Data quality issues significantly impact business decision-making by affecting the accuracy, completeness, timeliness, or consistency of analytical data. These issues can arise at any stage of the data pipeline, including during ingestion, storage, transformation, orchestration, or consumption. The text categorizes data quality issues into production-specific and development-specific types, each with distinct causes and implications. Production-specific issues often stem from changes in third-party data sources or infrastructure failures, while development-specific issues are usually due to incorrect technology implementation, misunderstood requirements, or unaccounted downstream dependencies. Solutions for mitigating these issues include using data observability tools, such as Metaplane and Monte Carlo, which help detect anomalies in production environments. These tools are more effective than traditional infrastructure monitoring tools and can inform each other to enhance data quality. The importance of proactive detection to avoid costly errors or risks in production is emphasized, highlighting that many data issues are essentially bugs in data-processing software.