Estimate what bad data costs your business each year. Enter your headcount and industry to see what poor data quality likely costs your organization, based on DoubleTrack's research of 8.36 million U.S. businesses.
Your estimate is your number of employees multiplied by a cost per employee for your industry. The rates come from DoubleTrack’s research on what poor data quality costs the U.S. economy.
Estimated annual cost = number of employees × cost per employee for your industry
The cost per employee starts from a baseline of $4,912. Gartner’s 2020 survey of 154 large enterprise customers found that poor data quality costs an average of $12.9 million per year. U.S. Census Bureau data shows that businesses with 1,000 or more employees average 2,626 employees per establishment, so $12.9 million divided by 2,626 gives the $4,912 baseline per employee.
That baseline is then adjusted by an industry multiplier. Flexera’s 2020 State of Tech Spend Report measures IT spending as a percentage of revenue by industry. Each industry’s share is divided by the 8.2% weighted average, so industries that spend more on IT carry a higher multiplier. Industries that Flexera does not cover use a 1.00× multiplier, which leaves the baseline unchanged.
Across the whole economy, the adjusted rates add up to $617 billion a year, or about $4,400 per employee. That national average is lower than the $4,912 baseline because several large industries, including healthcare, retail and manufacturing, have multipliers below 1.00×.
The research applies the per-employee baseline to every business size in the Census data. This view shows the unadjusted baseline, with no industry multipliers. Adjusting for industry mix brings the national total to $617 billion, or about $4,400 per employee.
Short answers about what bad data costs, how this calculator works, and how much to trust the estimate.
DoubleTrack’s research estimates that bad data costs the U.S. economy $617 billion a year, roughly 2% of GDP. That works out to about $4,400 per employee on average, ranging from about $3,000 to more than $12,000 per employee depending on the industry.
It multiplies your number of employees by a cost per employee for your industry. The rate starts from a $4,912 baseline, which is Gartner’s $12.9 million average annual cost of poor data quality divided by 2,626 employees, and is then adjusted by an industry multiplier based on IT spending intensity.
Treat it as a way to see scale, not a precise measurement of your costs. The Gartner baseline comes from large enterprises that were already buying data quality software, so it is not a representative sample and smaller businesses may see different cost profiles. The industry multipliers also assume that IT spending intensity tracks exposure to bad data, which is reasonable but unverified.
Data-intensive industries have more exposure. The Information sector, which covers software and technology hosting, has the highest cost at $12,161 per employee, about 2.8 times the national average. Healthcare and manufacturing are the lowest at $2,995 per employee.
The $4,912 baseline applies before any industry adjustment, and industries without IT-spend data use it as is. Several large industries, including healthcare, retail and manufacturing, have multipliers below 1.00×, which pulls the blended national average down to about $4,400 per employee.
The clearest signs are operational: teams reconcile conflicting reports, sales and finance cannot agree on the same number, onboarding a new system requires months of cleanup, or your team does not trust what analytics or AI tools produce. A good first step is the free 844 Data Snapshot, a 3-minute readout of your Customer data against DoubleTrack’s 4 Rs framework.
Source study. This calculator applies the method from DoubleTrack’s research on the cost of dirty data across the U.S. economy. Read the full study.
Baseline. Gartner’s Magic Quadrant for Data Quality Solutions (July 2020) reports a survey of 154 reference customers across 16 data quality vendors, who estimated the cost of poor data quality at $12.9 million per year. U.S. Census Bureau County Business Patterns data (2023) shows that businesses with 1,000 or more employees average 2,626 employees per establishment, which the study uses as a proxy for the enterprises Gartner surveyed. Dividing $12.9 million by 2,626 yields the $4,912 baseline.
Industry multipliers. Flexera’s 2020 State of Tech Spend Report provides IT spending as a percentage of revenue by industry. Each industry’s percentage is divided by the 8.2% weighted average to produce its multiplier. Software companies spend 24.7% of revenue on IT, for example, which yields 3.01×. Averaging that with Technology Hosting (1.94×) gives the Information sector multiplier of 2.48×. Industries without Flexera coverage use 1.00×.
National total. Applying the baseline and multipliers to the 139.8 million employees across 8.36 million U.S. establishments in the Census data yields $617 billion a year, or about $4,400 per employee. Measured against U.S. GDP of $31.1 trillion (U.S. Bureau of Economic Analysis, Q3 2025), that is roughly 2%.
Growth assumption. The study draws on IDC’s estimate that the volume of data stored globally doubles about every four years, and assumes that annual costs from dirty data double at the same rate.
Limitations. The Gartner baseline comes from large enterprises that were already investing in data quality solutions, so it is not a representative sample, and smaller businesses may have different cost profiles. Only the Census employment data is representative of the whole economy. The multipliers assume that IT spending intensity correlates with the cost of bad data, which is reasonable but unverified, and industries without Flexera coverage may be understated or overstated. All figures are estimates meant to illustrate scale, not precise measurements of what your organization loses.
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