Data quality monitoring is an essential task in many modern work environments. Keeping out junk and gibberish is a central problem when it comes to data monitoring. Let's look at how to attack the issue.

Automation

Generally, an organization collecting data doesn't have the resources to look at it all manually. While you may be able to personally check samples here and there, it's not a feasible solution at scale.

Automation is critical. Specifically, you want to utilize data quality monitoring software to track and remedy problems. Monitoring can help you automate the detection process. That's a big deal, especially if you're only seeing occasional problems.

Pattern Definitions

Data monitoring software leans heavily on defining patterns. If you've seen issues before, you can use the logs of what went wrong to identify the trouble.

Likewise, there are many common problems in data collection. For example, many firms that collect web form data by way of user entry run into junk from database protection scripts. These scripts are front-line defenders against injection attacks, but they often leave garbage where they scrub inputs and convert unrecognized or unacceptable characters into safe entities.

That's great, and you don't want to undo the work on account of data quality monitoring needs. However, you will need to fix it. Thankfully, you can usually reverse the scripts to create definitions that will fix the relics.

Early Intervention

It's tempting to let data collection systems run to completion and then implement fixes later. However, this can lead to confusion about what's going on with the data. If you took in data through web-scraping, for example, you might have to track down the source to see what created the junk in the entries. It's always easier to deal with these problems early so deploy your data monitoring software upfront.

Normalization

Junk items can also create data misalignments. Once you've applied the basic fixes, you may also have to verify how everything lines up. It's a good idea to have your data monitoring tools perform additional passes over the information. If there's something that doesn't fit with the rest of the database entries, it can flag and fix it.

Tracking and Refinement

Keep detailed logs of which entries created trouble. Ideally, you should have logs tracking each entry from its source to the moment the system flags it. Continue refining the process so your system will work faster and more effectively as it encounters more issues.