Synthetic Data for development & analytics
Modern organizations encounter data-related challenges such as privacy concerns and limited data diversity, which can significantly impede their ability to develop effective decision-making and growth strategies in two key areas:
Distributed Teams:
The ability to leverage organizationally and geographically separated teams for data-engineering or model development, is significantly impacted due to contractual and regulatory concerns related to sharing access to consumer or business data.
ML (Machine Learning) Models:
Machine learning relies heavily on accurate, diverse, and complete data to produce reliable models. Apart from privacy concerns, lack of comprehensive data affects areas such as outlier detection, bias removal, and minority-class handling.
What is Synthetic Data?
Most teams have used ad-hoc methods such as data-obfuscation towards enabling much needed operations around sensitive data. These techniques have evolved into a more organized discipline referred to as Synthetic Data Management that addresses specific problems such as the following:
Compliance:
Very often for compliance to different specifications like GDPR, HIPPA, CCPA we need to remove any reference to PII data elements such as names and social security numbers. Using synthetic over data obsfusctation with any replacement method is more reliable as it completely obliterates any risk of
tracing back to original person. as well as generates proper and realistic replacement PII which performs better for downstream automated and human processes
Backward Traceability
Even if PII has been obfuscated, in some cases, such as those of outliers in finance and health data, the information can be traced to specific subjects. A more comprehensive approach finds and modifies or removes such outliers without affecting data utility.
Parallel Data
When restrictions prevent any part of the data from being shared, Synthetic Data approaches can be deployed to create a parallel set that mimic not just the structure but also implicit all traits, utilizing statistical analysis such as mean and standard deviation, as well as correlation and factor analysis across
data attributes.
Data Augmentation
When data is scarce, synthetic techniques may be deployed to supplement augment or impute new data, to remove problems such as lack of diversity, class imbalance, and bias. Specific techniques may be deployed for generating, for example, time-series or sequential data vs static data.
Data Reduction
Working on complete datasets can result in massive computing costs in ongoing development & testing operations. Generating a summarized dataset that addresses the relevant for specific use-cases can be deployed to speed up development and reduce costs.
Complex Datasets
When dealing with complex datasets, synthetic data techniques can be deployed to deal with aspects such as multiple tables and relationships, multi-variate timeseries data, geo-location data, and images, while preserving the original data’s properties. Use of comprehensive and organized synthetic data techniques towards addressing problems such as the above, can increase speed and reduce costs in deploying data-driven decision-making strategies.
At Contata we have actively been leveraging Synthetic Data generation and management approaches to address various business problems for our clients. Our engagements have involved creating parallel datasets for enabling remote development, as well as engineering training data for ML models to add diversity and remove outliers. Our approach incorporates careful analysis of the operational objectives, and then deploying tried and tested tools towards engineering the right synthetic data solution for the situation. For more information on how Contata can help you, visit our website at www.contata.com
Contata is a global innovation leader in digital disruption and transformation. Our mission is to inspire ideas and unlock value through data science and technology. Contata is headquartered in Minneapolis, MN USA with international offices in Delhi and Nagpur, India and Stockholm, Sweden.