Data Quality 2.0: From Scoring To Data Reliability Engineering
📰 Forbes Innovation
Learn how Data Quality 2.0 shifts focus from scoring to data reliability engineering for better business outcomes
Action Steps
- Assess current data scoring methods
- Identify gaps in data system testing and operation
- Apply data reliability engineering principles
- Evaluate data quality in relation to business needs
- Implement changes to improve data system reliability
Who Needs to Know This
Data scientists, engineers, and product managers can benefit from understanding Data Quality 2.0 to improve data systems and meet business needs
Key Insight
💡 Data scoring only shows outcomes, not how data systems are built, tested, and operated
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📈 Move beyond data scoring to Data Quality 2.0: focus on reliability engineering for better business outcomes
Key Takeaways
Learn how Data Quality 2.0 shifts focus from scoring to data reliability engineering for better business outcomes
Full Article
Scores show outcomes, but they don’t reveal how a data system is built, tested and operated, or whether the data meets the needs of the business.
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