Data as a Product (DaaP) - A low touch, high trust approach to unlocking alternate data for real-world impact.
Hot Take
"You Don't Need More Data - You Need Smarter Data Products."
Data proliferation creates challenges: fragmentation, lack of structure, trustworthiness issues, and underutilization. Organizations desire data but struggle with activation and productization.
The concept poses: What if data were "curated, packaged, and activated - like a product"? What if scattered data from multiple sources could "seamlessly plug into business workflows - without compliance risks, friction, or endless integrations"?
Data as a Product (DaaP) represents "a low-touch, high-trust approach to unlocking alternate data for real-world impact." This approach reshapes how businesses approach monetization, collaboration, and strategy.
Major companies - Amazon, Walmart, Target, Revolut, PayPal, Chase, Mastercard, Truecaller, and Foursquare - are constructing data assets emphasizing consent, compliance, and trustworthiness.
What Makes a Product?
A product:
- Delivers immediate value
- Is usable, accessible, and impactful
Applied to data, DaaP means:
- Packaged, maintained, and delivered for usability
- Exceeds raw data through structure and governance
- Designed for AI, analytics, optimization, collaboration, and decision-making
"Data moves from Passive Storage ➜ Real Business Value"
DaaS v/s DaaP: What's the Difference?
Data as a Service (DaaS)
- Point solution for delivery lacking usability
- Requires additional exploration, processing, governance
- Examples: Loan Repayment History, E-Commerce Purchase Data
Data as a Product (DaaP)
- Fully structured, documented, consumable, actionable
- Includes: Discovery ➜ Usability ➜ Activation ➜ Outcome
- Examples: Credit Score, Purchase Intent, Conversion Propensity
"DaaS ➜ Data Delivery; DaaP ➜ Actionable Insight Ready for Activation"
1P v/s 2P Products
1P DaaP - Owned & Controlled
- Collected directly from users or derived internally
- Used for cohorting, personalization, BI, insights
Examples:
- Purchase History (1P Transactions)
- Tagged Risky Users (1P Loan Book)
- Retailer SKU Trends (1P Sales)
2P DaaP - Trusted External Data
- Shared by partner entities (consented and structured)
- Enhances user profiles beyond internal signals
Examples:
- Digital Wallet Share (Chase, Revolut)
- Fraud Databases (TransUnion, FICO)
- Digital Shelf Analytics (Walmart, Target)
The 2P Data Conundrum
2P data integration raises critical questions:
- Data source, format, and specifics?
- Data trustworthiness, legality, ethics, and consent?
- Freshness and accuracy for decision-making?
- Seamless, secure, cost-effective access?
- Delivery, maintenance, and measurement?
Organizations must prioritize:
- Discovery - Finding and consuming high-value 2P data
- Governance - Ensuring quality, compliance, privacy
- Activation - Integrating data into decision-making
DaaP Playbook (1/3) - Discovery
Finding & accessing high-value 2P data
- Identify right data partners with overlapping audiences
- Establish structured legal and commercial data contracts
- Ensure seamless access via APIs, SDKs, warehouse solutions
- Leverage data catalogs, metadata, schema mapping
Tools: Low-cost ETL, APIs/SDKs, Data Contracts, Documentation
DaaP Playbook (2/3) - Governance
Ensuring data quality, compliance & privacy
- Prioritize ethical, consented, compliant sources
- Verify quality and lineage for accuracy and freshness
- Implement Privacy-Enhancing Technologies (PETs)
- Leverage PETs for transparency and flexible feature engineering
Tools: Consent & Privacy, Decentralization, Freshness, PETs, Quality Verification
DaaP Playbook (3/3) - Activation
Turning data into actionable insights & measurable impact
- Prebuilt data and insight catalogs for industries and use-cases
- Low-touch AI and modeling tools with frameworks
- Operationalize apps, channels, integrations for outcomes
- Build feedback and measurement loops for validation
Tools: Feature Catalogs, Activation Channels, Auto-ML, Pre-built Apps, Integrations, Observability
How LattIQ Unlocks Value
Unlocking Scalable AI & Compliant Insights via 2P Data
- Resilient AI to transcend first-party limitations
- Reduced data asymmetry through second-party insights
- Proactive compliance in privacy evolution
- Federated modeling for privacy-first intelligence
Offerings:
- Compliant 2P data sources
- Privacy-first secure monetization platform
- Use case templates and self-service capabilities
- Trust-less collaboration with full governance
- Transparency-driven compliance for secure exchange
- One-click activation for fintech, BFSI, ads, marketing, commerce, modeling, decisioning AI
