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Leverage PII and sensitive data without compromising security and privacy
Extract valuable insights without viewing raw data
Row level details of datasets can be hidden from the user, but still used by the platform to create features for insight-generation and model building.
No need to aggregate or anonymize data
Aggregations contribute to the sever loss of data richness and granularity. Instead, this approach reflects the level at which actions are taken for specific use case requirements.
Leverage out-of-reach proprietary and confidential data sources
Use a pre-configured analytics pipeline in a secure environment, with easy access to additional external datasets.
Guarantee security while offering insights as a service
Data providers can become a true “signals marketplace” by allowing external clients to derive insights from non-aggregated data.
Secure, pre-configured analytics pipeline
No trade-off between privacy and signal
The growing reluctance to share sensitive data leaves massive value untapped. The AI behind Blindfolded Analytics extracts human-level ideas from data, all the while keeping sensitive information hidden from human eyes.
Autonomous Insight Generation
The Discovery platform can be configured to hide row-level information from certain datasets.
Users can see the schema, but can’t view the details (data table, samples, plots) or export the data.
Instead, the platform autonomously leverages the row-level data to generate insights and composite features, discover patterns, or hook datasets together.
Commercially sensitive data challenges and solutions
Shopper Data
As shopper data is too heavily aggregated due to data privacy restrictions, it’s much harder to link this data with other, contextual internal or external data.
Store Data
There are gaps in store-level data as retailers are unwilling to share sell-out or sell-in data, as the market relies on wholesale relationships.
Pricing Data
Instead of black box pricing models based on the underlying data size, frequency, or available aggregations, dynamic pricing can be based on insights extracted from disaggregated, attributed data.
Data Providers
The AI platform sits between the data provider and the data purchaser, surfacing patterns from integrated datasets. and opening up valuable new revenue streams as ‘insights-as-a-service’.
