HOW IT WORKS

Unlock Insights with Data Collaboration

Collaboration Without Data Alienation

Collaboration often stalls because sharing data is a prerequisite, restricting the potential for advanced people analytics. To unlock its true potential at scale, human collaboration between HR subject matter experts, data workers, and decision-makers across organizations is needed. Cross-organizational data collaboration achieves this, ensuring advanced insights while preserving privacy and maintaining control for the data providers.

Components

Collaborative Platform

Tapir’s community unbundles the data value chain, enabling members to collaborate seamlessly across organizational boundaries. Data Collaboration empowers HR professionals to share insights, drive innovation, and solve people challenges together, creating a rich and impactful data ecosystem.

Data Network Effects

Data is the most valuable resource of the digital age, but its value is not unlocked linearly but in the form of S-curves. A single organization’s data fails to reach the exponential value growth phase on its own. However, Data collaboration allows organizations to capture the network effects necessary to unlock this value through advanced analytics and AI models.

Preserving Privacy

Data alienation has been the biggest blocker to joint analytics. Legal-only solutions like the data trust concept fall short due to the risks of fraud and self-interest among data trusts and intermediaries. Data Collaboration preserves privacy and leaves full control over data access with the data providers.

Decentralized Governance

Every contributor to the data value chain deserves a fair share in the jointly created data products and value. Important decisions about our ecosystem are made collectively, ensuring transparency and inclusivity for all members.

How it Works

How it works - Part 1

CONTRIBUTE

  • Data Source: The data of the enterprise which remains on their servers – a database, a csv file or connected via API.
  • Local Training: Community-approved training algorithm runs on this local data within the enterprise’s private infrastructure.
  • Local Model: The result is a local model encapsulating the insights derived from the enterprise’s data. At no point in time raw data or PII data gets stored or transferred out of the private environment.
How it works - Part 2

BUILD

  • Local Model(s): The local models generated by each participating enterprise.
  • Federated Learning: The aggregation algorithm runs on a shared trusted provider infrastructure.
  • Aggregated Model: The result is a final model that aggregates the insights from all local models, enhancing overall accuracy and providing deep insights.
How it works - Part 3

USE

  • Local Processing: The final model is applied to the enterprise’s own data in its private environment.
  • Inference: The approved inference algorithm runs on the local data within the enterprise’s infrastructure.
  • Dashboard: The result is report data that is displayed within a local dashboard, providing actionable insights tailored to the enterprise’s specific needs.

Preserving privacy via Compute-to-Data

Compute-to-data is a key feature of our solution at Tapir. Our platform manages the computations/algorithms necessary to build a data product. Computations to your datasets are applied only in your private computing environment so that your sensitive information never leaves the realms of your organization, ensuring maximum privacy and security while still gaining valuable insights.

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