Waterfall data enrichment: Why streaming data beats static stacking

If you’ve been in RevOps circles lately, you’ve heard the term: waterfall data enrichment.

It sounds fancy. Maybe even essential.

The promise is simple: Send your leads through multiple data providers in sequence until every field is filled. Get 100% data coverage without paying for redundant lookups.

In theory? Brilliant.

In practice? You’re building complex plumbing to connect static data sources that were never meant to work together.

But here’s what’s changing: The best revenue teams aren’t building waterfalls anymore. They’re streaming data instead.

Let’s break down what waterfall enrichment is, why it exists, and why real-time streaming data is replacing it.

What is waterfall data enrichment, actually?

Let’s start simple.

Waterfall enrichment means routing your data through multiple providers in a prioritized sequence—like a waterfall flowing down steps—with each provider filling in the gaps the previous one missed.

Here’s how it typically works:

1. Lead enters your system (form fill, upload, API call)
2. First provider enriches (adds email, phone, company data)
3. Check for gaps (missing fields)
4. Second provider fills gaps (only calls if needed)
5. Third provider handles remaining data
6. Final result: Fully enriched record in your CRM

The goal: Maximum coverage at minimum cost.

Why waterfall enrichment exists

Waterfall strategies emerged because traditional data providers work like this:

The old model:

• Build giant static database
• Update it periodically (monthly, quarterly)
• Sell access to this frozen-in-time snapshot
• Hope it’s accurate when customers use it

The problem? No single static database has everything. One provider might have good email coverage but weak phone numbers. Another might have phones but stale emails.

So RevOps teams started chaining them together—hoping to cobble together complete records from multiple incomplete sources.

Waterfall enrichment is a workaround for static data.

The case for waterfall (if you’re stuck with static providers)

Let’s be fair. For teams using traditional static data providers, waterfall strategies can help.

Pro #1: Fills coverage gaps

If your primary provider only covers 50–60% of your target market, adding secondary sources helps.

Example:

• Provider A finds email: ✅
• Provider A finds phone: ❌
• Pass to Provider B for phone: ✅
• Result: Complete record (eventually)

Pro #2: Cost efficiency (in theory)

The math looks appealing when you use cheaper providers first, then expensive ones only for gaps.

But this assumes perfect orchestration with no maintenance costs. More on that in a moment.

Pro #3: Reduced vendor lock-in

Some teams like having multiple vendors as insurance.

If one provider raises prices (ZoomInfo increased prices significantly for many customers in 2024), you have alternatives ready.

The dark side: Why waterfall enrichment backfires

Waterfall enrichment sounds perfect on paper. But it introduces complexity, cost, and risk that most teams underestimate.

Con #1: Implementation complexity

You’re not just integrating one API. You’re integrating multiple providers.

Each has:

• Different authentication methods
• Different data schemas
• Different rate limits
• Different error handling
• Different field mappings

Translation: Your engineering or RevOps team spends weeks (or months) building and maintaining custom orchestration.

Unless you’re using a no-code platform (Clay, Census, Hightouch), waterfall enrichment is a significant technical lift.

The streaming alternative: Single integration. Data flows automatically. Done in hours, not months.

Con #2: Compliance nightmares

Each provider in your waterfall has different data sourcing and compliance practices.

If any provider violates GDPR or CCPA, you’re liable. You can’t just say “Provider C gave us bad data.” You’re responsible for every contact in your CRM.

Questions to ask each provider:

• Where does your data come from?
• Do you have consent for B2B contact data?
• Are you GDPR and CCPA compliant (with proof)?
• What certifications do you have?

If you can’t get clear answers, don’t add them to your waterfall.

Lusha’s compliance: ISO 27701, ISO 27001, SOC 2 Type II, GDPR, CCPA compliant. Transparent sourcing. Every contact meets compliance standards.

Con #3: Data conflicts and duplicates

What happens when providers disagree?

• Provider A says: Title = “VP of Sales”
• Provider B says: Title = “Director of Sales”
• Provider C says: Title = “Head of Revenue”

Which is correct?

You need conflict resolution logic. Without clear rules, your CRM becomes a mess of conflicting data.

The streaming alternative: Single source of verified truth. No conflicts. No duplicates. Data stays clean.

Con #4: Your data is still static

Here’s the fundamental problem with waterfall enrichment:

You’re stacking multiple static data sources.

Sure, you get more coverage today. But tomorrow? Next week? Next month?

• Contacts change jobs
• Companies get funded
• Teams expand or contract
• Tech stacks shift
• Phone numbers change
• Emails bounce

Waterfall enrichment doesn’t solve the staleness problem. It just gives you stale data from multiple sources instead of one.

Con #5: Hidden costs add up

The “cost efficiency” pitch assumes perfect execution.

In reality:

• Engineering time to build and maintain
• Data cleanup from conflicts
• Compliance reviews
• Wasted credits from overlapping calls
• System downtime and debugging

Many teams discover their waterfall costs more than a single, comprehensive provider.

The streaming data alternative

Here’s what’s changing the game:

Instead of building waterfalls to stack static data, the best teams are streaming live data.

What is streaming data?

Traditional approach:

1. Pull data from database
2. Use it (even if it’s stale)
3. Wait for next refresh
4. Repeat

Streaming approach:

1. Data flows continuously
2. Updates happen automatically
3. Records stay fresh in real-time
4. Signals trigger actions as conditions change

Lusha is built for streaming.

How Lusha replaces waterfall complexity

Instead of chaining multiple static providers, Lusha streams verified data and live signals through your entire stack.

