine learning is also transforming how organizations detect anomalies across their operations. Traditional rule-based systems often miss subtle patterns or generate excessive false alarms, making it difficult for teams to identify genuine issues. ML-driven anomaly detection models learn from historical behavior and adapt to new conditions, enabling them to flag unusual activity with far greater accuracy. Whether it’s unexpected shifts in production output, irregular financial transactions, or sudden changes in customer behavior, these models help businesses respond quickly and confidently.

Another area where machine learning is reshaping analytics is in the optimization of complex processes. Many industries rely on workflows with countless variables—such as supply chain routing, energy consumption, or workforce scheduling. ML algorithms can evaluate millions of possible scenarios and recommend the most efficient paths, often uncovering opportunities that human analysts would never identify. This level of optimization not only reduces operational costs but also improves service levels and resource utilization.

Machine learning is also enhancing the personalization of business insights. Instead of presenting the same dashboards to every user, ML models can tailor analytics experiences based on individual roles, preferences, and historical interactions. Executives may receive high-level summaries, while analysts see deeper drill-downs and predictive indicators. This adaptive approach ensures that each user receives the most relevant information without being overwhelmed by unnecessary detail, improving both adoption and decision quality.

In addition, ML-powered analytics is enabling organizations to simulate future scenarios with greater precision. By combining predictive models with real-time data, businesses can explore “what-if” analyses that account for dynamic market conditions, supply fluctuations, or customer demand shifts. These simulations help leaders evaluate risks, test strategies, and make informed decisions long before real-world consequences unfold. As a result, planning becomes more proactive and resilient.

Finally, machine learning is accelerating the shift toward fully automated decision systems. As models become more accurate and trustworthy, organizations are increasingly comfortable allowing ML-driven processes to execute actions autonomously—such as adjusting pricing, rerouting shipments, or allocating resources. This evolution does not eliminate human oversight; instead, it elevates human roles toward strategic supervision and exception handling. The combination of automated intelligence and human judgment creates a powerful framework for scalable, data-driven operations.