Impact of poor data quality: is it accurate that data quality problems cost U.S. businesses only about $60 million per year (as cited in 2003), or is that figure significantly understated?

Introduction / Context:
Data quality defects—duplicates, missing values, inconsistencies, and stale records—impose real costs: wasted marketing spend, operational rework, compliance risk, and lost revenue. This question evaluates whether a figure of approximately 60 million USD per year for all U.S. businesses meaningfully reflects the scope of the problem.

Given Data / Assumptions:

• The figure cited is extremely low for an economy-wide estimate.
• Data quality costs include direct (cleansing, tool licenses, staffing) and indirect (opportunity loss, reputational harm) components.
• Industry studies commonly report orders of magnitude higher impacts.

Concept / Approach:
Consider the breadth of data quality touchpoints across finance, healthcare, retail, manufacturing, and the public sector. Even modest per-organization costs aggregated over millions of businesses would exceed 60 million USD. Additionally, indirect impacts (for example, churn due to inaccurate billing) dwarf simple cleanup expenses. Therefore, asserting a nationwide cost of only 60 million USD per year is not credible and grossly understates the problem magnitude.

Step-by-Step Solution:

Verification / Alternative check:
Compare with widely cited research from industry groups and analysts that report far higher national totals; review case studies where a single enterprise spends many millions annually on data quality initiatives.

Why Other Options Are Wrong:

• Correct: Accepts an implausibly low number.
• Small business/“direct costs only” carve-outs cannot reconcile the huge gap between real-world totals and 60 million USD.
• “Cannot be estimated” is defeatist; while exact amounts vary, the order of magnitude is known to be far larger.

Common Pitfalls:
Confusing a narrow-scope estimate with a national total; ignoring indirect costs and risk exposure; relying on outdated or misquoted figures.

Final Answer:
Incorrect