FEATURE : INDUSTRIAL IOT
BUSINESSES ALSO MUST CONSIDER HOW THEY WILL HANDLE DATA COLLECTION , CONTEXTUALIZATION AND ANALYSIS WHEN PROJECTS SCALE OR PRODUCTS ROLL OUT .
exponentially more data in a never-ending stream . Getting relevant data to teams within the company and making sure they have the tools to use it to its best advantage is another challenge .
Businesses also must consider how they will handle data collection , contextualization and analysis when projects scale or products roll out . Many businesses make the mistake of evaluating either the technical value of industrial IoT data in context or its business value , not both . Consequently , homegrown data analysis tools developed in pilot stages may not be designed or resourced to scale to full-rate production or to other projects .
A related concern for many executives is whether their company has the deep IT skills required to build and maintain cloud-native IoT apps or the data science resources needed to uncover vital insights from the data collected .
These executives might also ask themselves : ‘ Do we really want to use our coding resources on this when they could be executing our core mission ?’. This is especially true in times of business uncertainty when large expenditures are difficult to justify .
How does OI make it easier for businesses ?
OI accelerates the realization of common value-adding industrial IoT use cases by using ‘ out-of-the-box ’ solutions . It enables users to leverage their industrial IoT data to gain insights on common concerns like uptime and downtime , maintenance optimization and
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