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  • Synthetic Data

    Published Date : March 22, 2018

    As per insideBIGDATA research, the size of the digital universe is likely to double every two years. Although companies can gain great insights from such large data sets, privacy concerns make it difficult for them to access the data they would like to be exposed to. With privacy laws across geographies getting more stringent by the day, companies are now taking to an innovative concept called “Synthetic Data” to finally attain insights.

    Synthetic data is basically information that's artificially manufactured rather than generated by real-world events. The benefits of using such a data stream include avoiding constraints with regards to using sensitive or regulated data, tailoring the data needs to certain conditions that cannot be obtained with authentic data and generating datasets for software testing and quality assurance purposes.

    Fintalk - Bank of Baroda

    In the financial sector, synthetic datasets such as debit and credit card payments data with the look and feel of typical transactional data can help expose fraudulent activity. Data scientists are using synthetic data to test or evaluate fraud detection systems as well as develop new fraud detection methods.

    Research efforts to advance synthetic data use in machine learning are underway. MIT Laboratory’s AI Lab developed a Synthetic Data Vault (SDV) that can construct machine learning models to automatically generate and extract its own synthetic data. Companies are also beginning to experiment with synthetic data techniques. As an instance, Deloitte used synthetic data to build an accurate model by artificially manufacturing 80% of the training data, using real data as seed data.

    Understanding the importance of Synthetic financial datasets, Google recently acquired Kaggle, a crowd sourced platform that hosts predictive modelling and analytics competitions to generate synthetic data. Kaggle’s platform recently hosted a competition to generate synthetic data to code a trading algorithm. Such competitions have germinated and led to the development of many successful projects including that of traffic forecasting – a game changer for autonomous vehicles.

    Even retailers like Nestle, PepsiCo, L’Oreal, Unilever, etc. have partnered with firms like to leverage their synthetic data platforms in order to ensure the availability of goods on the shelf.

    With an explosive growth in data generation, the need to glean meaningful insights from such massive datasets has become a matter of survival for businesses. Stringent privacy regulations have made it mandatory for data owners to restrict access to private data. Use of synthetic data is playing a significant role in generating insights that are in turn providing cues to build more effective products and services.

    Credits : Akhil Handa

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