Planning for data quality at a business scale means navigating a tricky pair of truths:

• A data quality plan can break down if it’s based on assumptions that turn out to be incorrect.

• To avoid assumptions, the data must be parsed, explored, and understood… which would mean starting the data quality process as a prerequisite of planning for data quality.

But the worst effects of this catch-22 can be prevented.

With trailblazer data quality, you can launch data quality in a sustainable, realistic, and targeted way: a way that’s rooted in fact and experience, not assumptions and suppositions...