5 Common Mistakes That Lead to Poor Data Quality


Bad data is worse than having no access to data at all. It can mislead teams into making poor decisions, confuse people in an organization, or worse, erode business trust. What makes this issue even more serious, is that issues with data that lead to inaccurate insights and reporting are often found too late in the process – sometimes even the wrong insights have been presented.

Working with some leading data-driven enterprises around the world, which take all the precautions and measures to ensure they have access to the best and most accurate data – we have identified some of the main areas which often lead to poor data quality.

1. Testing data only during development stages:

Many companies make the mistake of only testing their data once during the development stages when in face, this should be an ongoing process. Data changes and evolves as it’s transferred, loaded, and manipulated. Because of this, it’s essential to set the adequate quality checks at every step of the way.

2. Validating only during one stage of the data process:

Similar to the previous issue, some data analysts restrict their testing to one stage, such as the ETL process or when a database is migrated to the cloud. While it is important to verify the quality of the data during these major processes, it’s essential to apply those quality checks to every process in which data is used. From warehouse to final reporting dashboard or visualization tool. Since data is constantly being manipulated and transferred, BI teams and data analysts have a responsibility to ensure accuracy across every stage of the data life cycle.

3. Validating data partially:

While it might be tempting to validate a small sample of your data to then make the assumption that there aren’t any problems with the bulk of it, this is a risky move. To guarantee all data is accurate and its quality intact it should be tested in its entirety. While doing this manually would be virtually impossible, with the right tools it’s not just feasible but necessary.

4. Not testing data with the right frequency:

Many data or insights teams make the mistake of thinking that testing or verifying data quality once is enough. Truth is, data is constantly evolving and changing due to additions, manipulations, and migrations. As a result of this, it’s important to do quality check on your data frequently. These can be also automated so the frequency of the tests is set up optimally and the process is ongoing, automatic, and hassle-free.

5. Testing aggregated data instead of values:

In the interest of saving time, companies often test aggregated data instead of values. For example, you can compare the number of rows to verify a migration was complete but unless you compare values within these rows it’s impossible to fully know if there are any data discrepancies.

These are some of the most common mistakes that lead to major data discrepancies and issues further down the line. What makes it even more bothersome for data teams is that it often takes longer to discover the source of the problem than fixing it. Because of this, we’re on a mission to develop the best data testing solutions – helping businesses take control of their data with tests that can be easily automated and programmed to ensure all data goes through quality check on an ongoing basis to provide companies the confidence needed to make the most of their insights.