4 Data Quality Monitoring Mistakes To Avoid

Data quality monitoring is a process that demands a lot of attention. While data monitoring software can handle a lot of the job for you, there are still some basic issues you'll need to address. It is important to avoid key mistakes, in particular, and you'll want to be aware of these four.

Leaving Things Running

There is an obvious temptation to not mess with the system if everything appears to be running fine. However, issues can appear without causing catastrophic failures. Worse, you might not recognize them until they cause specific problems. Depending on how often you encounter certain errors, your datasets could accumulate months or years of errors before you notice anything is wrong.

Do not leave things running just for the sake of not upsetting the apple cart. Even if ingress and egress appear to be going smoothly, you should halt processes from time to time to check for issues.

Unchecked Vendor Data

Vendors typically have data quality monitoring software. However, you should never assume their checks are sufficient for your purposes. Likewise, you should never assume their processes are thorough enough.

Whenever your company takes in data from vendors, government agencies, public datasets, or other sources, run your own scans. Even if there isn't anything buggy with the data, you might find it doesn't fit your structures well enough. You don't want to discover these concerns only after you move the data into a production process.

Types

Data typing is also a common issue that people overlook. While data monitoring tools can scan for issues with types, you need to configure them to achieve your goals. People easily overlook typing as long as their software or code is producing the desired results. However, it's a poor practice to not explicitly type your data to your purpose.

Whenever your system ingests data, you should check it for the appropriate type. Even if the difference is minor, such as the difference in precision between two types of floating-point numbers, you should fix it immediately. This can prevent problems down the road if you make changes to your system in a way that could kick out problems with types.

Not Scanning Output

Organizations often focus on data quality monitoring as an ingestion issue. However, your systems can produce just as many problems with the results. Have processes in place to verify data quality and integrity on the way out, too. It may be the difference between messing up a report or not. 

For more information, contact a local data monitoring software company. 

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