Synthetic Data Statistics 2024 – Everything You Need to Know

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Are you looking to add Synthetic Data to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Synthetic Data statistics of 2024.

My team and I scanned the entire web and collected all the most useful Synthetic Data stats on this page. You don’t need to check any other resource on the web for any Synthetic Data statistics. All are here only 🙂

How much of an impact will Synthetic Data have on your day-to-day? or the day-to-day of your business? Should you invest in Synthetic Data? We will answer all your Synthetic Data related questions here.

Please read the page carefully and don’t miss any word. 🙂

On this page, you’ll learn about the following:

Best Synthetic Data Statistics

☰ Use “CTRL+F” to quickly find statistics. There are total 40 Synthetic Data Statistics on this page 🙂

Synthetic Data Latest Statistics

  • Test data managementwhich is expected to grow12.7% CAGR AI training data generation, which is expected to grow at22.5% CAGR. [0]
  • Gartner estimates that by 2024, 60% of the data used for the development of AI and analytics projects will be synthetically generated. [0]
  • Mostly AI claims that synthetic data can retain 99% of the information and value of the original dataset while protecting sensitive data from re. [0]
  • “Companies only need 50% of their original, authentic training data to finish the formal training of their algorithms”, claims Yashar Behzadi, CEO of Neuromation, a synthetic data generation startup. [0]
  • When training data is highly imbalanced (e.g. more than 99% instances belong to one class). [0]
  • Another important function of synthetic data is to keep data secure as17% of the global online population were victims of digital theft over the last few decades and it is estimated that 80% of the cybercrimes are not reported. [0]
  • Vendor claims include 80% of credit card owners can be re identified from 3 transactions whentraditional anonymization techniquesare used. [0]
  • 51% of mobile phone owners can be re identified by 2 antenna signals when traditional anonymization techniques are used. [0]
  • 87% of all people can be re identified by their birthday, gender and postcode when traditional anonymization techniques are used. [0]
  • A team at Deloitte Consulting generated 80% of the training data to be used in a model by synthesizing data and model accuracy was similar to a model trained on real data. [0]
  • A 2020 study shows that using synthetic data improved the machine learning model performance up to 20% while categorizing actions in videos. [0]
  • Researchers were able to identify drivers of cars with 87% accuracy by analyzing synthesized sensor data generated by vehicles. [0]
  • A study conducted in 2017 showed that 70% of the time predictive models built with real data and synthetic data generated results on par. [0]
  • A 2018 study depicts that using synthetic data reduces the false positive rates from 60% to 20% while predicting volcanic eruptions. [0]
  • It’s estimated that by 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated. [1]
  • 70% of the time group using synthetic data was able to produce results on par with the group using real data. [1]
  • This analysis in the original dataset suggested no significant difference in spirituality ratings between the nasal spray groups [t = 1.14, 95% CI , p=0.26]. [2]
  • A comparison of confidence intervals revealed 99.94% CI overlap between the synthetic and original datasets. [2]
  • A Pearson correlation test revealed no statistically significant correlation between age and self reported spirituality [r = 0.04, 95% CI , p=0.75], which is the same result as the linear model equivalent. [2]
  • The test of the standardized differences between the synthetic and original data for the nasal spray coefficient was not statistically significant and there was 87.8% overlap between the synthetic and observed data. [2]
  • The test of the standardized differences between the synthetic and original data for the religious affiliation coefficient was not statistically significant and there was 82.3% overlap between the synthetic and observed data. [2]
  • A comparison of confidence intervals revealed 91.8% CI overlap between the synthetic and original datasets. [2]
  • A comparison of confidence intervals revealed 92.9% CI overlap between the synthetic and original datasets. [2]
  • A comparison of confidence intervals revealed 72.4% CI overlap between the synthetic and original datasets. [2]
  • A comparison of the original and synthetic datasets revealed 213 replicated individuals (2.3% of the total sample). [2]
  • CI overlap = 92.4%; age standardized difference =. [2]
  • −0.74, p=0.46, CI overlap = 81.2%; location standardized difference = 0.95, p=0.34. [2]
  • Perhaps, this cannot be predicted in advance, but it would be interesting to test this with datasets that may have the kinds of characteristics that are noted in point 1. [2]
  • High skew and no missing data High skew and 5% missing data. [2]
  • For example, the model generated for the synthetic version of the dataset with 100 cases, HRV data with a low skew, and 5% missing data overall only had a 60.5% confidence interval overlap with the model generated from the observed data. [2]
  • The degree of missingness (none, 5%, or 20%). [2]
  • Meta analysed IVW MR findings suggested that higher GrimAge acceleration increased the risk of colorectal cancer (OR = 1.12 per year increase in GrimAge acceleration, 95% CI 1.04–1.20, p = 0.002). [2]
  • Among subtypes, the genetically predicted effect of GrimAge acceleration was greater for colon cancer (IVW OR = 1.15, 95% CI 1.09–1.21, p = 0.006), than rectal cancer (IVW OR = 1.05, 95% CI 0.97–1.13, p = 0.24). [2]
  • Among controls, the percentage of ILCs that produced amphiregulin was higher in females than in males, and people hospitalized with COVID 19 had a lower percentage of ILCs that produced amphiregulin than did controls. [2]
  • When evaluating synthetic continuous data, owners may want to compare medians means quartiles or other quantiles outliers or values at the extremes of the distribution (for example, top or bottom 5% of values). [3]
  • For example, observed survey data gives a population estimate for an area of 500,000, with the lower bound of the 95% confidence interval being 490,000 and the upper bound being 510,000. [3]
  • A point estimate from the synthetic data in the range 490,000 to 510,000 would be sufficient on the basis of 95% confidence intervals. [3]
  • Again, this can be done in either absolute or relative terms, or the degree of overlap in the 95% confidence intervals can be examined. [3]
  • He’s rallying support for a benchmark or competition on data quality which many claim represents 80 percent of the work in AI. [4]
  • a June 2021 report on synthetic data, Gartner predicted by 2030 most of the data used in AI will be artificially generated by rules, statistical models, simulations or other techniques. [4]

