Expectations Book

Expectation Books are designed to facilitate data quality assurance and validation. It empowers data analysts to define and run data quality tasks using expectations, ensuring that data meets predefined criteria and adheres to specific requirements. By setting and validating expectations, analysts can instill confidence in the data, leading to more reliable and trustworthy analytical outcomes.

1. Defining Data Expectations: In the Expectation Book, data analysts can articulate the expectations they have for the data they are working with. Expectations are predefined rules, conditions, or constraints that data must satisfy to be considered valid and of high quality. For example, an expectation may specify that a certain column should not contain null values or that numeric values should fall within a specific range.

2. Data Quality Validation: Once expectations are defined, analysts can execute data quality tasks to validate the data against these expectations. The Expectation Book evaluates the data and highlights any discrepancies or violations of the defined expectations. This validation process helps analysts identify and rectify data anomalies or errors, ensuring the accuracy and completeness of the data.

3. Customizable Data Expectations: The Expectation Book offers flexibility in defining expectations based on the specific data and analysis requirements. Analysts can tailor expectations to match the unique characteristics and constraints of their datasets, ensuring that the validation process aligns with their analytical objectives.

4. Iterative and Data-Driven Decision Making: The Expectation Book fosters an iterative approach to data analysis. Analysts can repeatedly validate the data against expectations, refining and updating expectations as insights are gained. This iterative process drives data-driven decision-making, as analysts can trust that the data quality is continuously monitored and improved.

5. Ensuring Data Trustworthiness: By enforcing data quality through expectations, the Expectation Book helps analysts and stakeholders trust the data they are working with. High-quality data leads to more accurate and reliable insights, reducing the risk of making critical decisions based on flawed or inaccurate information.

6. Compliance and Governance: In addition to promoting data trustworthiness, the Expectation Book supports data compliance and governance efforts. By validating data against predefined expectations, organizations can ensure that data adheres to regulatory requirements and internal data standards.

7. Collaborative Data Quality Assurance: The Expectation Book facilitates collaboration among data analysts and teams. Analysts can share their defined expectations with colleagues, seek feedback, and jointly establish data quality norms, leading to a collective effort in ensuring data reliability.