Applying foundational techniques and practices like Data Validation, Real-time processing and "strong ETL" are helpful in establishing your Data Warehouse from the start, and if they are not done (well), then you may be at a disadvantage when dealing with deadlines and a fast pace of requests. I would add that having a deep understanding of the related business function is also critical. How can we measure "accuracy" if we don't know what the data is "supposed to say". This usually requires engagement from others in the business (outside of the data team) to solidify the understanding and assure that the quality tests, data models, and resulting metrics are aligned with how the customers (internal or external) are expecting to use data.

To ensure data accuracy without delays in data warehousing, start by implementing automated validation frameworks to check data consistency and quality across source and target systems in real-time. Use Change Data Capture (CDC) and incremental loading to optimize ETL/ELT processes, reducing processing time while maintaining accuracy. Leverage AI-driven anomaly detection for early identification of inconsistencies. Collaborate with business stakeholders to align data models with expectations and conduct thorough testing in staging environments. Establish clear data governance policies and monitoring tools to maintain consistency and enable faster issue resolution.

There are many things outlined by others that are brilliant suggestions but if you’re really under the gun - there’s only one thing to do; Prioritise the critical data sets

Here few strategies - automate etl audit framework it helps to validate the data accuracy source vs target tables - proper unit testing and Quality testing and UAT will helps many bugs - need to document functional requirements and acceptance criteria

Automate key processes like real-time data validation and monitoring. Use tools like Change Data Capture (CDC) to process updates as they happen and optimize your ETL/ELT workflows with incremental loads and cloud-based platforms for faster data handling. Set up automated alerts to catch issues as soon as they arise, minimizing the risk of errors. It’s also essential to establish clear data standards and have dedicated data stewards to maintain consistency. Implement machine learning models to detect anomalies and perform thorough testing in a staging environment before moving data to production. This combination of automation, proactive monitoring, and solid governance ensures data accuracy while avoiding unnecessary delays.