Data Quality and its Usage in the AI and LLM Era
Data quality refers to the overall utility of data based on attributes such as accuracy, completeness, reliability, relevance, and timeliness. It is an assessment of how well-suited data is for making decisions, driving processes, and achieving business objectives. The importance of data quality stems from its impact on the operational efficiency, strategic planning, and decision-making capabilities of an organization. High-quality data is critical for several practical reasons.
In this chapter, we present the data quality and its usage in the AI and Large Language Model (LLM) era. We also show practical use cases and features of the data in real applications.
The key topics covered in this chapter are as follows:
- Data quality and its usage
- Characterizing good data quality
- Examples of poor data quality and accidents
- Practicing with Python code
