Data as a Product

𝗛𝗼𝘁-𝘁𝗮𝗸𝗲: 𝗬𝗼𝘂 𝗗𝗼𝗻’𝘁 𝗡𝗲𝗲𝗱 𝗠𝗼𝗿𝗲 𝗗𝗮𝘁𝗮—𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀.

Data is everywhere. But here’s the challenge: It’s fragmented, unstructured, hard to trust, and underutilised. While most organisations 𝘄𝗮𝗻𝘁 data, few know how to 𝗮𝗰𝘁𝗶𝘃𝗮𝘁𝗲 it. Even fewer know how to 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘀𝗲 it.

🔹 What if data wasn’t just shared, but curated, packaged, and activated—like a product? 📦
🔹 What if raw, scattered data from multiple sources could seamlessly plug into business workflows—without compliance risks, friction, or endless integrations? 📈

This is 𝗗𝗮𝘁𝗮 𝗮𝘀 𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 (𝗗𝗮𝗮𝗣)—a 𝗹𝗼𝘄-𝘁𝗼𝘂𝗰𝗵, 𝗵𝗶𝗴𝗵-𝘁𝗿𝘂𝘀𝘁 approach to unlocking alternate data for real-world impact. It’s a new way for businesses to think about data monetization, collaboration, and strategy.

From retailers like Amazon, Walmart, Target to payment networks like Revolut, PayPal, Chase, Mastercard and even consumer ecosystems like Truecaller, Foursquare, the biggest players are building data assets for consented, compliant and high-trust intelligence.

It’s not just about accessing data—it’s about making it work, securely and compliantly. 🔒

𝗜𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗶𝘀 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸:
✅ Why 𝗿𝗮𝘄 𝗱𝗮𝘁𝗮 ≠ 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘃𝗮𝗹𝘂𝗲
✅ How to 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝟮𝗣 𝗱𝗮𝘁𝗮 with minimal friction
✅ The future of 𝘀𝗲𝗰𝘂𝗿𝗲, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻

What makes a product?

A Product is anything that:

• Delivers immediate value

• Usable, Accessible & Impactful

Applying this framework to the Data construct, we can define Data as a Product (DaaP) as the following:

• Packaged, maintained & delivered for usability

• More than raw data — structured, governed, consumable

• Designed for AI, analytics, optimisations, cross-org collaborations, & advanced 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 data delivery, but lacks usability

• Needs further exploration, processing, governance

Eg. Loan Repayment History, E-Commerce Purchase Data

📦 Data as a Product (DaaP)

• Fully structured, documented, consumable & actionable

• Includes: Discovery ➜ Usability ➜ Activation ➜ Outcome

Eg. 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 from internal systems

• Used for cohorting, personalization, BI, and 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 & structured

• Enhances depth of user profile beyond internal signals

Examples:
💳 Digital Wallet Share — Chase, Revolut
❗ Fraud Databases — TransUnion, FICO
🛍️ Digital Shelf Analytics — Walmart, Target

The 2P Data Conundrum

2P data complicates the process, raising new questions:

🔹 What’s the data source? What exact data? What formats?
🔹 How do I trust the data? Is it legal, ethical, & consented?
🔹 Is the data fresh and accurate for decision making?
🔹 How to ensure seamless, secure & cost-effective access?
🔹 How will it be delivered, maintained, and measured?

To fully unlock the power of 2P Data, organizations must focus on:

1. Discovery - Finding & Consuming high-value 2P data

2. Governance - Ensuring quality, compliance & privacy

3. Activation - Integrating data into decision-making

DaaP Playbook (1/3) - Discovery

Finding & accessing high-value 2P data

• Identify the right data partners with overlapping user base to build deeper understanding of your audience

• Establish structured legal & commercial data contracts to ensure reliability, compliance and transparency

• Ensuring seamless data access via APIs, SDKs & warehouse native solutions with well-documented tools

• Leveraging data catalogs, metadata, & schema mapping for discoverability to accelerate real productivity

Tools: Low-cost ETL, APIs / SDKs, Data Contracts, Domain Context, Documentation

DaaP Playbook (2/3) - Governance

Ensuring data quality, compliance & privacy

• Prioritize ethical, consented & compliant data sources with proper storage & verification mechanisms

• Verify data quality and lineage to ensure accuracy, freshness & consistency for reliability

• Implementing Privacy-Enhancing Technologies (PETs) and privacy budgets for secure, trusted collaboration

• Leveraging PETs for transparency as well as higher flexibility on purpose-specific feature engineering

Tools: Consent & Privacy, Decentralization, Freshness, PETs led transformations, Verifiable Quality

DaaP Playbook (3/3) - Activation

Turning data into actionable insights & measurable impact

• Prebuilt Data and Insight Catalogs ready for specific industries, customers, & use-cases for low time-to-value

• Leveraging low-touch AI & modelling tools with purpose-built frameworks for better efficacy

• Operationalize mission-focused apps, activation channels, & integrations to convert insights to outcomes

• Building effective feedback & measurement loops to validate success and enable monetization at scale

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 break 1P glass ceiling

• 🔗 Reduced data asymmetry via 2P insights

• 🛡️ Proactive Compliance in evolving privacy landscape

• 🤝 Federated modeling for privacy-first intelligence

What do we have to offer?

• Out-of-the-box sources for compliant 2P data

• Privacy-first platform for secure monetisation

• Built for everyone: Use case templates + Self-serve

• Trust-less collaboration with full control & governance

• Transparency-driven compliance for secure exchange

• One-click activation for fintech, BFSI, ads, marketing, commerce, modelling, decisioning AI

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