Companies in a wide range of industries rely on data ingestion to understand what's happening in the world and to make decisions. If the ingested data looks like trash, though, it can be challenging to figure out what to do. Follow this list of recommendations to reduce the odds your data ingestion processes will lead to poor results.
Data Quality Monitoring
Monitoring is critical to figuring out what's happening and why. Fortunately, modern data quality monitoring software allows you to quickly analyze inputs and outputs to determine where things are going wrong.
You can develop an ideal version of what a particular dataset should look like and train the data monitoring software to identify it. Subsequently, you can run through your processes while allowing the software to monitor for potential defects. The system will then score the ingested data on how much it matches the ideal version. You can then use the logs to identify which parts of the process appear to be failing so you can drill down and find solutions.
Notably, you'll need to have standards so the data monitoring systems can do their jobs. For example, it's wise to adopt specific typing for ingested data so you can be sure there won't be a risk of an ugly conversion. If the ingestion tools are storing everything as a string value, for example, that could cause problems when you need to pull out numerical values. Regardless of how strongly or weakly typed your preferred data processing tools are, it's a good practice to strongly type the values during intake.
You can use these standards to train the data quality monitoring software. With everything following strict standards, the system should be able to quickly identify anything that deviates from them. In many cases, the software may even be able to make the necessary corrections without human intervention.
Data monitoring methods should be deployed as far forward in the process as possible. Some folks assume, for example, that commercial vendors will always scrub their data and maintain high data standards.
Even if this ends up being true, you should be aware that their standards aren't necessarily your standards. A minute difference, such as using a 32-bit integer to store a value while a vendor uses a 64-bit floating-point number, could have catastrophic consequences if it leads to mangled data going into production. The smart move is to develop strong standards and use data quality monitoring software to scrub ingested data from the beginning of the process.
For more information on data monitoring, contact a professional near you.