I know you want to use Synthetic Data Software, thus we made this list of best Synthetic Data Software. We also wrote about how to learn Synthetic Data Software and how to install Synthetic Data Software. Recently we wrote how to uninstall Synthetic Data Software for newbie users. Don’t forgot to check latest Synthetic Data statistics of 2024.

Reference

  1. aimultiple – https://research.aimultiple.com/synthetic-data-stats/.
  2. aimultiple – https://research.aimultiple.com/synthetic-data/.
  3. elifesciences – https://elifesciences.org/articles/53275.
  4. ons – https://www.ons.gov.uk/methodology/methodologicalpublications/generalmethodology/onsworkingpaperseries/onsmethodologyworkingpaperseriesnumber16syntheticdatapilot.
  5. nvidia – https://blogs.nvidia.com/blog/2021/06/08/what-is-synthetic-data/.

How Useful is Synthetic Data

Synthetic data is essentially artificially created data that mirrors the statistical properties of real data without exposing any personally identifiable information. This allows for testing algorithms, models, and applications without risking the privacy of individuals. By generating data that is statistically similar to real data, organizations can still run simulations and analyses while keeping sensitive information safe from prying eyes.

One of the key benefits of synthetic data is its versatility. It can be used in a wide range of applications, from training machine learning models to testing new software systems. This flexibility allows organizations to explore innovative solutions and technologies without the fear of compromising data privacy. Synthetic data also allows for greater collaboration between companies, researchers, and organizations, as they can share datasets without divulging confidential information.

Another advantage of synthetic data is its ability to address data scarcity issues. Oftentimes, real-world data can be limited in size or scope, making it difficult to develop robust models or algorithms. Synthetic data can help supplement these datasets, providing additional data points and variations that can enhance the accuracy and effectiveness of data-driven models. This can be particularly beneficial in industries such as healthcare, where access to large and diverse datasets can be challenging.

Moreover, synthetic data can be a useful tool in addressing bias and fairness issues in data analysis. Biases inherent in real data can be reflected in machine learning models and algorithms, leading to skewed results and discriminatory outcomes. By generating a diverse set of synthetic data, organizations can mitigate these biases and ensure that their analyses are more accurate and impartial. This can be particularly important in areas such as lending, hiring, and criminal justice, where data-driven decisions can have profound implications for individuals.

Despite its many advantages, synthetic data is not without its limitations. While it can mimic the statistical properties of real data, it may not fully capture the nuances and complexities of actual data sources. This can lead to discrepancies in performance when using synthetic data in real-world scenarios. Organizations must be mindful of these limitations and carefully validate the effectiveness of synthetic data before making important decisions based on its results.

In conclusion, synthetic data is a valuable tool that can provide organizations with the means to analyze data securely, supplement existing datasets, address bias issues, and explore new solutions without compromising privacy. By leveraging the power of synthetic data, organizations can unlock valuable insights and drive innovation in a data-driven world.

In Conclusion

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