Mindblown: a blog about philosophy.

Dive into the data quality hierarchy, understanding that building systems producing high-quality data is better than fixing the source data after the fact, transforming data during extraction, cleansing data at the point of usage, or working with poor quality data. #DataQualityHierarchy #DataEngineering

Explore strategies to stop generating poor quality data, including developing developers’ data engineering skills, supporting application development teams, and allowing for adequate resources. Discover how building systems that produce high-quality data is the ultimate goal. #StopPoorQualityData #DataEngineeringStrategies

Learn how to address the root cause of poor quality data in a data source. Understand the importance of identifying quality problems, determining their sources, defining a desired state, and prioritizing solutions. #DataQualityProblems #RootCause

Explore the most effective strategy for handling data technical debt: fixing data issues at the source. Learn why preventing poor quality data is the ideal approach and discover the root causes of data quality problems. #PreventingDataIssues #DataEngineering

Delve into the difficulties of fixing source data, including database refactoring and data repair. Understand the roadblocks involving ownership, skills, tools, and resource allocation. #FixingSourceData #DataQualityFix

Learn about the efficiency of data cleansing within data warehouse solutions and the advantages of encapsulating database access for high-quality data. Explore the challenges these approaches face. #DataCleansingEfficiency #ETL

Discover why relying solely on data cleansing efforts is ineffective. Understand the problems of redundant cleansing and how it amplifies technical debt within organizations. #DataCleansingStrategies #TechnicalDebt

Explore why data cleansing is crucial to combat data quality issues and how it impacts data-driven decision-making, software development, and more. Learn about the challenges it poses. #DataCleansing #DataQuality

Dive into advanced anonymization methods, including differential privacy and k-anonymization. Enhance data security and privacy for AI applications. #Anonymization #DataSecurity