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Operationalizing IoT Data to Drive Revenue in the Enterprise

Digesting data and devising a way to collect, analyze and operationalize insights can result in profitability.
By Rick Veague
Jul 22, 2018

Connected devices can generate valuable data streams that provide a wealth of knowledge which enterprises can employ to enhance product and service solutions. This potential for enterprises to successfully transform their business models and increase profitability isn't being tapped to the extent that it can be. Sixty percent do not possess the analytic capabilities they need to leverage this data, according to an IoT study conducted by Capgemini. Unfortunately, most of these data streams pile up in silos and are forgotten about, but digesting this data and devising a way to collect, analyze and operationalize these insights will result in profitability, if done right.

Understanding the Potential of IoT Data
Operationalizing data from connected devices begins with knowing what it can do for your company. Take the example of how asset-intensive service operations can take advantage of predictive maintenance insights generated from data streams. Already, on the plant floor, IoT data is used to drive condition-based maintenance and even preventive or predictive maintenance. Driving IoT data to asset performance management or EAM software can help companies track which components are operating correctly and capture a comprehensive snapshot of asset readiness and operational capacity.

For instance, an industrial manufacturer can take data from the equipment sold to its customer and convert it into a resource to help sell and deliver on aftermarket service contracts. In other words, data insights can fully transform a product-centric business model into a service-forward product-as-a-service model.

Modifying Your Monitoring Along the Way
It's important to go in with some direction on which patterns of data you're interested in analyzing. It might be clear that factors like temperature or rotational speed could be worth tracking in your pursuit to determine which factors negatively affect total throughput. There might be customers demanding more proactive service, and who would welcome an annual maintenance contract by which service is driven by real-time condition monitoring.

As monitoring progresses, making note of other factors affecting the process being studied could provide valuable observations worth adjusting your initial objectives around if it impacts productivity. For example, you might find that vibration patterns appear to accompany the temperature and speed factors in affecting total throughput, making it clear that this should be monitored as well. By and large, setting concrete objectives regarding what to achieve with IoT data is important, but reworking along the way will ensure no trend is forgotten.

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