Businesses are happy to have more data about their operations, customers, and the results of strategy implementation. The only problem is that, once they have it, they may not know exactly what to do with all that information.

Not knowing the best way to read, understand, and apply data can actually be costing your business. Those costs could take the form of lost revenue opportunities, lower efficiency and productivity, quality issues, and more. For example, Forrester reports that between 60 percent and 73 percent of all data within an enterprise goes unused for analytics. And that's despite the fact that more companies are talking about big data, using technology to capture more data, and acknowledging the value of this information.

The Costs of Misused Data

Today many industrial and manufacturing companies are using equipment that records vast volumes of sensor data. Unfortunately, these same companies are not putting that data to good use. One costly area they should be targeting is downtime, which can happen suddenly and cause a huge hit to productivity. If companies were using their data to predict downtime, they could plan appropriately and maintain, or even increase, productivity.

Now, it's not that companies are intentionally misusing their data. They are simply trying to understand what it is that they have, which is understandable considering the high volumes of complex data their equipment is pumping out. Locating and predicting anomalies that would lead to downtime may feel akin to uncovering the proverbial needle in a haystack. Determining what anomalies to look for is the first challenge to overcome, and from there, you would need to know where to look within the data.

Better Ways to Use Data

Fortunately, there are ways to recognize these mistakes and leverage technology for increased financial returns. In its e-book Anomaly Detection & Prediction Decoded: 6 Industries, Copious Challenges, Extraordinary Impact, DataRPM, a Progress company and cognitive disruptor, discusses how six key industries have struggled to use data correctly and what they could change in order to reap its benefits.

This includes applying technology like cognitive predictive maintenance for the Industrial Internet of Things, which can help companies gain insights from patterns in the available data. Detecting anomalies before they lead to costly downtime results in reduced costs and maximized profitability throughout a company's operations.

According to DataRPM's findings, "The Industrial Internet of Things (IIoT) is unlocking new possibilities for asset-intensive industries like manufacturing, aviation, oil and gas, automotive, transportation and logistics, and energy and utilities." Yet the e-book also notes that "almost 85 percent of these industries let this data sourced from trillions of data points go unused."

Lower Costs, Higher Gains

If these industries did apply machine learning-based anomaly detection, they would realize significant financial benefits. The aviation industry could reduce total airplane ground downtime -- after all, more planes in the air means greater revenue. The manufacturing industry could significantly reduce its scrap production. And the oil and gas industry could better address offshore asset breakdowns to maintain higher production levels.

DataRPM's e-book offers quantitative findings to back up these benefits, saying that 1 percent improvement in productivity across the manufacturing industry can result in $500 million in annual savings and that predicting anomalies on time can result in savings of up to 12 percent over scheduled repairs, maintenance cost reductions of up to 30 percent, and elimination of breakdowns by up to 70 percent.

Those are incredible gains just from better leveraging data that's already available. As the machine learning aspect of this technology improves over time, more insights will be available to help companies better utilize their data. As a result, they will see gains in profitability as costs plummet.