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Finding Value in IoT Data

When companies leave their Internet of Things data untouched, the result is an underperforming asset.
By Tomer Shiran
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.
• Object storage is generally used for storing this data because of its flexibility, scalability and low cost—but object storage isn't easy to connect to analytics and BI tools in the first place.
• IoT data is massive, and analyzing it calls for elastic computing resources that are independent of storage, and that can easily adapt to heavy analytics workloads.
• Building a semantic layer is critical, given the breadth of data that's available for analysis and the difficulty of interpreting it.

As a result, many companies leave their IoT data untouched, and it becomes an underperforming asset. How can companies fix this situation?

Making Data Consumable
IoT device data is often kept in object stores such as S3. These days, users often need to manually transform that object store data into a format that is consumable by the tools they use. And while data continues to grow, the effectiveness of processes such as "extract, transform, load" (ETL) is not increasing to keep up, and performance continues to suffer as the size and complexity of datasets increases.

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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