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Genesys Cloud data integration and reporting

Your Genesys Cloud data in SQL Server, ready for reporting and Power BI

Genesys Cloud is an excellent contact-centre platform and a frustrating reporting source. Retention limits, export caps and dashboards that never quite match finance. We use the Genesys Cloud Platform API to pull the raw data into Microsoft SQL Server or Azure, on a schedule, with full history, so your analysts can finally answer their own questions.

  • ConversationsEvery interaction, segment, participant and metric
  • Queues & routingObservations, skills, wrap-up codes, service level
  • AgentsPresence, routing status, activity, adherence
  • QualityEvaluations, scores, surveys, audit trail

What we extract

What we extract with the Genesys Cloud Platform API

Conversation details, complete and historical

The conversation detail model is the heart of Genesys reporting and the hardest to extract well. We query it in ordered, paged intervals, use asynchronous jobs for large back-fills and reconcile late-arriving segments so that yesterday's numbers do not change next week.

  • /api/v2/analytics/conversations/details/query for incremental loads
  • /api/v2/analytics/conversations/details/jobs for multi-month history
  • Participants, sessions, segments and metrics normalised into relational tables

Queue, agent and interval aggregates

Service level, abandon rate, handle time, occupancy and adherence per interval, per queue, per agent, per media type. Stored at the granularity you choose so that a dashboard for the floor and a monthly board report come from the same table.

  • /api/v2/analytics/conversations/aggregates/query and /users/aggregates/query
  • /api/v2/analytics/queues/observations/query for near real-time views
  • User presence and routing-status details for adherence and shrinkage

Configuration, quality and audit data

Reporting is only trustworthy when queue names, wrap-up codes, skills and divisions are joined from the source, not typed into a spreadsheet. We keep the reference data in sync and version it, so a renamed queue does not break last year's report.

  • Users, divisions, queues, skills, wrap-up codes and schedules
  • Quality evaluations and surveys via /api/v2/quality/evaluations/query
  • Speech and text analytics: sentiment, topics and transcripts per conversation
  • Audit events for compliance and change tracking

Landed in SQL Server, modelled for BI

Raw JSON is kept for traceability, then flattened into a documented star schema in SQL Server, Azure SQL or Managed Instance. Power BI, Microsoft Fabric, Excel and your existing data warehouse can all read it. You own the database, the model and the history.

-- a typical slice of the delivered model
genesys.Conversation        (ConversationId, ConversationStart, ConversationEnd, DivisionId, ...)
genesys.Participant         (ConversationId, ParticipantId, Purpose, UserId, QueueId, ...)
genesys.Segment             (ParticipantId, SessionId, SegmentStart, SegmentEnd, SegmentType, WrapUpCode, ...)
genesys.ParticipantMetric   (ParticipantId, MetricName, MetricValue, Emitted, ...)
genesys.QueueIntervalAgg    (QueueId, MediaType, IntervalStart, nOffered, nAnswered, tHandle, ServiceLevel, ...)
genesys.Dim_User / Dim_Queue / Dim_WrapUpCode / Dim_Division   -- slowly changing, versioned

Delivered with a data dictionary and example Power BI measures.

How the pipeline runs

From OAuth client to trusted table

Every step below is one we have solved before. The order is what keeps the data consistent.

  1. Access with least privilege

    An OAuth client-credentials grant in your Genesys Cloud organisation, scoped to the analytics, routing, users and quality permissions the extraction actually needs. No agent licences, no admin roles. How we secure access to your environment.

  2. Historical back-fill

    Asynchronous analytics jobs pull the history in chunks that respect the API's interval and page limits. