Datathons: Data-intensive hackathons not just for innovation
Power up ideas with highly realistic synthetic data and zero privacy risk
Proof
A North American financial service provider wanted to gain actionable business insights on current research topics and disruptive technologies. The company previously used Kaggle datasets for research hackathons; however, the Kaggle data was not representative of their real data environment. Setting up a synthetic data sandbox provided university students and researchers high levels of explorability without endangering data privacy. Participating teams developed viable product ideas leveraging AI-powered financial prediction functionalities. Additionally, the bank signed a long-term partnership with the university and used hackathons to acquire talent from academia.
Challenges
- Tapping into the creativity and knowledge of external talent is crucial for driving innovation.
- Most production data is off-limits to participants; sample data is insufficient and is not representative of the original.
- Developers are often discouraged when they have to work with dummy data lacking dimension and volume.
- Due to the data-poor environment, and lack of exposure to production data, disruptive technologies fail to perform up to their potential.
- Providing partial datasets restricts creativity and outcomes.
- Non-representative datasets lead to ideas born on false premises.
Solution
Synthetic data provides unprecedented levels of data explorability without privacy issues. Synthesizing full datasets empowers developers and scientists to ask any questions without being restricted by data provisioning constraints.
Generate synthetic data from your behavioral data assets quickly and easily to create a highly realistic, privacy-compliant data environment. As-good-as-real synthetic copies of your production datasets enable hackathon participants to work on robust solutions safely and without the debilitating limits of data scarcity while keeping privacy intact.
Why MOSTLY AI?
MOSTLY AI offers unparalleled data accuracy, empowering hackathon participants to test their ideas in a data environment with data that’s statistically identical to the original. Data privacy is a built-in feature: the synthetic data points bear no 1:1 relationship to the original data points so you can share your data with peace of mind. You can generate synthetic datasets quickly and easily from various data input types, including behavioral data and Customer-Account-Transaction tables, providing readily available sources for idea development and testing. Synthetic datasets also come with automated QA reports detailing quality and privacy dimensions.
