How To Establish Performance Indicators For Data Quality Monitoring
Data quality monitoring is critical for virtually all organizations collecting and analyzing information. However, you will need to do more than find the right data monitoring software and let it go to work. Organizations also have to establish performance indicators so they can determine whether their data does or doesn't meet quality standards.
How do you produce those metrics, though? Your organization can use these four methods.
Some data quality monitoring issues are well understood. For example, you probably don't need to do extensive research to learn what a malformed email address looks like. Fortunately, many types of data quality monitoring software have built-in definitions. If your use case matches one of these definitions, there's a good chance you can use it out of the box. Even if that's not the case, you may be able to modify the definition to meet your need.
Curation and Training
One of the more straightforward techniques is curation followed by training. You curate a collection of data manually so you can have confidence in its quality. Suppose you need to have the data quality monitoring software ingest business addresses from a scraping script. You can set aside a dataset of addresses that meet your quality standards. The software can then compare other addresses to the confirmed ones to determine whether something is amiss.
If you have a way to collect feedback from users, you may also have a way to collect ratings. Suppose you need to analyze the quality of content displayed on a set of web pages. You could add a rating for each page. When users rate pages, you can then feed the data into the system to analyze quality. If a page generates a lot of low ratings, your data quality monitoring tools can flag it as problematic. Likewise, high ratings will flag excellent quality.
Another method allows data through initially to see how well it performs. This approach works best if problematic data is likely to kick out errors during analysis or in production. Presuming that's the case, you can log the errors and what caused them. The resulting dataset should give the data monitoring software a good base for determining what a messy entry looks like. It then adjusts to what appears to be wrong, performs fixes, and check for problems again. By rinsing and repeating, you can often quickly narrow in on what looks like the most successful solution. To learn more, contact a data monitoring service.