Implementing Batch Identity Resolution Jobs for Historical Data Cleanup and Consolidation

How-To-Articles
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Implementing Batch Identity Resolution Jobs for Historical Data Cleanup and Consolidation

What This Guide Covers

This guide details the architecture and execution of batch identity resolution workflows using Genesys Cloud CX APIs to consolidate fragmented customer profiles from legacy systems. You will build a process that ingests historical data, resolves duplicate identities via deterministic and probabilistic matching, and updates the unified customer view in your CRM or data warehouse. The end result is a clean, deduplicated dataset that ensures accurate historical analytics and prevents future identity fragmentation.

Prerequisites, Roles & Licensing

  • Licensing: Genesys Cloud CX Standard or higher. Identity Resolution features are available in all tiers, but advanced matching rules may require CX 3 or specific add-ons depending on your contract.
  • Permissions:
    • Identity > Identity Resolution > Edit
    • Data > Data Connector > Edit (if using Data Connectors for ingestion)
    • Admin > System > Edit (for API credential management)
    • CRM > [Your CRM] > Edit (if updating records directly via CRM integration)
  • OAuth Scopes: identity:resolution:write, identity:resolution:read, data:connector:write.
  • External Dependencies: A source system containing historical data (e.g., SQL database, SFTP drop, or legacy CRM export) and a target system for the resolved identities (e.g., Salesforce, SAP, or a Data Lake).

The Implementation Deep-Dive

1. Designing the Identity Resolution Model and Matching Rules

Before processing data, you must define how Genesys determines that two records represent the same person. In a batch context, this is critical because the system cannot rely on real-time phone context or session cookies. You must configure Matching Rules within the Identity Resolution module.

The architecture relies on a hierarchy of match types:

  1. Deterministic Matches: Exact matches on high-confidence fields (e.g., Email, Phone Number, Government ID). These have the highest weight and usually trigger an immediate merge.
  2. Probabilistic Matches: Weighted scoring based on fuzzy logic (e.g., Name + Address similarity). These require a threshold score to trigger a merge.

Configuration Steps:
Navigate to Admin > Identity > Identity Resolution > Matching Rules. Create a new rule set specifically for your batch job to avoid impacting real-time interactions.

The Trap: Do not apply overly aggressive probabilistic thresholds (e.g., >80% confidence) for batch jobs without extensive testing. In historical data, addresses are often outdated or incomplete. A probabilistic match on “John Smith” at “123 Main St” and “John Smith” at “125 Main St” might incorrectly merge two different households. This leads to “phantom profiles” where customer histories are blended, corrupting analytics.

Architectural Reasoning: We isolate batch matching rules from real-time rules because batch jobs process millions of records. Real-time rules need sub-millisecond latency and high precision. Batch rules can afford higher latency but must prioritize recall to ensure no duplicates are missed. By separating them, you prevent a heavy batch job from degrading the performance of live IVR interactions.

Example Matching Rule Configuration (JSON Payload):

POST /api/v2/identity/resolution/matchingrules
{
  "name": "Batch_Historical_Cleanup_Rule",
  "description": "Deterministic match on Email and Phone for legacy data ingestion",
  "matchType": "DETERMINISTIC",
  "fields": [
    {
      "name": "email",
      "transformations": ["trim", "lowercase"]
    },
    {
      "name": "phone",
      "transformations": ["normalize_e164"]
    }
  ],
  "priority": 10
}

2. Ingesting Historical Data via Data Connectors or API

Genesys Cloud provides two primary mechanisms for batch identity ingestion: Data Connectors (for continuous or scheduled pulls) and the Identity Resolution API (for one-off bulk imports). For a one-time historical cleanup, the API approach offers more control over error handling and transaction management.

You will use the POST /api/v2/identity/resolution/identities endpoint. However, calling this endpoint record-by-record for millions of rows is inefficient and risks hitting rate limits. You must implement a chunked upload strategy.

Implementation Strategy:

  1. Extract data from your legacy source.
  2. Normalize fields (e.g., ensure all phones are E.164, emails are lowercase).
  3. Split the dataset into chunks of 100–500 records.
  4. Use an asynchronous worker process to post each chunk.

The Trap: Sending chunks larger than 500 records increases the likelihood of partial failures. If a chunk fails, the entire batch of 500 records is rejected. Smaller chunks allow for granular retry logic. Furthermore, failing to normalize data before ingestion causes the Identity Resolution engine to treat “john@example.com” and “John@example.com” as distinct identities if the matching rule does not explicitly include a lowercase transformation. Always normalize at the source or in the ingestion script, not solely in the matching rule.

Architectural Reasoning: We use the Identity Resolution API instead of direct CRM writes because the Identity Resolution engine acts as the source of truth for identity logic. If you write directly to Salesforce, you bypass Genesys’s deduplication logic, creating duplicates that are expensive to clean up later. By pushing data through the Identity Resolution API, Genesys automatically checks for existing identities, merges if a match is found, or creates a new identity if unique. This ensures consistency across all channels (Voice, Digital, Email).

