Data Enrichment: Connecting Web2 and Web3

Enterprise TradFi needs a picture painted for them — and this is how it’s going to be done

9 min readFeb 12, 2026

The Phone Is Always Listening

You’re at dinner with friends or family, talking about a specific product, a movie you saw, or a book you wanted to read. You say your goodbyes, drive home, and completely forget about that conversation.

Then two days later, when you open Twitter or Amazon, you’re being shown an ad for that very same thing.

Your phone was listening and all of your patterns and behaviors while using your phone and specific applications led the company to believe that you were interested in that product, so you’re presented with an ad for it.

This isn’t magic. It’s attribution combined with data enrichment.

Enrichment is painting the full picture.

What Data Enrichment Actually Is:
Traditional finance and Web2 companies that deal in e-commerce, financial transactions, and dozens of other verticals have massive amounts of consumer information. They know:

Where we live
Where we bank
Where we shop
How we shop
Almost anything and everything about us

Data enrichment is the process of enhancing raw data by adding valuable context and details from internal or external sources.

This information can come from inside a company’s own database, or they can get it from an outside source — like another data company or an enrichment service. The goal is to make data more complete, more accurate, and more insightful for different business decisions, customer experiences, improved UI/UX, and better analytics.

Essentially, you’re taking really simple records and turning them into rich individual profiles with demographic, behavioral, geographic, and temporal attributes.

How Data Enrichment Works Today:

The process combines internal and external data:

Step 1: Merge First-Party and Third-Party Data
Merge your first-party data (like customer names and emails) with third-party data (like industry, company size, or location).

Step 2: Add Context
Fill in the missing gaps and add meaning to basic information. For example, adding location and purchase history to a customer’s name and email.

Step 3: Optimize with AI and Automation
AI and automation help optimize the process, though it could be done manually.

Common Types of Data Added:

Demographic: Age, gender, income
Firmographic: Company name, industry, revenue, size, technology used
Geographic: City, country, time zone
Behavioral: Website visits, purchase history, engagement metrics
Technographic: Software platforms and tools used

The Benefits: Why Companies Do This

The benefits and use cases are straightforward:

Tailored recommendations and offers for increasing engagement
Deeper insights for data-driven strategies
Creating detailed customer profiles for targeted marketing efforts

This is how Amazon knows what to recommend. This is how Target knew a teenager was pregnant before her father did. This is how every major company operates today.

The Web3 Gap: Half the Picture Is Missing

Here’s the problem: as traditional finance and Web2 companies start to consume Web3 infrastructure and rails — where decentralization is going to change the way finance looks — they’re going to need Web3 behavioral data to complete the picture.

The companies that are building this intelligence and using data enrichment are going to start combining:

Web3 information (transactions, wallets, on-chain behavior)
Web2 information (purchase history, demographics, browsing behavior)

Companies like Amazon, Best Buy, Sephora, Target, and Apple are either going to:

Come up with their own in-house Web3 solutions (wallets, blockchains, applications)
Partner with Web3 companies that can provide this enrichment

They will offer you a service. You will use said service because of the value you derive from it. And then they are going to take the Web3 information they’ve collected from you and either go to a third-party company to enrich it, or they’re going to do it in-house.

The Privacy Problem

But one of the biggest issues here is dealing with privacy.

How are you going to safely collect a Web3 user’s information and then connect the dots and add it to a Web2 profile?

Now you essentially have:

A user’s name
A user’s email address
Their social media profile
Their wallet address
All of the subsequent wallets they are connected to

This is an incredibly sensitive combination of data that, if mishandled, could violate privacy in ways that even Web2 surveillance capitalism hasn’t achieved yet.

The Business Opportunity: Bridging Both Worlds

This is going to create a potentially massive value proposition for a business that can bridge both the Web2 and Web3 worlds with valuable intelligence, data, and insights.

Here’s why both sides need this:

Web3 businesses don’t necessarily have:

The infrastructure to collect and enrich Web2 data
The time to build these capabilities
The ability to work with enterprise clients at scale

Web2 businesses don’t necessarily:

Know the Web3 space
Know what to collect from on-chain activity
Know how to use blockchain data
Understand wallet behavior and DeFi interactions

The Solution: Web2 to Web3 Enrichment

Here’s what the solution looks like:

The bridge that business will need moving forward.

The Process:

Web2 enterprise businesses bring their existing data sets
A specialized business enriches that data with Web3 behavioral information and insights
The enhanced data set is returned to the enterprise client

The Value Proposition:

Bridging Web2 and Web3 ecosystems
Competitive intelligence for emerging markets
Strategic decision-making data for Web3 expansion
Complete user profiles across both digital worlds

What This Enables: Unified Profiles

Basically, Web2 to Web3 enrichment means targeting and enriching traditional user profiles with crypto and blockchain activity.

Most Web2 companies already have established user databases. What they need is a machine learning-powered attribution model that connects the dots between:

Web2 profiles (email, name, browsing history, purchase behavior)
Web3 wallet addresses (on-chain transactions, DeFi activity, NFT holdings)

The output is a unified platform with user profiles that combine both traditional and blockchain behaviors.

This means:

Web2 businesses have Web3 information about their users
Web3 businesses have Web2 information about their users

The Technical Workflow: How This Actually Works

Here’s the step-by-step process for how a Web2 company gets their data enriched with Web3 behavioral information:

The technical proccess of “connecting the dots”

Phase 1: Data Upload and Preparation

Step 1: Web2 Company Exports Their Database
The company exports their existing user database. This data includes:

Traditional behavior information (page views, purchases, sessions)
User identities (email hashes, device IDs, cookies, IP addresses)

Step 2: Secure Upload
They upload that data using a secure mechanism, likely with encrypted transfers.

