Data Quality Strategies: How to Assess The Effectiveness of Data Quality Techniques

Work In Progress

How to Assess Data Quality Strategies

Look at the complete IT picture, not just the narrow scope promoted by the traditional data management community. Three categories of risk, financial, complexity, and cultural. The cultural risks are particularly important because those are what will often determine the ultimate success of a data quality program. Sadly, they are often ignored.

The following risk factors are important when comparing data quality strategies:

  1. Timing. Proactive vs. reactive
  2. Payback period.
  3. Cost. Initial investment and ongoing.
  4. Level of automation.
  5. Organizational complexity.
  6. Technical complexity.
  7. Cultural alignment with development teams.
  8. Cultural alignment with data professionals.

Related Resources

Recommended Reading

Choose Your WoW! 2nd Edition
This book, Choose Your WoW! A Disciplined Agile Approach to Optimizing Your Way of Working (WoW) – Second Edition, is an indispensable guide for agile coaches and practitioners. It overviews key aspects of the Disciplined Agile® (DA™) tool kit. Hundreds of organizations around the world have already benefited from DA, which is the only comprehensive tool kit available for guidance on building high-performance agile teams and optimizing your WoW. As a hybrid of the leading agile, lean, and traditional approaches, DA provides hundreds of strategies to help you make better decisions within your agile teams, balancing self-organization with the realities and constraints of your unique enterprise context.


I also maintain an agile database books page which overviews many books you will find interesting.