FEATURE : INDUSTRIAL IOT
Many industries have undergone Digital Transformation processes , unlocking efficiencies and business benefits . Industrial environments are one such example , with IoT solutions now commonplace . But it ’ s important that organizations identify and leverage tools that will provide detailed insights from these technologies . Harshad Pingle ,
Product Manager for MindSphere , the industrial IoT-as-a-Service solution from Siemens , highlights the importance of putting industrial IoT data in context , as well as how businesses can take immediate steps to begin seeing results .
Operations Insight ( OI ) is a MindSphere solution designed to enable businesses to contextualize industrial Internet of Things ( IoT ) data from connected assets in a way that allows meaningful analysis leading to vital insights .
It can work with data from individual assets within a production line or scale to include enterprise-wide data streams .
The solution enables machine and production insights based on a seamless user experience leveraging contextualized data . It accelerates the realization of common value-adding industrial IoT use cases by using ‘ out-of-the-box ’ intuitive capabilities . It can be configured and extended easily for solution specific customization at scale .
Intelligent CIO spoke to Harshad Pingle , Product Manager for MindSphere , to find out more about the solution and how it is benefiting organizations .
Why is it important to put industrial IoT data in context ?
Any business on a digitalization journey knows that engaging with the industrial IoT is not as simple as adding some sensors and data collection capabilities . It ’ s not hard to drown in the sea of your industrial IoT data if you don ’ t have a solution that ’ s ready to accept it and put it to use . The structure of industrial IoT data is different than that of relational structured data and index-based semi-structured data that ’ s used in manufacturing and business operations .
Each piece of industrial IoT data comes with information about what physical property or state is being quantified , but it also contains contextual clues like when , over what duration , from which systems and how frequently the data was collected . The productivity and effectiveness of any analysis of industrial IoT data depends on how these data streams are processed , combined , compared , inserted and inferred .
A robust solution that can add relevant context information from other systems – customer relationship management ( CRM ), quality management ( QMS ), manufacturing execution ( MES ), enterprise resource planning ( ERP ), etc – to industrial IoT data can illuminate operational behavior . Contextualized data makes analytics more effective and productive and enables data-driven decisions .
What ’ s the business value of putting industrial IoT data in context ?
Contextualising your industrial IoT data is just one step toward making data-driven business decisions . When
Intelligent insights for industrial IoT
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