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Finding Value in IoT Data
When companies leave their Internet of Things data untouched, the result is an underperforming asset.
Jul 08, 2019—
Data consumed and produced by Internet of Things (IoT) devices is growing at an ever-expanding rate—there will be nearly 31 billion IoT connected devices by 2020. But the data generated from IoT devices is only valuable if you can analyze it, and performing that analysis presents its own set of challenges:
• Sensor and machine data is highly unstructured, which makes it difficult to use with traditional analytics and business intelligence (BI) tools that are designed to process structured data.
Making Data Consumable
Users need a platform that allows them to connect their favorite BI or data science tools directly to their data regardless of where it is located, and regardless of how it is structured, without compromising on performance. The platform also needs to expose data from any source using the robustness and flexibility of SQL, since, in the majority of enterprises, SQL is the most popular data-access language known by users.
Building a semantic layer is critical, too. The semantic layer provides meaning and context to the underlying data so that business users don't need to build a sophisticated understanding of the underlying ways in which the information is stored. When data is properly tagged, catalogued and made searchable, its value increases because teams can more easily build a shared understanding that helps them reach, and act on, conclusions.
Finally, IoT data sets are generated by a huge number of devices, and these devices record a broad array of data, enabling a broad array of use cases, including maintenance to operations optimization to supply chains. But the value of this information is increased when combined with existing enterprise data sources, such as sales, customer and product information.
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