Conclusion
In this chapter, we explored the fundamental concept of data quality and its critical role in driving informed decision-making. We established a framework for understanding the various dimensions of data quality, including accuracy, completeness, consistency, and timeliness. We further delved into different sources of data errors and imperfections, highlighting the importance of proactive data quality management practices. You should now have a firm understanding of the following areas:
- The importance of data quality, including why it is required for building trust in data.
- How to define a data quality strategy and framework
- Specific needs when it comes to implementation
- How to design a data quality solution with impact
- How to set data quality up for success in your company
By prioritizing data quality, organizations can unlock the true potential of their information assets. Clean, consistent, and reliable data empowers effective analytics...
