Data Contract Testing: Keeping Data Quality on Track
Data Contract Testing: Keeping Data Quality on Track
Robert Bruce, Chief Technical Officer
A Safety Net to Avoid Disruption
A Safety Net to Avoid Disruption
Imagine if an insurance app suddenly crashed because a data format changed overnight. Policies get mispriced, claims stall, and customers lose trust. This nightmare can happen when upstream data changes unexpectedly break downstream applications.
Data contract testing is the safety net that stops these surprises. It makes sure everyone plays by the same data rules, much like an agreed blueprint. In technical terms, a data contract defines the structure, format, and quality of data shared between producers and consumers – think of it like an API contract, but for data. When teams test against this contract, they catch issues early and keep data flowing smoothly.
In this article, we'll explore why data contract testing is extremely beneficial for data integrity and CI/CD pipelines. We'll see how poor data quality can wreak havoc on a business (with real insurance examples) and how much it costs to fix.
Data Contracts: What are they? Why do they matter?
A data contract is essentially an agreement on what data should look like. It lays out the expected schema (like field names and types), acceptable values, and even rules about the data’s quality. If you’ve seen API contracts in software, this is the data equivalent. It spells out all expectations so there's no ambiguity. By making “semantic and quality expectations explicit”, data contracts ensure everyone – from the team providing a dataset to the apps consuming it – has a common understanding of the data.
In practice, a data contract might say: “The policy_number field is a non-null string of 10 characters, and the premium_amount is a positive number. No more than 1% of records can have missing customer IDs.” These kinds of rules can be written down (often in a YAML or JSON file) as the contract. The Open Data Contract Specification (ODCS) 3.0.1 is one popular open standard for doing this. It defines a structured format to capture all these details, from schema to data quality checks, in a platform-neutral way. ODCS was even born from real-world needs (PayPal originally used it to prevent data mishaps).
Crucially, data contracts aren't just documentation; they can be actively used in development and testing. Teams can use the contract as a basis for automatic checks – validating that the data produced actually meets the agreed structure and quality. This is where data contract testing comes into play.
Catch Issues Early: A data contract test will detect schema mismatches or unexpected data changes before deployment. It’s far better to fail a build than to corrupt a production report.
Automate Quality Checks: These tests run automatically as part of the pipeline. The contract acts as an automated checklist, verifying the data’s structure and format so services stay steady and work great across all platforms.
Faster, Safer Releases: By embedding contract tests in CI/CD, teams can move faster with confidence. It reduces the need for slow end-to-end tests, because many problems are already screened out.
No Breaking Changes: Perhaps most importantly, contract tests ensure backward compatibility. If an upstream data producer introduces a change that would break a downstream consumer (for example, renaming a column or changing a data type), the contract test fails and stops the deployment.
Data Integrity in CI/CD Pipelines
Modern development moves fast. Continuous integration and continuous delivery (CI/CD) pipelines deploy changes rapidly. Without checks, it's easy for a change in one part of the data pipeline to ripple down and break something else. Data contract testing acts like a gatekeeper in the CI/CD pipeline, preventing bad data changes from ever reaching production.
Think of it like a spellcheck for your data pipeline: any non-conforming data gets flagged immediately. In a continuous delivery setup, this is crucial.
Keeping data integrity intact and deployments safe.
The High Cost of Poor Data Quality
Why all this fuss about enforcing data contracts?
Because bad data can wreak havoc on business operations; poor data quality is a silent killer in data-driven businesses. For AI models and BI tools, the impact can be devastating. Flawed data skews insights, drives incorrect decisions, and introduces operational risks.
When poor data enters these systems, businesses often face:
- Inaccurate AI Predictions: Incorrect data skews training sets, leading to unreliable models that make bad decisions.
- Broken BI Dashboards: Reporting tools may fail to provide meaningful insights, making performance tracking difficult.
- Increased Operational Burden: To isolate bad data, teams must introduce extra validation steps, manual corrections, and repeated data pipeline tests - all of which increase costs and waste valuable resources.
The result? Hours of effort spent diagnosing issues that could have been avoided with the right preventive measures in place.
Insurance: Why You Should Care About Data Contracts!
Nowhere is this more evident than in data-heavy industries like insurance. Insurers live and die by their data – whether it’s customer information, risk models, or claims data. If that data is wrong or inconsistent, the consequences can be dire.
Real-world examples in the insurance sector highlight the stakes:
- Revenue Drain: Bad data directly hits the bottom line. A recent MIT Sloan study found that poor data quality can cost insurance carriers up to 15-25% of their total revenue. Imagine losing a quarter of your revenue simply because of data errors – that’s a huge toll.
- Operational Headaches: Poor data quality leads to costly rework. Insurers often need to manually correct errors or repeat processes, adding hours of wasted effort and missed business opportunities.
- Underwriting and Claims Errors: Inaccurate or inconsistent data can lead to bad decisions in underwriting and claims, risking overpayments, incorrect pricing, or rejected claims that should have been approved.
- Loss of Trust and Compliance Risks: Poor data erodes confidence in reporting. Worse still, regulatory fines for incorrect data can be substantial.
To put the cost in perspective, Gartner analysts estimate that on average the cost of bad data to an organisation is $12.9 million per year across industries. Insurance companies, dealing with massive volumes of data, often feel this pain acutely.
Data quality isn’t a “nice to have,” it's critical, and enforcing data contracts is one of the best ways to do it.
Now Is The Time To Take Action
Implementing data contract testing with ODCS 3.0.1 might sound complex, but expert guidance can make it smooth and effective. Our consultancy services are designed to help businesses:
- Identify key data flows that would benefit most from contract testing.
- Implement ODCS 3.0.1 within their CI/CD pipelines, ensuring automated checks that stop issues before they arise.
- Design practical, effective data contracts that strike the right balance between flexibility and control.
- Develop processes to ensure contract testing becomes a natural part of your data lifecycle, preventing bad data from ever reaching downstream systems.
By partnering with us, you'll avoid common pitfalls and ensure your data contracts deliver real value. Our experts can help streamline your implementation, saving your team time and reducing the risk of unexpected issues down the line.
Conclusion: Better Data, Better Business
Data contract testing isn’t just about technical correctness; it’s about ensuring your business has accurate, trustworthy data to make decisions with confidence. By leveraging ODCS 3.0.1 and expert consultancy services, you can dramatically reduce the risks posed by bad data while improving data quality across your organisation.
The benefits are tangible: fewer integration headaches, more stable CI/CD deployments, and significantly reduced risk of costly data mistakes. Imagine the peace of mind knowing that a change in a policy database won’t unknowingly sabotage an underwriting model or a customer-facing app – because your contract tests will catch any deviations instantly.
Want to improve your data quality with data contract testing? Contact our team to find out how we can help you build a robust, scalable solution tailored to your business needs.
