The cost of bad data is high – from faulty insights, poor business decisions and lost sales to an annual cost of $3.1 trillion for U.S. companies alone. Avoiding major pitfalls of bad data comes down to how your organization approaches data quality. But since most data is complex and fluid, not knowing where to begin or what type of solution is best, has become a common issue.
Consider this guiding principle: You can’t determine the right solution until you understand the relationship between rules-based data quality (where internal subject matter knowledge is necessary) and active data quality (things that are constantly changing outside the organization). This is in the critical zone indicating when data quality is best addressed, whether in house, or with off-the-shelf solutions, or by a combination of both.