GITNUXREPORT 2025

Data Quality Statistics

Poor data quality causes major project failures and financial losses annually.

Jannik Linder

Co-Founder of Gitnux, specialized in content and tech since 2016.

First published: April 29, 2025

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Statistic 1

45% of organizations report not having sufficient resources to improve data quality

Statistic 2

50% of organizations lack a formal data quality strategy

Statistic 3

78% of organizations see an ROI within 12 months of implementing data quality improvements

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92% of data quality issues are preventable through proper governance

Statistic 5

55% of organizations do not conduct regular data quality assessments

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80% of data-related issues can be prevented with proper training and processes

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Companies with strong data quality practices are 2.5 times more likely to outperform their competitors financially

Statistic 8

25% of big data projects fail due to poor data quality

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60% of all business data is inaccurate

Statistic 10

Poor data quality costs US businesses over $3 trillion annually

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75% of organizations report that data quality issues impact their decision-making

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Data cleansing can improve data quality by up to 95%

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80% of data scientists spend 80% of their time cleaning and preparing data

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Data quality issues cause 40% of all failed enterprise projects

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Organizations with high data quality are five times more likely to make faster decisions

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Data quality problems lead to 10% or more of organizational costs

Statistic 17

59% of data failures are due to poor data quality

Statistic 18

Implementing data quality controls can reduce error rates by up to 80%

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65% of enterprises experience data quality issues in their big data initiatives

Statistic 20

Data quality issues result in an average loss of 10% revenue for organizations annually

Statistic 21

Improving data quality can increase customer satisfaction by 20%

Statistic 22

Data quality issues are responsible for 25% of data breaches

Statistic 23

Regular data quality audits can improve data accuracy by 98%

Statistic 24

Data quality issues cause an estimated 15-20% of operational costs annually

Statistic 25

Having a data stewardship program increases data quality scores by up to 30%

Statistic 26

85% of data analysts report wasting time on data cleaning

Statistic 27

Data validation can reduce data errors by 70%

Statistic 28

Enhancing data quality leads to better regulatory compliance in 75% of cases

Statistic 29

78% of organizations say that inaccurate data impacts their business operations

Statistic 30

Data quality improvements can lead to 25% faster decision-making

Statistic 31

Human error is responsible for 60% of data quality issues

Statistic 32

Data quality scores directly correlate with customer retention rates, with higher scores leading to up to 15% retention increase

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Data duplication accounts for 20-30% of all data errors

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90% of companies consider data quality a top priority

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70% of business users say they don't trust their data

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81% of organizations believe that improving data quality is critical to their digital transformation

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35% of data quality initiatives fail due to lack of executive support

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66% of organizations feel unprepared to address data quality issues

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72% of organizations believe that improving data quality will help meet compliance standards

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Data profiling tools help identify 80% of data anomalies during initial analysis

Statistic 41

67% of companies are planning to invest more in data quality tools in the next year

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Key Highlights

25% of big data projects fail due to poor data quality
90% of companies consider data quality a top priority
60% of all business data is inaccurate
Poor data quality costs US businesses over $3 trillion annually
75% of organizations report that data quality issues impact their decision-making
Data cleansing can improve data quality by up to 95%
80% of data scientists spend 80% of their time cleaning and preparing data
Data quality issues cause 40% of all failed enterprise projects
Organizations with high data quality are five times more likely to make faster decisions
50% of organizations lack a formal data quality strategy
Data quality problems lead to 10% or more of organizational costs
59% of data failures are due to poor data quality
70% of business users say they don't trust their data

Did you know that poor data quality costs U.S. businesses over $3 trillion annually and is responsible for 25% of data breaches, yet 70% of organizations still lack a formal strategy to address this critical issue?

Challenges, Failures, and Improvement Initiatives

45% of organizations report not having sufficient resources to improve data quality

Challenges, Failures, and Improvement Initiatives Interpretation

Nearly half of organizations are navigating the data waters with a leaky boat, highlighting that without adequate resources, even the best data strategies risk sinking before they set sail.

Data Management Practices and Strategies

50% of organizations lack a formal data quality strategy
78% of organizations see an ROI within 12 months of implementing data quality improvements
92% of data quality issues are preventable through proper governance
55% of organizations do not conduct regular data quality assessments
80% of data-related issues can be prevented with proper training and processes

Data Management Practices and Strategies Interpretation

Despite over half of organizations missing a formal data quality strategy and a significant portion neglecting regular assessments, the compelling ROI and preventability of most issues underscore that investing in governance, training, and systematic evaluation isn't just wise—it's essential for turning data from a liability into a strategic asset.

Data Quality Impact

Companies with strong data quality practices are 2.5 times more likely to outperform their competitors financially

Data Quality Impact Interpretation

Investing in robust data quality practices isn’t just good housekeeping—it's a strategic move that can propel your company to financial outperformers, essentially turning clean data into a competitive advantage.

Data Quality Impact and Cost

25% of big data projects fail due to poor data quality
60% of all business data is inaccurate
Poor data quality costs US businesses over $3 trillion annually
75% of organizations report that data quality issues impact their decision-making
Data cleansing can improve data quality by up to 95%
80% of data scientists spend 80% of their time cleaning and preparing data
Data quality issues cause 40% of all failed enterprise projects
Organizations with high data quality are five times more likely to make faster decisions
Data quality problems lead to 10% or more of organizational costs
59% of data failures are due to poor data quality
Implementing data quality controls can reduce error rates by up to 80%
65% of enterprises experience data quality issues in their big data initiatives
Data quality issues result in an average loss of 10% revenue for organizations annually
Improving data quality can increase customer satisfaction by 20%
Data quality issues are responsible for 25% of data breaches
Regular data quality audits can improve data accuracy by 98%
Data quality issues cause an estimated 15-20% of operational costs annually
Having a data stewardship program increases data quality scores by up to 30%
85% of data analysts report wasting time on data cleaning
Data validation can reduce data errors by 70%
Enhancing data quality leads to better regulatory compliance in 75% of cases
78% of organizations say that inaccurate data impacts their business operations
Data quality improvements can lead to 25% faster decision-making
Human error is responsible for 60% of data quality issues
Data quality scores directly correlate with customer retention rates, with higher scores leading to up to 15% retention increase
Data duplication accounts for 20-30% of all data errors

Data Quality Impact and Cost Interpretation

Despite heavy investments and technological advances, poor data quality continues to silently drain over $3 trillion annually, bogging down decision-making, inflating costs, and eroding customer trust—proving that in the realm of big data, it's not just about collecting more but ensuring what you have is truly worth the effort.

Organizational Perceptions and Trust

90% of companies consider data quality a top priority
70% of business users say they don't trust their data
81% of organizations believe that improving data quality is critical to their digital transformation
35% of data quality initiatives fail due to lack of executive support
66% of organizations feel unprepared to address data quality issues
72% of organizations believe that improving data quality will help meet compliance standards

Organizational Perceptions and Trust Interpretation

Despite nearly universal recognition of data quality’s importance, a troubling trust gap persists, with over two-thirds feeling unprepared and over a third of initiatives failing due to lack of executive support, underscoring that true digital transformation hinges on translating top priorities into tangible action.

Technologies and Tools for Data Quality

Data profiling tools help identify 80% of data anomalies during initial analysis
67% of companies are planning to invest more in data quality tools in the next year

Technologies and Tools for Data Quality Interpretation

With data profiling tools catching 80% of anomalies early on and over two-thirds of companies planning to boost their investments, organizations are increasingly recognizing that in the data game, prevention truly is better than remediation.