Coverage you can trust:

• 280M+ contacts
• 70M+ companies
• 95% verified emails
• 90% direct dials

Always-fresh data:

• Live lists that update automatically
• Real-time signals (job changes, funding, hiring surges)
• Continuous enrichment across your CRM
• No manual refreshes needed

Single integration:

• Native connectors: Salesforce, HubSpot, Outreach
• Automation platforms: Make, Zapier, n8n
• API for custom workflows
• Set up in hours, not weeks

Built-in intelligence:

• AI-powered account recommendations
• Lookalike targeting
• Signal-based triggers
• Automated routing and scoring

The difference in practice

Old way (waterfall with static data):

1. Export list from CRM
2. Send to Provider A for enrichment
3. Check gaps
4. Send gaps to Provider B
5. Merge results back to CRM
6. Hope data is still accurate
7. Repeat monthly

New way (streaming with Lusha):

1. Connect Lusha to your CRM once
2. Data enriches and updates automatically
3. Get notified when contacts change roles
4. Trigger plays when companies get funded
5. Lists update as buyer conditions change
6. Everything stays in motion

You spend time selling, not managing data pipelines.

When waterfall enrichment still makes sense

We’re not saying waterfall never has a place. There are specific scenarios where it’s still useful.

✅ Good use case: Specialized data beyond contacts

If you need data types beyond standard contact and company information:

• Deep technographic data (exact software versions, implementation dates)
• Proprietary firmographic data
• Industry-specific signals not available publicly

…then layering specialized providers on top of your contact data makes sense.

Lusha’s take: We provide verified contacts, company data, and live signals exceptionally well. If you need ultra-specialized data on top, that’s a valid add-on. But start with Lusha as your streaming foundation.

✅ Good use case: You already have no-code infrastructure

If you’re using:

• Clay (built for waterfall workflows)
• Census or Hightouch (reverse ETL with enrichment)
• Workato or Tray (iPaaS platforms)

…waterfall enrichment is a configuration, not a development project.

But ask yourself: Would streaming data from one provider eliminate the need for this complexity?

❌ Bad use case: Trying to fix a weak primary provider

This is the most common mistake.

Teams choose a cheap or incomplete provider, then build elaborate waterfalls to compensate.

The smarter approach: Start with comprehensive, streaming coverage from day one.

How to evaluate if you need waterfall

Here’s an honest test:

Step 1: Audit your current provider

Run 100 of your target accounts through your current data provider.

Check:

• Coverage percentage
• Email accuracy
• Phone accuracy
• How current the data is
• Compliance documentation

Step 2: Ask the hard questions

Is low coverage the real problem? Or is it:

• Data goes stale quickly?
• No way to know when contacts change jobs?
• No signals to trigger outreach timing?
• Manual refreshes eating up time?

If it’s staleness and signals you need, waterfall won’t fix it. Streaming will.

Is your team spending time on data plumbing?

• Building integrations?
• Maintaining enrichment workflows?
• Cleaning up conflicting data?
• Manually refreshing lists?

If yes, you don’t need more providers. You need automation.

Step 3: Test streaming data

Before building a waterfall:

1. Try a streaming data platform (like Lusha)
2. Connect it to your CRM
3. Let it run for two weeks
4. Compare coverage and freshness to your current setup

Most teams discover streaming data eliminates the need for waterfall entirely.

Bottom line: Streaming beats stacking

Waterfall enrichment made sense when all data was static.

But revenue doesn’t wait for batch updates. Buyers change. Markets shift. Opportunities emerge and disappear.

You can’t win with yesterday’s data—no matter how many providers you stack.

The future of B2B data isn’t waterfalls. It’s streams.

Streaming data means:

• Always-fresh contacts that update automatically
• Live signals that trigger actions in real-time
• One integration that keeps everything synced
• Less time managing data, more time selling

Waterfall means:

• Multiple static snapshots layered together
• Complex orchestration and maintenance
• Data conflicts and compliance risks
• Still dealing with staleness

Most revenue teams are realizing: It’s easier to stream from one source than stack from many.

Ready to test streaming vs stacking?

The fastest way to know if you need waterfall enrichment is to try streaming data first.

Here’s what to do:

1. Export 100 of your target accounts
2. Run them through Lusha
3. Check coverage (emails and phones)
4. Evaluate data accuracy
5. See how live signals work

If Lusha’s coverage is strong and data stays fresh automatically, you won’t need a waterfall.

Try Lusha free → Test on your actual target accounts.

See why revenue teams are choosing streaming over stacking.

What’s possible with streaming data

If you sell:

• Find and engage real buyers faster
• Spend less time researching
• Get notified when timing is right
• Keep your pipeline alive and moving

If you run RevOps:

• Keep every system synced automatically
• Route and score on verified, fresh data
• Cut enrichment costs while improving accuracy
• No more manual data management

One platform. One stream of data. Every GTM motion connected.

Lusha at a glance:

Verified coverage 280M+ contacts · 70M+ companies

Accuracy 95% verified emails · 90% direct dials

Real-time signals Job changes · Funding rounds · Hiring surges · Tech-stack shifts · Account growth

Compliance ISO 27701 · ISO 27001 · SOC 2 Type II · GDPR · CCPA

Integrations Salesforce · HubSpot · Outreach · Make · Zapier · n8n

Capabilities Enrichment · Automation · Signals · Continuous sync

Related reading:

• How to cut your lead response time from 20 to 2 minutes
• Sales streaming vs. sales seeking: how RevOps just became B2B’s most important team
• Bulk enrichment: why every list needs it before you use it