Know the Difference between Good Data and Bad Data for Better Automated Manufacturing Outcomes
Automation is transforming the way manufacturers operate at every level and across industry verticals. It’s a powerful way to accelerate processes, deliver better products faster and generate revenue faster and more broadly.
At the heart of quality, manufacturing automation is data. It’s the raw material needed to provide information that helps machines and processes run correctly.
Like with other processes, if the raw material you provide is poor, then the end products will be adversely affected. That means ensuring that you consider the quality of your data as closely as you would when making decisions about any other materials used in the creation of your products.
Understanding the Difference Between Good Data and Bad
When considering whether your data is good or bad, it’s important to look at several components:
Validity. Data validity is about how your data is structured and organized. Your data needs to be cleaned, prepped, and checked to ensure that it is formatted correctly, consistently stored and tagged, and structured, written, and organized the same way throughout. Think about dates. If you use “MM-DD-YYYY” and some of your data is in “MM-DD-YY” format, your processes will have a problem
Accuracy. Is your data accurate? When you have confidence that the data you’re using for your manufacturing automation processes, you will be more confident that your outcomes will deliver the desired results. Accurate data means ensuring that machines and devices used in your manufacturing processes are properly calibrated and generating true information that leads to better results. It means having standardized processes that verify, double-check, inspect and adjust accordingly to ensure accuracy. There are often multiple variables, coming in from multiple sources, as part of data collection and use. Accuracy also means ensuring that data is not compromised when you are processing and consolidating information via various channels and multiple, complex steps. That means your manufacturing business must have confidence, or apply due diligence, to any data you may acquire from other sources
Completeness. Data completeness is about having a comprehensive set of data, with no missing pieces that can lead to inconsistencies or errors in the manufacturing process. Data completeness means ensuring that gaps are filled and missing information is sought out.
Timeliness. Is the data you’re using the most recently generated or acquired? Does it reflect the latest results? Your data needs to be available and accessible at the moment of need. Without having timely data, your processes could suffer from using outdated information. You can start improving data timeliness by examining internal workflows to ensure that data is available at the right time for the right process
Uniqueness. Is your information scrubbed and scrutinized to ensure it is not repeating results? If data is repeated, it can throw off results, counts, and add unnecessary expenses to your operation.
At PrimeTest Automation, we design and build custom manufacturing automation systems for assembly lines and other manufacturing facilities. We understand and value the importance of good data in creating quality automation solutions. To learn more and discuss your automation needs, contact us today.