This is a summary of the text about data quality tools. Data quality is becoming increasingly important, and having the right tools in your stack can help you manage it effectively. The key to choosing the right tool is to focus on addressing specific pain points, considering your existing tech stack, team capabilities, scalability needs, and budget constraints. There are different types of data quality tools, including discovery, testing, cleansing and standardization, observability, governance, and remediation/enrichment tools. Each category addresses a specific aspect of the quality challenge. The best tool for you will depend on your unique needs, but investing in data quality tools can help build confidence in your decision-making process based on trusted data. Some key considerations when choosing a data quality tool include whether it is open-source or commercial, its scalability and integration capabilities, and whether it offers automation and monitoring features.