Maximizing ROI with data science solutions

As you watch a trader’s computer buying and selling dozens of times per minute algorithms humming, data streaming it’s easy to think it’s magic. But that “magic” works because behind it there’s rigorous data-science, testing, back-testing, risk adjustment and continuous feedback loops. In fact, in studies of algorithmic trading driven by machine learning and statistical models, returns far above standard “buy-and-hold” benchmarks have been observed.

If ML-based trading can produce such high returns under the extreme risk, volatility and unpredictability of markets with outcomes often measured in percentages far exceeding typical corporate gains then imagine what data science can do for a business operating in a more stable, controlled environment. With cleaner data, predictable processes, defined KPIs, and less volatility in “inputs,” the same models that drive trading gains can amplify ROI in sales forecasting, operations optimization, customer segmentation, supply-chain analytics and much more.

the same models that drive trading gains can amplify ROI in sales forecasting, operations optimization, customer segmentation, supply-chain analytics and much more.

In short: if algorithmic trading  the high-wire act of finance responds so well to data-driven models, then in business contexts where you control more variables and reduce risk, data science doesn’t just contribute it multiplies value.