1) Ensure as much data validation as possible. Can be manual data checks (rather at the start of the project / MVP phase) or automated checks, like tests that will query multiple data sources speaking about the same data and compare results. 2) Involve business users in creating the tests. Gain overall understanding of data you’re working with and how it should be and validate with business users. Ask them for additional checks that could be done and would make sense. Don’t be able afraid to break the status quo and ask even obvious questions - some things taken for granted in data don’t really have to be this way in reality and it’s better to be safe than be sorry.

To ensure it's reliability, you need to first ensure that the data sources are reliable. This is to ensure that the datasets are accurate. You must also make sure that you cross-check the data with others obtained from other reliable data sources. This is to ensure that the data obtained are accurate. You need to also review and update your data sources. This is to ensure that they aren't outdated as time goes on.

I’d cross-check data from multiple trusted sources to ensure accuracy. Using validation techniques like error checks and consistency tests would help catch discrepancies. Regular backups and version control would prevent data loss or corruption. If needed, I’d consult experts or stakeholders to confirm key data points.

Ensuring data reliability is crucial for making informed decisions in any project. I implemented data quality checks by setting up automated validation rules to detect inconsistencies early. Version control and audit trails helped track changes and maintain data integrity. I also conducted exploratory data analysis (EDA) to identify anomalies and inconsistencies before using the data for modeling. Collaborating with stakeholders to define clear data governance policies ensured consistency across teams

some tips to ensure reliability from a retail/ e-commerce POV: -Customer Feedback Loop: Use real-time customer feedback and reviews as live data points to validate product information and refine inventory decisions. -Omnichannel Data Reconciliation: Cross-check data across online, in-store, and third-party platforms to catch inconsistencies and ensure product availability and pricing accuracy. -AI-Powered Anomaly Detection: Implement machine learning models to flag unusual trends in sales or inventory data -Proactive Data Health Monitoring: Set up automated alerts for important metrics. Sudden shifts can signal data integrity issues, allowing teams to investigate and resolve them before they impact business decisions.