Data Trust & The Importance Of Testing

You have likely heard the saying: “Trust is very hard to gain, but very easy to lose.” In 2025, this statement applies to data more than ever. As organisations double down on becoming data-driven, the ability to make informed, confident decisions hinges entirely on the trust stakeholders have in the data they are given. This trust is not built on a single dashboard or one-off report, it is cultivated over time through consistent accuracy, transparency, and responsiveness to issues.

Modern organisations face a paradox: the more data they collect, the harder it becomes to maintain its quality. With self-serve BI tools and AI models generating insights on demand, any data flaw can spread faster and wider than ever before. A single inaccurate metric can lead to costly missteps, whether it is setting the wrong business strategy, misallocating budgets, or damaging customer trust.

It is important to remember that data quality is a business risk issue, not just a technical one. When leaders see quality issues as impacting revenue, brand trust, or compliance, investment in testing and governance becomes non-negotiable.

Embrace Data Testing: The Anchor for Trust

Data testing is not a nice-to-have, it is a non-negotiable safeguard. What is a data test? These are automated checks, designed to run continuously, that validate whether data matches expectations. By catching anomalies early, you prevent faulty data from making its way into decision-making tools such as dashboards and reports. Key test categories every team should adopt:

• Unicity: Ensuring key identifiers (like user_id) remain unique, preventing duplicate or conflicting records.
• Allowed Values: Defining valid states or categories, e.g., ensuring order_status only contains “Pending,” “Shipped,” or “Cancelled.”
• Non-emptiness: Detecting unexpected NULLs in critical fields, which can break downstream analyses.
• Data Recency: Flagging when datasets are not updated within expected timeframes.
• Row Count Monitoring: Spotting sudden drops or spikes that indicate missing or duplicated data.

Always version control your data tests, just like code. This way, any change to a test is documented and reviewed, ensuring your testing suite evolves alongside your business logic.

Embedding Testing into Your Workflow

The most successful teams do not just do data testing, they make it an automatic part of the development lifecycle. That means testing is not an afterthought, but something baked in from the very start. Here is how to embed testing effectively:

1. Define Assumptions Early: Before writing a single line of SQL, define what “good data” means for that model, this might require the context given from stakeholders on what numbers they would expect to see.
2. Start Small, Expand Continuously: Cover the biggest risks first, then iterate to catch more edge cases over time.
3. Close the Loop: Whenever a new issue is found, immediately add a test to catch it in the future.

Embedding tests early ensures that most issues are caught in development or staging environments, long before they affect production data. This proactive approach prevents the dreaded “stakeholder bug report,” where business users discover the problem before you do. Maintain a shared data testing knowledge base where analysts can document common pitfalls and their corresponding test patterns. This accelerates onboarding and ensures consistency across the team.

Our Blueprint at 173tech

At 173tech, we use a three-tiered testing approach to ensure no low-quality data slips through:

1. Local Testing: Analysts run tests locally before submitting changes, catching most issues early.
2. CI/Pull-Request Tests: Automated tests run during the code review process, blocking merges until all tests pass.
3. Scheduled Production Testing: Tests run on the same schedule as our ELT/ETL processes, ensuring data quality is continuously validated as new data arrives.

This layered defence means errors are caught at multiple stages, with redundancy to minimise risk. We apply this to both simple checks (like uniqueness) and complex ones (like anomaly detection based on historical trends). We also automate alert routing so the right person is notified immediately when a test fails, before stakeholders are impacted.

Trust is a fragile thing and automated tests are just one step in ensuring data quality. If you want to build a scaleable data pipeline where data quality is embedded from day one, be sure to reach out to our friendly team.