Not only do organizations need to gather data utilizing equipment condition monitoring solutions, they have to know how to analyze it in order to optimize predictive maintenance.

Data has helped organizations in many industries transition from traditional operations models in which "break-fix" support and instinct-based strategies reigned to innovative new ways of doing business. Many companies now utilize data for more effective decision making, predictive and proactive operations management, and information-driven methods. This shift enables companies to scale many of their core business concerns, from supply chains to daily communication activities to marketing efforts, getting more out of their investments in the form of productivity and profits.

In the industrial, manufacturing and mining sectors, big data is a relatively new concept. Many organizations have incorporated quantifiable insights into their approaches, but oftentimes information doesn't translate into action. Predictive maintenance depends upon a company's willingness to strategize their data gathering and synthesizing efforts to produce actionable insights. Otherwise it's just noise and clutter that won't lead anywhere. Manufacturing Business Technology contributor Bob Dean recently remarked that taking action with data represented "the next frontier" for manufacturing firms.

"Data in motion is a term used to describe the continuous interactions between connected elements such as people, processes and things," Dean explained. "Put another way, more and more data types from new devices, sensors and cameras are at maximum value while still in motion."

Smart equipment maintenance devices, from tension monitors to strain gauge sensors, enable operations leaders to stay in constant communication with ongoing activity and asset performance in large industrial sites. Making decisions and correcting issues in real time allows organizations the opportunity to continually ensure that everything is running at an optimal capacity. Additionally, analyzing this data can lead to better informed projects of future performance, leading to an uptick in predictive maintenance and future-looking ideation that can create a sustained rise in profitability.

Why predictive maintenance depends on constant motion
Predictive maintenance helps asset managers identify machines that are at an increased risk for breaking down and communicate that possibility through data. Unless organizations make it a priority to keep data in motion, however, preventative maintenance is liable to lose some of its effectiveness. Automation World executive editor Aaron Hand wrote about a conversation he recently had with an expert on big data within the industrial sphere that illustrated the importance of examining data streams together. Without this key next step, data will be left in silos and can do little in the way of accurate forecasting. Hand's friend, who remained nameless to preserve anonymity, said that while data gleaned from a sensor could indicate increased risk of a system failure, without other corroborating data to give context and the right applications and tools to analyze it, the actual location of the potential failure will likely remain a mystery.

"It's like if I said to you, 'Aaron, I can tell you that something in your house is going to fail tomorrow,'" the friend told Hand. "'But I can't tell you what. So you'll need to have a plumber on hand, and an electrician, and an HVAC guy, and whoever else you might need to fix whatever happens to go wrong.'"

Equipment monitoring systems and data analysis tools in conjunction can help organizations make predictions with clarity and accuracy,