Step 3: Validation and Storage
Upon receipt, the data is validated and stored in isolated Web2 enrichment partitions.

Phase 2: Machine Learning Matching

Step 4: Pattern Analysis
The machine learning model analyzes the uploaded Web2 user profiles. The algorithm identifies:

Behavioral patterns
Digital fingerprints
Temporal identifiers
Other matching signals

Step 5: Profile-to-Wallet Matching
The model matches Web2 profiles to known Web3 wallet addresses based on attributed information that has already been collected from on-chain activity.

Step 6: Confidence Scoring
Confidence scores are assigned to each potential match using:

Proprietary algorithm matching
Pattern recognition across behavioral signals
Cross-platform activity correlation

Phase 3: Privacy-Preserving Enrichment

Step 7: Zero-Knowledge Proofs and Blind Compute
Because this particular business is dealing with highly sensitive data, they use privacy-preserving technology:

Zero-knowledge proofs
Blind compute
Never exposing raw data

This means you’re proving connections exist without revealing the actual data.

Phase 4: Unified Profile Creation

Step 8: Data Combination
The system combines:

Web2 Data from Client:

Browser behavior
Purchase history
Session data
Demographics
Engagement metrics

Web3 Data from On-Chain:

Wallet addresses
On-chain transaction history
DeFi protocol interactions
Token balances
Smart contract interactions

Step 9: Output Generation
The output is unified profiles showing complete cross-platform behavior across both domains.

What Unified Profiles Actually Enable:

Enabling this means better products, better decisions, more information, better products.

Let’s get concrete about what this data enables:

For Retail Companies (Amazon, Target, Walmart):

User bought a gaming PC on Amazon → owns 5 ETH and trades on DEXs daily → recommend Web3 gaming platforms, crypto tax software, hardware wallets
User shops for luxury goods → holds high-value NFTs → offer exclusive NFT-gated shopping experiences

For Financial Services (Banks, Investment Firms):

Customer has traditional investment portfolio → also has $500K in DeFi positions → offer crypto custody services, tax optimization, institutional DeFi access
Customer makes frequent international transfers → actively uses stablecoins for cross-border payments → offer integrated crypto payment solutions

For Social Media Platforms:

User engages with finance content → trades options and derivatives on-chain → serve content about DeFi derivatives, yield optimization
User follows crypto influencers → has never made an on-chain transaction → serve educational content about Web3 onboarding

For Gaming Companies:

Player spends money on in-game items → owns gaming NFTs on multiple chains → offer NFT integration, play-to-earn opportunities
Player has no Web3 activity → high engagement in-game → introduce them to blockchain gaming gradually

The Business Value: Why This Matters

A company that can unify both worlds is going to drive massive value by:

Better Product Development:
Identify which users are ready for Web3 features and which need more education.

Better Campaigns:
Target users with relevant offers based on complete behavioral profiles, not partial data.

More Targeted Analytics:
Understand the full customer journey across Web2 and Web3 touchpoints.

Less Waste:
Stop spending money on marketing to users who aren’t ready or aren’t interested based on their actual behavior across both ecosystems.

Strategic Decision-Making:
Determine when and how to launch Web3 initiatives based on actual user readiness and interest, not speculation.

The Privacy Advantage: Why This Isn’t Surveillance 2.0

Web3 businesses are going to have a much more unique and secure way of unifying all of this information while still allowing businesses to drive value without exposing people’s information.

How privacy is maintained:

Zero-Knowledge Proofs:
Prove that a transaction happened and that what happened did happen on-chain with an immutable record — without revealing the transaction details or linking it to a real identity.

User Control:
Give people the ability to have their information secured so it won’t be leaked in an exploit or a hack.

Encryption Throughout:
All data transfers are encrypted. Raw data is never exposed.

Isolated Partitions:
Web2 and Web3 data are stored separately and only combined through privacy-preserving algorithms.

What This Enables at Scale

Privacy is going to be built in not an after thought.

This essentially means that a company is going to be able to:

Filter for Fraud:
Identify wash trading, Sybil attacks, and bot behavior by correlating Web2 and Web3 patterns.

Align with Compliance:
Meet KYC/AML requirements while still preserving user privacy through zero-knowledge proofs.

Preserve Privacy:
Use advanced cryptography to ensure user data is never exposed, even to the company doing the enrichment.

Paint the Complete Picture:
Have comprehensive Web2 and Web3 data that paints a complete picture of user behavior — instead of only looking at one half or an incomplete version of it.

The Market Opportunity

Traditional finance and enterprise Web2 companies are going to need this capability as they move into Web3. They can either:

Build it themselves (expensive, time-consuming, requires Web3 expertise they don’t have)
Partner with a company that specializes in this (faster, more secure, privacy-preserving by default)

The companies that can provide this service — bridging Web2 and Web3 data with privacy-preserving enrichment — are going to be essential infrastructure for the next wave of Web3 adoption.

The Bottom Line

Data enrichment is how Web2 companies know everything about you. As Web3 adoption grows, they’re going to need to know your on-chain behavior too — not to exploit it, but to serve you better.

The question is: can this be done in a way that preserves privacy while still providing business value?

The answer is yes — through machine learning attribution models, zero-knowledge proofs, and privacy-preserving computation that connects Web2 and Web3 data without exposing raw user information.

The companies that build this infrastructure will enable:

Enterprise adoption of Web3
Better products that serve users across both ecosystems
Privacy-preserving analytics that benefit businesses and users
Strategic decision-making based on complete data, not partial pictures

Enterprise TradFi needs a picture painted for them. This is how it’s going to be done.