Industrial Data Management
Improve Decision-Making Through Industrial Data Management
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.
Good Industrial Data Management Improves Decision-Making
Many manufacturing companies have a data problem, and that’s why sound data management and analytics practices have to be exercised by your IT and manufacturing teams. Your IoT data helps you make both short- and long-term decisions concerning manufacturing operations. If you can’t leverage your data real-time, you cannot make fully informed decisions.
Additionally, if you’re tracking data and trends using an Excel file you’ll find it challenging to use visualizations, which helps you with decision-making.
Purge Out-of-Date, Stale Data
Another problem to avoid is storing data that’s stale or beyond its shelf life. By reducing the amount of stored and collected data, you can more quickly cut through outliers, find meaningful trends, and direct production decisions to smart solutions. To avoid this problem, simply establish a system for routinely purging “expired” data.
By organizing data appropriately and setting up a well-designed data management system, you’ll avoid looking for a needle in a haystack and trying to make sense of unorganized data.
Results Engineering has deep experience helping manufacturers make sense of their valuable data. We help our clients with overall IIoT integration planning and design work, which includes extensive industrial data management support. The result: you’ll extract more value from your data and leverage all of it to improve real time decision-making.
We help our clients with overall IIoT integration planning and design work, which includes extensive industrial data management support.
Common Industrial IoT Data Management Questions
I already have a data collection system, what makes this different?
IoT based data management is cloud based, eliminating the need for bulky equipment, saving you cost and space. Data is also available real-time from anywhere, allowing for easier and more flexible management.
I have all this data. Can I use it retrospectively?
Whenever possible, we will bring all your existing data into your new IoT data management setup so you can have both historical and future data readily available at your fingertips.
Manufacturers have no idea how impactful IIoT/Smart Factory is to their plants.
Results Engineering created a free plant assessment to solve that problem. 30 questions and 30 minutes will reveal all you need to know.
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