Any operation that handles data faces a core problem known as GIGO. In computer science, GIGO refers to the problem of garbage in yielding garbage out. Whenever a system ingests low-quality data, it's nearly impossible to avoid spitting out low-quality analysis.
How do you attack the problem, though? Data monitoring is critical so let's look at how you can start cleaning up the ingestion and output processes with the right tools and attitudes.
Identify Ingestion Points
Foremost, you'll need to be sure your data monitoring software is looking at all of your system's ingestion points. Ideally, you can implement a data quality monitoring regimen from the start.
However, many organizations have numerous legacy systems. In these organizations, the team must map all the data ingestion points so they can begin monitoring. Once they've identified where ingestion occurs, they can turn the software loose to scan for data quality issues.
Establish Patterns
Many data quality problems follow certain patterns. For example, a company might have implemented protections against SQL injection attacks in its online customer service model decades ago. This is an absolutely necessary security provision, but it can also lead to widespread data quality issues.
Fortunately, most code for handling SQL injection risks conforms to a set of patterns. Once you've trained the data monitoring software to those patterns, it can usually repair the issues automatically.
Broad Scanning
You should also perform broad scanning of your datasets, especially if you have older ones that haven't previously been subject to data quality monitoring methods. A broad scan can identify common problems, such as duplicates or shifted fields. You can then have the software conduct wide corrections to improve overall quality.
Check Outputs
It is wise to also check for garbage coming out of the system. Whenever you use analysis tools, for example, it's prudent to run data quality monitoring software beforehand. You can take a limited set of data and quickly verify its quality. If there are detectable problems, the software can flag it for correction and a human can either verify or reject the correction.
Notably, this also serves as a training tool for the software. As humans supervise the process of analyzing potential garbage outputs, the system can use confirmations and rejections to build more patterns.
Foster a Culture of Data Quality
Over the long run, an organization needs to value data quality at a cultural level. People need to trust the software and deploy it in all tasks. This will create a virtuous feedback loop to promote constant improvement.
For more information, contact a data monitoring software company like FirstEigen.
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