Posted on: 12 April 2022
Using data quality monitoring software is a good way to ensure the information your organization collects and uses will be in good shape. Given the role data monitoring plays in some operations, potential adopters may be curious about how it does the job. Here are 5 elements of the typical data quality monitoring system.
The simplest way in many instances is to create definitions. If a company uses special characters to protect database forms against SQL injection attacks, for example, the coders and data scientists at that organization should know what to look for. They can create definitions that clean up common gibberish, such as mangled HTML entities, left behind by data sanitizing efforts.
Most data monitoring software packages have methods to handle the creation of manual definitions. You can simply collect the characteristics of each definition and add them.
Logging and Patterns
Logging all input attempts is another way to keep tabs on data. Using the logs, the data quality monitoring software can study patterns. If it sees commonalities among the logged failures, it can assemble a definition based on the commonalities. You can then tweak the definition or put it work as-is.
For example, the software might see issues with certain foreign language character sets. It can flag the potential issues and inform you of the pattern of failures.
Scanning for Duplicates, Blanks, and Missing Values
Another simple solution is to scan the data regularly for duplicated, blank, or missing values. These are common problems that can occur when people click the submit button twice rather than waiting or if they don't fill things in. The system can report on problematic entries and ask you what to do with them. You can also set it up to automatically remove the entries without human intervention.
In many datasets, all or most of the entries will land within certain domains. For example, a database of customers' ages shouldn't have an entry for someone who is 220 years old. The data monitoring software can study the range of typical entries to determine if something is unusual. It can then flag it for your review or eliminate it.
Many databases also apply time and date stamps to entries. From a data monitoring standpoint, it's wise to check entries for freshness. If an entry is especially old, the system can ask you to look at it. You can then decide whether to update or remove the entry.
Consider getting data quality monitoring software to help your company today.Share