Data quality monitoring software can help you stay ahead of problems with the information your organization is using. You may wonder how important data monitoring is in a particular situation. Let's look at four times you'll want to have data quality monitoring practices in place.
One of the most critical use cases is anything that involves unsupervised automation. If an organization is collecting data from the field and using it to handle automated tasks, it can't risk creating trouble.
Suppose a supermarket chain is ingesting data from IoT devices that monitor temperature control systems. You might assume your overall control of the devices means data monitoring software isn't necessary because you control all the inputs.
However, sensors can be tricky things. A sensor might go dead white the attached device keeps taking readings. Consequently, it might send a missing or NaN value to the main system. This could trigger a host of scenarios, from a full-on system fault to the device treating the value as valid. Ultimately, that may lead to an unpredictable outcome with a temperature control unit that handles food.
Passing Through to Reports
Another scenario involves systems that pass data through to reporting software. For example, a fashion company might use data dashboards to monitor social media trends. If you're not engaged in data monitoring, there's a risk the garbled data will get through. Depending on how the system handles it, this could cause a crash. It might also leave your boss wondering what the heck happened to the system.
This issue is something of a two-way street in terms of data quality monitoring. Foremost, companies need to monitor inputs to prevent injection attacks. However, many organizations already scrub their outside inputs aggressively, especially from websites and apps.
This often leads to the use of HTML entities to represent particular characters. Also, some systems scrub inputs at every stage. A company might scrub the input at the time of user entry and again when someone internally handles it. The net effect is gibberish nested within gibberish.
Data monitoring software can follow patterns to detect when this happens. They can reverse the process and reshape the data into something secure and readable. You'll have readable data without sacrificing security.
To err is human, and that's especially the case when people enter data. A company surveying the public may see errors with names, addresses, and even entries. Data quality monitoring software can check for errors, make corrections, and reduce the risk of information going to waste.