Waterfall data enrichment

How to implement a cascading data enrichment workflow

A cascading data enrichment workflow is a process that checks multiple data providers in a defined order to populate missing lead fields—such as work email, phone number, job title, company, industry, or LinkedIn URL—until the required fields are populated or all available providers have been exhausted.

The key idea is to enrich each field independently, stop looking for a field once you have a sufficiently reliable value, and continue checking providers for fields that are still missing.

For example, if you are enriching a lead using Provider A, Provider B, and Provider C:

• Provider A returns the lead's name and company but no email.
• Provider B returns a verified work email and job title but no phone.
• Provider C returns a phone number.
• The final record combines the best available values from all three providers without unnecessarily overwriting existing data.

1. Understand the workflow

Input lead

Name, company, LinkedIn URL, or email

Check existing fields

Identify which fields are missing, stale, or unverified

Provider A

Fill missing fields with acceptable results

(Recalculate remaining fields)

Fields still missing

Provider B → Provider C → …

Query only for fields that still need enrichment

Validate and merge

Resolve conflicts, record provenance, and preserve existing data

Enriched lead

Complete, partially complete, or marked for review

There are two important design decisions:

1. Field-level cascading: Each field can come from a different provider.
2. Sequential execution: A later provider is called only if earlier providers have not supplied acceptable values for the fields being enriched.

This is usually more efficient and reliable than querying every provider for every lead.

2. Define your provider order and enrichment rules

Start by deciding which provider should be queried first for each field. The order should reflect data quality, match rate, cost, and latency—not just the provider's overall reputation.

For example:

These are illustrative roles; assign your actual providers based on their capabilities and contract terms.

Configure the workflow separately for each field:

• Email: Stop when an acceptable, sufficiently verified work email is found.
• Phone: Continue until an acceptable phone number is found.
• Job title: Prefer the most recent, well-matched employment record.
• Company domain: Prefer an exact company match over a fuzzy name match.

This resembles the waterfall model used by platforms such as Apollo, where configured sources are checked in order and email and phone enrichment can be enabled independently.docs.apollo.io+1

3. Implement the cascading logic

The most maintainable approach is to represent the workflow as a configurable provider pipeline, rather than hard-coding a long series of API calls.

Here is an illustrative Python implementation:

from dataclasses import dataclass, field
from typing import Any

REQUIRED_FIELDS = {
    "work_email",
    "phone",
    "job_title",
    "company_domain",
}

@dataclass
class Lead:
    id: str
    fields: dict[str, Any]
    provenance: dict[str, str] = field(default_factory=dict)

def is_acceptable(field_name, value, confidence=None):
    if value is None or value == "":
        return False

    # Replace with field-specific quality checks.
    if field_name == "work_email":
        return (
            "@" in value
            and confidence in {"high", "verified"}
        )

    return True

def enrich_lead(lead, providers):
    missing = {
        name for name in REQUIRED_FIELDS
        if not lead.fields.get(name)
    }

    for provider in providers:
        if not missing:
            break

        # Each provider implements lookup(lead, fields).
        result = provider.lookup(lead, missing)

        for field_name in list(missing):
            value = result.get(field_name)
            confidence = result.get(
                f"{field_name}_confidence"
            )

            if is_acceptable(
                field_name, value, confidence
            ):
                lead.fields[field_name] = value
                lead.provenance[field_name] = (
                    provider.name
                )
                missing.remove(field_name)

    return {
        "lead": lead,
        "missing_fields": missing,
        "status": (
            "complete" if not missing
            else "partial"
        ),
    }

This code demonstrates the core algorithm. The provider.lookup() interface, provider-specific confidence scores, authentication, API error handling, and persistence are integration details you would implement for your chosen services.

What makes this work?

1. Calculate the missing fields before calling any provider.
2. Pass only those missing fields to the next provider.
3. Validate each returned value independently.
4. Store the provider responsible for each accepted field.
5. Remove successfully populated fields from the outstanding set.
6. Stop when the set is empty or the provider list is exhausted.

Important: Don't treat a nonempty field as automatically complete. An unverified email, a stale job title, or a low-confidence person match may still need another provider.

4. Build the workflow around field-level state

A useful production design tracks more than the lead's final values.

Example lead record

Illustrative data only. A production record should also retain timestamps, match confidence, and verification details where available.

I recommend tracking these properties per field:

• value
• source_provider
• status — missing, found, verified, rejected, or stale
• confidence
• last_verified_at
• attempted_providers
• updated_at

This lets you distinguish a genuinely verified field from one that was merely returned by a provider.

5. Handle conflicts, failures, and cost controls

A production workflow needs explicit rules for cases where enrichment is not straightforward.

A few particularly important safeguards:

• Never overwrite manually maintained CRM fields by default.
• Use exponential backoff for transient errors and a maximum retry count.
• Set a maximum provider spend per lead or batch.
• Use idempotency keys and deduplicate webhook events so retries don't cause duplicate updates.
• Keep an audit log of attempted providers and accepted values.

These are standard reliability considerations for API-driven enrichment pipelines.github.com+2

6. Decide whether to build your own pipeline or use an existing waterfall API

You don't necessarily need to implement the provider loop yourself.