Example Ingestion Script (Python/Pseudo-code):

import requests
import json
import time

GENESYS_BASE_URL = "https://api.mypurecloud.com"
ACCESS_TOKEN = "your_oauth_token"

def send_chunk(identities_chunk):
    headers = {
        'Authorization': f'Bearer {ACCESS_TOKEN}',
        'Content-Type': 'application/json'
    }
    
    payload = {
        "identities": identities_chunk
    }
    
    response = requests.post(
        f"{GENESYS_BASE_URL}/api/v2/identity/resolution/identities",
        headers=headers,
        json=payload
    )
    
    if response.status_code == 200:
        return response.json()
    else:
        # Implement exponential backoff here
        time.sleep(2)
        raise Exception(f"Failed to ingest chunk: {response.text}")

# Example Identity Object
identity_record = {
    "attributes": {
        "email": "customer@example.com",
        "phone": "+15551234567",
        "first_name": "Jane",
        "last_name": "Doe",
        "source_system": "legacy_crm_v1"
    }
}

3. Handling Merge Conflicts and Attribute Prioritization

When a new historical record matches an existing identity, a merge occurs. The critical decision is: Which attribute value wins? If the legacy record has an old email address, and the current Genesys identity has a new one, which one persists?

Genesys uses Attribute Prioritization rules. You must define which data source is authoritative for each field. For historical cleanup, you often want the newest data to win, or you may want to retain specific legacy data for audit purposes.

Configuration Steps:
Navigate to Admin > Identity > Identity Resolution > Attribute Prioritization. Create a rule that defines the hierarchy. For example:

  • email: Priority 1 = Real-time Interaction, Priority 2 = CRM, Priority 3 = Legacy Batch Import.

The Trap: Setting the batch import source as the highest priority for all fields will overwrite current, valid customer data with stale historical data. This is a catastrophic error that can break current workflows (e.g., sending emails to invalid addresses). Always set the batch import source as the lowest priority unless you are explicitly correcting known bad data.

Architectural Reasoning: We separate merge logic from ingestion logic. The ingestion job simply submits data. The Identity Resolution engine handles the merge based on predefined policies. This decoupling allows you to change prioritization rules without modifying the ingestion code. It also ensures that if you run the batch job multiple times (idempotency), the result is consistent because the prioritization rules dictate the outcome regardless of execution order.

Example Attribute Prioritization Rule:

POST /api/v2/identity/resolution/attributepriorities
{
  "name": "Batch_Import_Low_Priority",
  "attributeName": "email",
  "priorities": [
    {
      "sourceSystem": "genesys_cloud",
      "priority": 1
    },
    {
      "sourceSystem": "salesforce",
      "priority": 2
    },
    {
      "sourceSystem": "legacy_crm_v1",
      "priority": 10
    }
  ]
}

4. Validating and Exporting Resolved Identities

After ingestion, you must verify the results. Genesys provides the GET /api/v2/identity/resolution/identities endpoint to query resolved identities. However, for large datasets, you should use Data Connectors to export the resolved identities to a staging area for validation.

Implementation Strategy:

  1. Create a Data Connector profile that reads from the “Identity Resolution” data source.
  2. Configure a filter to only include identities updated during the batch window (e.g., lastUpdated > [batch_start_time]).
  3. Export the data to a CSV or JSON file in an SFTP location or AWS S3 bucket.
  4. Compare the exported data with the original source to calculate merge rates and duplicate elimination metrics.

The Trap: Failing to filter by lastUpdated time will result in exporting the entire identity database, which can be terabytes of data. This causes timeout errors and unnecessary data transfer costs. Always scope your exports to the specific batch window.

Architectural Reasoning: We use Data Connectors for export rather than polling the API because connectors are optimized for large data transfers and handle pagination automatically. Polling the API for millions of records requires complex pagination logic and is prone to missing records if new data is added during the export process. Connectors provide a snapshot-in-time view, which is essential for accurate reconciliation.

Example Data Connector Export Configuration:

POST /api/v2/data/connector/profiles
{
  "name": "Batch_Identity_Export",
  "type": "SFTP",
  "source": {
    "type": "IdentityResolution",
    "filter": {
      "field": "lastUpdated",
      "operator": "greaterThan",
      "value": "2023-10-01T00:00:00Z"
    }
  },
  "destination": {
    "host": "sftp.yourcompany.com",
    "username": "export_user",
    "password": "secure_password",
    "path": "/exports/identity_cleanup/"
  }
}

Validation, Edge Cases & Troubleshooting

Edge Case 1: The “Silent Fail” on Partial Matches

The Failure Condition: The batch job reports success, but historical data is not merged as expected. The identity count increases instead of decreasing.
The Root Cause: The matching rules are too strict. For example, if you require an exact match on phone and email, but the legacy data has only phone, no merge occurs. The system creates a new identity with the phone number, leaving the original identity untouched.
The Solution: Review the matching rule logs in Admin > Identity > Identity Resolution > Logs. Enable debug logging for the batch job to see which rules were evaluated and why they failed. Relax the deterministic rules to include probabilistic matching for fields with high missing data rates.

Edge Case 2: Rate Limiting and Throttling

The Failure Condition: The ingestion script throws 429 Too Many Requests errors after processing a few thousand records.
The Root Cause: Genesys Cloud APIs have rate limits (typically 100–200 requests per minute per tenant, depending on your tier). Sending chunks too quickly triggers throttling.
The Solution: Implement exponential backoff in your ingestion script. When a 429 error is received, wait for the Retry-After header value (or a default of 1–5 seconds) before retrying. Additionally, monitor the Usage tab in Admin to track API consumption in real-time.

Edge Case 3: Circular Merge References

The Failure Condition: The system throws a 500 Internal Server Error during ingestion with a message about “circular reference” or “merge loop.”
The Root Cause: This is rare in batch jobs but can occur if you are manually manipulating identity IDs and creating a scenario where Identity A merges into Identity B, which merges into Identity A.
The Solution: Ensure your ingestion script does not attempt to merge identities that are already in a merge state. Use the GET /api/v2/identity/resolution/identities/{identityId} endpoint to check the status of an identity before attempting a merge. If an identity is marked as Merged, do not include it in the batch upload.

Official References