Setting Up Unit Tests for Analytics

Automated solutions are available for unit testing, especially for straightforward checks like identifying duplicates, missing entries, and outliers. Python libraries such as pandas_profiling and pydqc serve this purpose effectively. Additionally, some companies, like Ataccama and First Eigen, provide tools for creating data quality rules and mapping.

However, for intricate scenarios where data quality hinges on specific business concepts, manual interventions are often necessary. I recommend compiling a list using tools like Google Docs or Notion that outlines all potential issues and their tests. This reference can be invaluable for future data quality assurance efforts.

The Nature of Your Data Matters

The approach to developing unit tests largely depends on the type of data you possess. Data varies in structure (structured vs. unstructured), is relevant to different business sectors (like product, marketing, and sales), and addresses diverse queries. For instance, a unit test designed to detect duplicated web page views cannot also validate the accuracy of customer subscription dates. Your testing strategy must align with the data you intend to present to end-users.

Prioritizing Data Importance

Initially, I assumed all data held equal significance. While maintaining quality is crucial, not all data fields carry the same weight. Key metrics like monthly revenue, product type, and cohort start dates are far more critical than less significant data, such as the number of baseball caps a customer owns. Identifying vital data can be achieved by consulting your manager, engaging with stakeholders, or researching the data's role within your organization (e.g., its presence in reports or dashboards, alignment with company OKRs, etc.).

It is also important to note that a currently minor field may gain importance in the future. While it is challenging to predict these shifts, documenting low-impact issues that require significant effort to resolve can be beneficial for later reference.

Starting Simple and Progressing to Complexity

I advise beginning with simple tests before moving on to more complex scenarios. Check for unexpected null values, duplicate entries, and verify that numeric fields fall within a standard range (e.g., 1.5 IQR). Ensure consistent date formats and watch for data type conversion errors.

After conducting preliminary tests, delve into more complex cases. Collaborate with stakeholders to clarify business logic. For instance, when assessing values tied to a consensus definition among various managers, confirm the logic, establish edge cases, and write transformations to encapsulate this logic while spot-checking values to ensure they meet business expectations.

Understanding Variance

When implementing changes that replace existing values, it's essential to assess the variance between old and new data. Here, variance pertains to accounting rather than statistical definitions. Understanding how your changes will affect production systems is crucial before making them accessible to end-users. Failing to do so may lead to questions regarding data integrity, potentially undermining user trust—a trust that is difficult to rebuild.