Data quality challenges are prevalent in companies today due to software sprawl, data proliferation, and human errors. To address these issues, data teams should employ data quality management tools, strategies, and processes. Common data quality challenges include managing an increasing number of tools and sources, dealing with external factors affecting data quality, ensuring data engineers understand the context of each metric, obtaining necessary metadata for effective operations, and recruiting experienced data engineers. To mitigate these challenges, companies can invest in data observability tools that provide real-time anomaly detection, usage analytics, and lineage features to improve stakeholder trust and free up time for higher-priority projects.