Adopt a habit of creating data validation at each stage of data processing. You can achieve this via Dags in airflow in an automated fashion. - Extraction verification - ensure all transfers (any regional transfers) and loads are complete. Implement post load audit - ensure right volume of data gets loaded into datalake - Data freshness check - this will ensure you review data at required frequencies and if not trigger alarm ‼️ - Data quality check - check for any nulls and schema formatting errors. - Transformations rules validation - Apply and verify transformation rules to the extracted data. This includes format normalisation, data cleansing, deduplication, and conversion processes to create marts.

To ensure accurate analytics work despite a heavy team workload hindering data validation processes, prioritize data validation by allocating specific time and resources for these tasks. Streamline processes by identifying and eliminating bottlenecks, and automate repetitive tasks using validation tools to save time. Establish regular checkpoints for data validation throughout the analytics workflow to catch issues early. Distribute the workload effectively among team members and consider using sampling techniques to validate a subset of data when time is constrained. Finally, foster a culture that values data quality by providing training on best practices for data validation.

When my team's workload began to impact data validation processes, I took steps to streamline and automate tasks. By implementing automated validation checks and ensuring team-wide cross-training, we reduced manual effort and maintained consistency. I also prioritized critical data sets for validation and implemented scheduled reviews for less urgent tasks. Revisiting our data governance and improving documentation helped balance workload while ensuring the accuracy of our analytics work, even under pressure.

To maintain accuracy in analytics work during a heavy workload, I prioritize tasks and focus on validating the most critical aspects first. I streamline processes by using automation tools and delegating tasks when possible to improve efficiency. Additionally, I rely on a detailed validation checklist to minimize errors and ensure consistency throughout the project. This approach helps me stay organized and deliver high-quality results, even when managing multiple tasks at once.

Automate Data Validation Processes Identify key metrics: Focus on validating the most critical data sets that have the highest business impact. For example, revenue, customer acquisition, or product sales data should take priority over secondary metrics. Create a data validation role: Assign a dedicated team member or create a rotation where someone is responsible for data validation. This ensures the process is prioritized, rather than overlooked in favor of other tasks Streamline the data pipeline Establish data governance frameworks Use data validation checkpoints Configure alerts for anomalies Create a data quality scorecard