Key Takeaways
- 42% of respondents reported using a cloud data warehouse for analytics at least monthly in 2023
- 55% of organizations were using cloud data analytics services in 2023
- 78% of organizations reported that data integration is a top investment priority in 2024
- In 2023, 27% of organizations reported using serverless platforms for data processing
- 2.7x average faster query performance was reported when using materialized views versus non-materialized approaches in a 2022 study of cloud analytics
- Up to 10x faster ingestion throughput was reported for vectorized execution compared with row-by-row processing in a 2021 database performance study
- The global cloud data warehouse market was valued at $5.1 billion in 2023
- The global data integration market size was $9.3 billion in 2023
- The global data quality software market was $4.2 billion in 2023
- 79% of organizations reported using or planning to use ML-enabled data platforms by 2024
- According to a 2022 survey, 88% of organizations reported that data downtime or data quality issues negatively impacted business outcomes
- The 2024 Verizon DBIR reported that 73% of breaches involved human element (phishing, social engineering, etc.), increasing emphasis on secure data handling
- The IBM 2024 report states the average time to contain a breach was 73 days (median) globally
- In 2023, reducing data outages improved performance; Gartner estimated that for some organizations, preventing downtime saves between $250,000 and $1 million per hour (reported in Gartner outage cost discussions)
- Google Cloud’s BigQuery pricing guidance shows query costs are based on bytes processed, with on-demand pricing ($5 per TB processed as listed in documentation for region-independent public on-demand pricing)
Data integration, governance, and automated quality tools are rapidly scaling, boosting performance and reliability across cloud analytics.
Related reading
Market Adoption
Market Adoption Interpretation
Performance Metrics
Performance Metrics Interpretation
More related reading
Market Size
Market Size Interpretation
Industry Trends
Industry Trends Interpretation
More related reading
Cost Analysis
Cost Analysis Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Timothy Grant. (2026, February 13). Dat Statistics. Gitnux. https://gitnux.org/dat-statistics
Timothy Grant. "Dat Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/dat-statistics.
Timothy Grant. 2026. "Dat Statistics." Gitnux. https://gitnux.org/dat-statistics.
References
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- 3gartner.com/en/newsroom/press-releases/2024-02-19-gartner-reveals-top-data-and-analytics-priorities-for-2024
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- 7gartner.com/en/newsroom/press-releases/2024-04-17-gartner-says-73-percent-of-organizations-will-have-established-data-governance-by-2025
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