Industrial data management is a critical aspect of IIoT.

If you don’t properly manage data when it’s first collected, for instance, there are limited benefits to making quantifiable and data-driven decisions in IIoT.

By applying IoT data management and analytics best practices, you’ll collect, sort and analyze terabytes of data to extract value. After it’s collected, industrial data is distributed to the appropriate caches, whether lakes or silos. Finally, it’s sorted and grouped together for future analysis. Filtering and categorizing data is essential for associating it with signal types such as vibration, temperature, or pressure, etc. If data is sent in a stream to a single source with no useful structure, it’s impossible to manage.

In the past, data was collected and stored in the form of random transactions on a server. Many companies have hundreds of these unused data silos filled with uncategorized information. Think of this data as a refrigerator-sized box of screws. How difficult would it be sifting through the box to find the right size for your specific need? Like data, if someone organized the like types together, you could find what you need quickly and efficiently.