Summary

In this chapter, we explored factors that affect data quality and why data cleaning is crucial in the data preparation process. We discussed the importance of understanding data quality standards and the impact of data accuracy, completeness, consistency, validity, and timeliness on analyses and decision-making. We also identified common sources of data quality issues, such as data entry errors, incomplete or missing data, data integration challenges, data transformation and manipulation, data storage and transfer issues, data governance and documentation gaps, data changes and updates, and external data sources.

Furthermore, we delved into why data cleaning is everyone’s responsibility within a company. By recognizing data cleaning as a shared responsibility, individuals can contribute to data integrity, decision-making, and a holistic view of the data ecosystem. We highlighted the benefits of the early detection of data issues, continuous improvement, empowerment...