Data Quality

• Profile for data quality, including the appropriate techniques, criteria, and KPIs
• Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.
• Formulate the reference architecture for data quality, including practical design patterns for remediating data quality
• Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business

An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.