Bad data can lead organizations to make mistakes. If your organization isn’t continuously ensuring data is accurate, you can never be sure the business decisions you’re making are smart.
So, how do you continuously validate your business data without consuming excessive resources? The answer starts with your data professionals, who are entrusted with massive volumes of internal, customer and partner data. A breakdown in database performance can consume valuable time finding a fix. Also, data that’s not properly tested and validated can lead to poor business decisions. IBM estimates poor quality data costs the U.S. economy $3 trillion per year.
Understanding where to implement data validation tests is a vital step to ensuring accurate data. Data testing is traditionally a development-focused initiative that involves testing smaller units of functionality and asserting what the expected results will be within a development or test database environment. Data validation, on the other hand, focuses on production databases and involves validating larger processes. These processes can include Extract, Load, Transform (ELT) and Extract, Transform and Load (ETL) batches, data and application integration, vendor data feeds and data exports for partners.
While data validation appears to be a massive undertaking, making small but important changes now will set you up for future success. Consider these approaches to incorporate testing and validation into standard database platform operations.
Conduct Unit Testing
Test small units of functionality in isolation from other parts of the system. Your database management team will be able to offer temporary or mock-up data stores that will allow data professionals to test smaller subsets of data.
Conduct Integration Testing
Verify units of functionality combine to produce useful results. This will mean taking a careful look to make sure data gets from point A to point B with the intended transformations. Breaking down these processes into bite-sized pieces is the key to testing business rules throughout integration processes.
The keys to building quality test plans include being able to standardize tests, repeat them on a schedule or within a continuous integration/continuous delivery (CI/CD) pipeline and make testing results transparent to stakeholders. Developing an automated process is fundamental to taking a proactive approach as you incorporate testing and validation into regular processes.
Monitor Production Databases
Don’t stop at testing and validating data only when updates are deployed. Validating that data is correct should be an integral, ongoing process and will ensure confidence in the data.
Validate counts, balances and totals anywhere data is transferred from one system to another. Monitor that values fall into expected ranges, domain values are adhered to and data heuristics are met. Also, evaluate validation results over time to determine overall data health. Is the data optimized for business needs? Are there holes in the efficiency process that should be addressed immediately? Leverage historical validation results for baselines to identify exceptions and determine a path forward for continuous monitoring processes. Make sure you have the needed capabilities for ongoing testing, validation and monitoring processes for your database environments.
The journey to a proactive approach to database testing—and away from a traditional reactive or ad-hoc approach—will come with its challenges. Bad data affects companies of all sizes in all industries. Once you understand where to implement data validation tests and start following these best practices for testing and validation, you’ll be well on your way to stopping bad data from compromising your organization’s ability to make sound business decisions.