When organizations focus on data quality monitoring, they often see it as a single phase of a larger set of processes. However, a good data monitoring system needs to have layers. Let's explore why there should be layers and how you can do it.
Redundant Checks
More than anything, you want to have redundancies in place. The team at a typical organization comes into contact with data in multiple ways. Consequently, a single layer of data monitoring software doesn't provide sufficient coverage. If you only deploy data quality monitoring software during the intake phase, for example, there will be no check if a user, database, or analytics package fudges something.
Breakdowns
Another argument for building your data monitoring operation in layers is to cover potential breakdowns. If you only deploy software at one point, there will be a single point of failure.
In the best-case scenario, a failure causes the whole system to fail and you have to restart. That would be costly, but at least you'd notice the problem. In the worst-case scenario, data flows without any quality controls and risks going into the final product. If you have decision-making systems, especially unsupervised automated ones, corrupt data could lead to adverse outcomes.
Performance Evaluation
With data quality monitoring in layers, you can set later layers to evaluate performance. You will be able to test how well your filters are working. If you start to see problems at the end of the process, you can start checking for issues upstream. Likewise, data quality monitoring software will automatically trigger reviews if it starts identifying consistently messy results.
Long-Term Warehousing
People regularly archive data without much thought to its quality. Oftentimes, they assume previous data monitoring checks assure nothing could be wrong with the stored information.
However, warehousing poses two major quality control problems. First, the data might be mangled during use or as it goes into storage. Second, a few bits may flip over time due to electrical fluctuations or background radiation. In either case, someone may pull the data from the warehouse and not know there are potential quality issues. Data quality monitoring layers, though, will improve the odds of detecting defects.
Improvement of Metrics
Data quality monitoring should be a continuous process. With the right tools in place, you can monitor the metrics in addition to the data. This will allow you to refine your definitions and filters, leading to sustained improvements.
For more information, contact a company like FirstEigen.
