Data Action mapping fails: /api/v2/conversations vs /api/v2/analytics/conversations payload structure

I’m completely stumped as to why my Data Action JSON transform keeps failing when I switch the source endpoint from the real-time conversation API to the analytics one. I am building a historical reporting pipeline that needs to enrich conversation metadata before pushing it to our data warehouse.

I started with /api/v2/conversations because it was easier to debug in Postman. The payload structure is straightforward. I mapped the to and from fields using standard JSONPath expressions. It worked perfectly for live data. However, when I switched the HTTP GET request in the Data Action to /api/v2/analytics/conversations/summary/interval, the response object changed completely. The nested media objects are gone. Instead, I get aggregated metrics.

My current JSON transform looks like this:

{
 "conversationId": "$.id",
 "participants": "$.to",
 "duration": "$.metrics.duration"
}

When pointing to the analytics endpoint, $.to returns null. I understand that analytics returns aggregated data, but I need the participant details for the specific interval. I tried adding groupBy=conversationId to the query params, hoping to get a flattened list of participants per conversation, but the response still lacks the detailed contact info I need for the mapping.

Is there a way to get participant-level granularity from the analytics endpoint without hitting the rate limits on /api/v2/conversations? Or am I stuck writing a secondary lookup step in my Data Action to fetch details for every ID returned by the analytics query? The latency is killing my pipeline efficiency. I need a clean JSON mapping that works for historical data without chaining five separate API calls.

Have you tried isolating the schema mismatch by validating the raw JSON against the OpenAPI specification for both endpoints before passing it to the Data Action? The real-time /api/v2/conversations endpoint returns a flattened structure where participant details are often nested differently than the analytics /api/v2/analytics/conversations/summary endpoint. In my experience with token vault rotation, I often see similar “silent” failures where the payload shape changes but the error message remains generic. You need to explicitly map the participants array from the analytics response, which is an array of objects, to the expected input format of your downstream action. The analytics payload includes participant_id and role fields that do not exist in the real-time to/from objects. If your Data Action expects a simple string for the caller ID, it will reject the complex analytics object.

Try using a JSONata expression in your Data Action to flatten the analytics participant data. Instead of mapping the whole object, extract the specific fields you need. Here is a working example for mapping the caller’s phone number from the analytics response:

{
 "caller_id": $if($exists(participants[?role='agent'][0].routing_data.phone_number), participants[?role='agent'][0].routing_data.phone_number, null),
 "conversation_id": id
}

This approach ensures that even if the analytics schema adds new nested fields in future API versions, your transformation remains robust. I also recommend adding a validation step in your pipeline that checks for the presence of routing_data before attempting the map. This prevents null pointer exceptions that often trigger 500 errors in the Data Action engine. Check the OAuth scopes on the service account too; analytics endpoints require analytics:read which is separate from conversation:read. Missing this scope can sometimes return a truncated payload that looks like a schema error.

According to the docs, they say that the Analytics endpoint returns aggregated summary data, which strips out the granular participant arrays found in the real-time conversation API. When I was debugging a similar webhook ingestion issue in my Rails middleware, I realized the Data Action was failing because it expected a participants array that simply does not exist in the /api/v2/analytics/conversations/summary response. The summary endpoint provides wrap_up_code and duration but lacks the to and from contact details needed for enrichment. You need to switch your data source or use the Conversation API for raw data, then process it asynchronously. Here is how I structure the Faraday request to ensure I get the full participant object for mapping:

conn = Faraday.new(url: 'https://mydomain.mygen.com/api/v2/conversations')
response = conn.get do |req|
 req.headers['Authorization'] = "Bearer #{access_token}"
 req.params['pageSize'] = 1
end
participants = response.body['conversations'].first['participants']

Check these related concepts:

• Real-time vs Historical API schema differences
• Data Action JSON transform syntax
• Participant object structure in Genesys Cloud
• Webhook payload flattening techniques

The quickest way to solve this is to handle the schema divergence in Terraform before the Data Action even runs.

locals {
 # Normalize the structure for the Data Action input
 normalized_participants = var.source == "analytics" ? [for p in data.genesyscloud_analytics_conversations.summary[0].participants : { id = p.id, name = p.name }] : data.genesyscloud_conversations.conversation[0].participants
}

If I remember correctly…
Cause: analytics endpoints are cached so they lag during k6 spikes. try the websockets api instead for real-time data…
Solution: use genesyscloud.dataaction in Terraform to map the schema difference before the action runs.