4 Data Quality Issues To Monitor
Look at almost any computerized system, and someone somewhere is collecting data from it. This has caused a huge boom in the amount of data, but it also has created a mess in terms of unclean data. Consequently, data quality monitoring is becoming a bigger deal each year.
Data quality monitoring software makes it easier to keep tabs on the state of your warehouse. However, you'll need to configure it to identify some of the most common problems. Folks using data monitoring software should be vigilant about these four issues.
Type
If you insert the wrong kind of data into a field, it's hard to predict what the result will be. Suppose you've built a web scraper to pull dates from a site. What happens if the columns are misaligned or the date format is something unexpected? It's possible the system self-corrects, or it might default to something like 0000-00-00 or some other date.
Your data monitoring software should hunt for these errors, tabulate how often they happen, and point you to all of the specific occurrences. This will allow you to make code corrections to account for errors, misalignments, and missing information.
Processing Artifacts
Anyone who has ever seen a bit of gibberish text that starts with an ampersand has probably seen data that was heavily scrubbed. For example, a server hosting an intake form might scrub the data to prevent a database injection attack. This is all well-intentioned, and it's consistent with best practices on the security side. However, these artifacts can be ugly to the human eye and downright unreadable for many machines.
Fortunately, correction is simple. You typically have to reverse the process, restoring the original information based on the pattern that scrubbed the data. Data quality monitoring software should be able to detect and address these issues.
Synchronization
Some activities used databases to synchronize systems. You might have a phone app that needs to update the information a user interface displays every few minutes.
Most organizations use timestamps to handle this problem, and most applications have fairly high tolerances for inconsistencies. However, high-precision applications such as microcontrollers for IoT devices may need perfect date and time data. Automated monitoring will allow you to confirm that everything is synchronized.
Company- or Industry-Specific Rules
Not all data quality problems are as broadly applicable as the previous examples. You might have specific rules for what is good data. A pollster, for example, may need to toss out garbage answers from insincere respondents. They need data monitoring tools that use rule engines so they can classify and exclude these entries.