Data quality is crucial for successful data-driven decision-making processes. However, companies struggle with improving their data quality and keeping it on a high level. This difficulty increases with the size of an organization, the complexity of tracking requirements and the up-to-dateness of an IT-infrastructure. This talk takes a journey through the data analytics lifecycle from tracking design, over integration to aggregation, analysis, and interpretation. On this way, it emphasizes on common data quality issues and how to avoid or improve them in existing productive systems. Overall, the talk aims at providing best practices for investigating and improving data quality by showing common real-world scenarios and hands-on recommendations.