Without a doubt, data analytics are a foundation component for a complete IT solution. Most realize that the explosive growth of machine-generated data can reveal keys to improved business performance. The challenge is in making sense of collected data and unlocking its value.
The most recognized concept is predictive analytics. Through machine learning, it is possible to model how a piece of equipment operates and identify the factors that influence its behavior. Once you understand the leading indicators to a failure event, you can then monitor for those conditions and take proactive action. This is a powerful tool when applied to mission critical equipment where unexpected downtime has a serious impact to the business.
Less attention has been given to prescriptive analytics. Leveraging the same data models that are used to understand equipment behavior, you can establish a desired baseline of performance and then compare that to real-time operations in the field. Not only does this enable you to identify equipment that does not conform to the baseline, but this technique can provide a prescriptive remediation plan for bringing it into compliance. With the right systems in place, you can immediately deploy the plan to equipment through automation.
When thinking through your IoT strategy, I encourage you to combine both types of analytics to achieve your business objectives. Together, it will maximize the uptime and performance of your critical corporate assets.