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Internet of things (IoT) data promises to unlock unique and unprecedented business insights, but only if enterprises can successfully manage the data flowing into their organizations from IoT sources. One problem enterprises will encounter as they try to elicit value from their IoT initiatives is data drift: changes to the structure, content, and meaning of data that result from frequent and unpredictable changes to source devices and data processing infrastructure.

Whether processed in stream or batch form, data typically moves from source to final storage locations through a variety of tools. Changes anywhere along this chain -- be they schema changes to source systems, shifts in the meaning of coded field values, or an upgrade or addition to the software components involved in data production -- can result in incomplete, inaccurate, or inconsistent data in downstream systems.

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