Option A: Use Apollo's built-in waterfall enrichment

Best if you primarily need contact emails and phone numbers. Configure your data sources in order and call the people enrichment API with run_waterfall_email=true and/or run_waterfall_phone=true. Asynchronous results can be received by webhook or retrieved using the supported polling flow.github.com+3

Documentation:Apollo waterfall API guide

Option B: Use a workflow-based enrichment platform

Best if you want to orchestrate multiple providers with visual workflows, conditional branching, and CRM integrations rather than build the entire orchestration layer yourself.

Explore:Clay platform

Option C: Build a custom orchestration service

Best when you need complete control over field-level rules, provider selection, pricing, data ownership, validation, retries, and CRM updates.

Typical stack: Python or Node.js, a queue such as SQS or RabbitMQ, a relational database, provider API adapters, and a CRM integration.

7. Measure whether the cascade is working

Track the following metrics for every provider and for the workflow as a whole:

• Field completion rate: Percentage of requested fields successfully populated.
• Provider hit rate: Percentage of calls returning acceptable values.
• Cost per successful field: Total enrichment cost divided by accepted field values.
• Verification rate: Percentage of populated fields that pass validation.
• Latency: Time to complete enrichment, including asynchronous processing.
• Data freshness: Percentage of fields meeting your freshness requirements.

For example, if Provider A fills 60% of missing emails and Provider B fills 25% of the remaining 40%, the combined completion rate is:

60\% + (40\% \times 25\%) = 70\%

This makes it easier to decide whether another provider improves coverage enough to justify its cost.

Recommended implementation plan

1. Define your required lead fields and what counts as an acceptable value for each.
2. Assign providers an order for each field, based on quality, coverage, cost, and speed.
3. Implement provider adapters behind a common interface.
4. Build a field-level cascading loop that stops when each field meets its acceptance criteria.
5. Persist values alongside provenance, timestamps, and verification status.
6. Add retries, rate limits, cost ceilings, idempotency, and CRM overwrite protections.
7. Run a representative test batch and compare completion rates, accuracy, latency, and cost before scaling up.

One final design recommendation: Treat enrichment as a stateful job rather than a single synchronous API call. That makes the workflow easier to resume after failures, handle asynchronous provider responses, and re-enrich only the fields that remain incomplete.

To tailor this into an implementation you can actually deploy, what system are you building the workflow in?

1. Workflow platform

Clay

n8n / Make / Zapier

Custom Python or Node.js

CRM-native workflow

2. Enrichment providers

Apollo

Clay provider integrations

Multiple third-party APIs

Not selected yet

3. Where should enriched leads be saved?

Salesforce

HubSpot

Database or data warehouse

Other CRM

[Build my implementation plan]

Setting up a waterfall enrichment process involves querying provider A first, checking if the required data is returned, and if not, cascading down to provider B, then provider C, until your data fields are filled or your providers are exhausted.

Here is a clean, structured blueprint to build an efficient, cost-effective waterfall enrichment pipeline:

1. Define Your Data Schema & Priorities

• Field Mapping: Map out exactly what data points you need (e.g., email, phone number, company size, industry). Not all providers are good at everything; some excel at mobile numbers while others specialize in firmographics.
• Cost vs. Quality Hierarchy: Rank your providers based on accuracy and cost. Put your cheapest or highest-accuracy/lowest-cost provider first, and your most expensive or niche provider last (or vice versa, depending on your budget strategy).

2. Establish the Sequential Logic (The Waterfall Loop)

When a record (lead, contact, or account) enters your system, execute the following logic programmatically via an API, webhook, or automation tool (like Tray.io, Make, or Retool):

1. Step 1 (Trigger): A new or updated record enters the CRM (e.g., a new email is added).
2. Step 2 (Primary Query): Query Provider A using a unique identifier (like email or domain).
3. Step 3 (Evaluation - Pass/Fail): Check the response payload.
   • Did Provider A return the target fields (e.g., phone AND title)?
   • If Yes : Populate the CRM fields, log "Provider A" as the data source, and stop the waterfall to save credits.
   • If No (or partial match): Move to the next step.
4. Step 4 (Secondary Query): Query Provider B for the missing fields.
5. Step 5 (Repeat & Fallback): Continue down to Provider C if necessary. If all providers fail, tag the record as "Unenriched" or route it to a manual research queue.

3. Implement Merge & Conflict Resolution Rules

• Field-Level Fallback: Instead of wholesale rejecting a provider's entire payload because one field is missing, allow field-level cascading. If Provider A gives a great company size but no phone number, keep Provider A's company size and let Provider B fill in the phone number.
• Timestamp & Source Tracking: Always write the source name and enrichment date into hidden CRM fields (e.g., Last_Enriched_By, Enrichment_Date ). This helps you audit data quality and debug mismatches later.

4. Choose Your Implementation Route

• Out-of-the-Box Tools: Platforms like Clay or dedicated data orchestration tools natively support multi-step waterfall integrations without writing custom code.
• Custom Middleware (Serverless/Python/Node.js): If building in-house, set up a webhook receiver (e.g., AWS Lambda) that calls provider APIs sequentially using async/await logic, then pushes the consolidated JSON payload to your CRM via API.

If you'd like, let me know:

• What CRM you are using (HubSpot, Salesforce, etc.)
• What providers you plan to use
• Whether you want to build this using native CRM workflows , a no-code tool like Clay , or custom code

I can give you a more specific architectural layout or code snippet.