AI in data clean rooms — Matt Kilmartin & Matthew Karasick // Habu
- Part 1 AI in data clean rooms — Matt Kilmartin & Matthew Karasick // Habu
- Part 2Utilizing data to drive smart business outcomes — Matt Kilmartin & Matthew Karasick // Habu
Show Notes
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03:42Understanding the function and purpose of a data clean roomA data clean room is a virtual environment that facilitates data collaboration between companies without sharing or copying the actual data. It enables companies with shared business interests to work together on data projects while ensuring privacy, compliance, and security.
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04:37Ensuring controlled data sharing with Habu's privacy and compliance layerHabu's software enables controlled data sharing by granting specific data views for desired outcomes without actual data copying or moving. This compliance and privacy layer ensures the data owner retains control over shared data aspects and permitted analytical workloads.
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05:20Three core obstacles Habu overcomes for customers and their collaboration partnersHabu's integration layer directly accesses data without requiring data movement. The privacy and governance layer enforces agreed-upon actions, while the analytical layer simplifies insights extraction within privacy and governance parameters.
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07:34Enabling secure collaborative analysis with Habu's analytical layerHabu's analytical layer connects to existing data, enabling secure processing of queries or models, producing output without granting data ownership or download rights to third parties. Ultimately, the source data remains unmoved throughout, ensuring privacy and security.
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09:31Enabling model building without direct data accessWhile the third party can't access specific individual data rows, they can obtain privacy-safe insights like the size and shape of the data. This approach allows them to build and train models without direct data access, resulting in model outputs containing statistical details and coefficients.
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10:49Navigating privacy and collaboration challenges in data driven businessesFor detailed access needs, collaboration partners can contact the data owner, striking a balance between interaction and privacy. Habu's solution enables controlled exposure of specific aspects, fostering valuable collaboration on first-party data integration for improved business outcomes.
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12:49The benefits of a collaborative approach to data accessAlthough this approach is a step removed from direct data viewing, it offers a trade-off: while analysts sacrifice some direct access, they gain access to a broader range of data sources, facilitated by technical safeguards and privacy measures.
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13:47Data clean room success storiesData clean room success stories include Disney utilizing data to enhance advertising strategies and LinkedIn enriching profiles and measuring impact. Other sectors like financial services, media, retail, and travel have also found value in data clean room solutions.
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16:23AIs role in collaborative data analytics and context aware LLM creationAI accelerates innovation by aiding in the discovery and implementation of privacy-compliant use cases. It also enables the creation of context-aware AI models through clean room collaborations that enhance understanding using partner data.
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20:23Enterprise adoption of context aware AI modelsMany enterprises are actively investing in developing their own context-aware AI models and considering the data from clean rooms as part of that process. However, as its in the early stages, it will take time to observe how these models are utilized and refined.
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21:02AI enhanced productivity in the Habu platformAI integration in data clean rooms provides enhanced productivity and capabilities within the Habu platform. Users can engage AI to ask the data questions, receive alerts for specific conditions, automate query writing, and craft dataset descriptions for partner collaboration.
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22:48Data clean rooms vs. escrow servicesHabu's solution connects to existing data sources without sharing data. It enforces privacy controls, accesses minimal data for agreed-upon use cases, and ensures technical guarantees during runtime, eliminating the need for an escrow service.
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24:37Trusted escrows vs. technical guarantee escrowsEscrow services involve trusting them to hold your data, while with Habu, it's a technical guarantee escrow. Confidential computing ensures approved code runs on trusted hardware exclusively, preventing even Habu from accessing the data in that environment.
Quotes
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"A CPG being able to take their first-party data, and connect it with different media partners, or different retailers where a lot of the transactions are happening is incredibly valuable." - Matthew Karasick
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"In the past, if you had asked to use certain data for model training, the answer would have been a definite 'no.' It wasn't about disliking the model, but rather about the potential applications if the data were shared." - Matthew Karasick
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"As enterprises start to ask themselves how they can use LLMs and AI themselves, what theyre quickly doing is trying to figure out how to tune their own LLM and AI so that it is context aware." - Matthew Karasick
- Part 1 AI in data clean rooms — Matt Kilmartin & Matthew Karasick // Habu
- Part 2Utilizing data to drive smart business outcomes — Matt Kilmartin & Matthew Karasick // Habu
Up Next:
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Part 1AI in data clean rooms — Matt Kilmartin & Matthew Karasick // Habu
Matt Kilmartin and Matthew Karasick, CEO and CPO at Habu, explore data-driven AI solutions for business efficiency. Data clean rooms enable companies to collaborate with their partners' data without the need to physically move or copy the data. AI integration takes this to a whole other level as it enhances the analysis and insights derived from the shared data, in addition to an array of other benefits. Today, Matt and Matthew discuss AI data in clean rooms.
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Part 2Utilizing data to drive smart business outcomes — Matt Kilmartin & Matthew Karasick // Habu
Matt Kilmartin and Matthew Karasick, CEO and CPO at Habu, explore data-driven AI solutions for business efficiency. Using data to drive smart business outcomes within the framework of data clean rooms involves collaborating with partners' data without sharing the actual data itself. For instance, companies can analyze consumer behavior, optimize marketing strategies, and measure the impact of advertising campaigns using combined datasets, all while safeguarding data privacy and security. Today, Matt and Matthew discuss utilizing data to drive smart business outcomes.
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