Highlights from our Synthetic Data quality report
Case study: the Census Income Database (1994)
This case study demonstrates highlights from our quality report containing various statistics from synthetic data generated through our Syntho Engine in comparison to the original data. We practice with the publicly available income dataset from the Census Income Database (1994). This dataset contains various personal attributes (age, education, relationship etc.) enriched with income-related data (hours-per-week, income etc.). The various categories from this original dataset supplemented with example data records are found in the table below.
The original Census Income Database (1994)
Example data records
An introduction to our Synthetic Data quality report
Syntho generates a quality report for every generated synthetic dataset. Our quality report contains various common statistics such as averages and distributions, enriched with more advanced statistics, such as correlations and multivariate distributions.
This case study offers a sneak preview in the full version of the quality report, since the full statistical report covers tens of pages of descriptive statistics. The full version of the quality report for the case study with the Census Income Database (1994) is available upon request via the form at the bottom of this page.
Synthetic Data quality report: descriptive statistics
Part of the quality report is a table containing summary statistics of the original dataset in comparison to the synthetic dataset generated by Syntho. This table contains descriptive statistics such as averages, standard deviations, minima, maxima, and correlations. As can be seen in the table below, summary statistics for original data (left) and synthetic data (right) are nearly identical. Note that we are able to tailor the required amount of data to fulfil the goals of your use case.
Original data: left – synthetic data: right
Synthetic Data quality report: univariate distributions
Univariate distributions give insight in the frequency of a certain data record for a given category or value. When displayed in a graph, one can observe the frequency of all data records for a certain value, which provides insight in the distribution. As can be seen in the following graphs, the distribution for original data (grey) and synthetic data (blue) are nearly identical for the categories age (continuous variable) and workclass (categorial variable).
Original data: grey – synthetic data: blue
Synthetic Data quality report: correlations
Correlations provide insight in the degree to which two variables are related. Displayed in a matrix, one can easily observe correlations for each variable combination. As can be seen in the following matrices, the matrix for original data (grey) and the matrix for synthetic data (blue) are nearly identical for all combinations of categories from the Census Income Database (1994).
Original data: left in grey – synthetic data: right in blue
Synthetic Data quality report: multivariate distributions and correlations
Where univariate distributions and correlations provide insight in the distribution and relationship for single categories, multivariate distributions provide insight for combinations of categories and are also captured by the Syntho Engine.
As an example, we provide you with the result of the bivariate distribution that provides insight in the frequency of a combination of data records for two given categories. When displayed in a matrix, one can observe the frequency of all possible combinations of data records for two categories, which provides insight in the bivariate distribution. As can be seen in the following matrices, the matrix for original data (grey) and the matrix for synthetic data (blue) are nearly identical for all possible combinations of data records for the two categories age and education.
Original data: left in grey – synthetic data: right in bluecq
Since multivariate correlations and distributions can be formed as endless combinations, they result in numerous graphs for potential analysis. As illustrated, we only provide the bivariate distribution between age and education in this shortened version of the quality report.
Synthetic Data quality report: deep ‘hidden’ relations
The Syntho Engine also captures deeper, ‘hidden’ relations, other than multivariate correlations and bivariate distributions. We evaluate these by a build in mechanism in our Syntho Engine that evaluates the accuracy of a machine learning model that is trained to distinguish between real and synthetic data. Experiments show that even machine learning models can barely distinguish original data from synthetic data. These outcomes are not only one of the interesting elements in our quality report, these are also the key performance measure that Syntho uses for optimization of the Syntho Engine.
As all deep ‘hidden’ relations of the data are retained within synthetic data, it can be used as a full replacement for the original data in machine learnings tasks. As an example we will use the Census dataset again, let’s say you want to predict the income of an individual given its other characteristic. Now if we test the accuracy on a separately hold test set for two machine learning models, one trained on the original data and the other trained on the synthetic data, an accuracy loss of less of than 2% is observed. This shows that synthetic data retains statistical properties to such an extent that, even in machine learning tasks, it provides a viable alternative for real data.
Explore the added value of synthetic data with Syntho
Getting familiar with the potential added value of synthetic data works best when you actually work with a synthetic version of your own original dataset. To realize this, Syntho offers the opportunity for a pilot. This pilot leads to a full synthetic version of your original dataset, supplied with a quality report and tailor-made dashboard. Consequently, you are able to fully explore the added value of synthetic data with us.
Key pilot deliverables
Fully synthetic and anonymous dataset that preserves the value of your original dataset
Quality report demonstrating the statistical significance of your synthetic dataset
Hands-on experience with synthetic data as a best practice for privacy-by-design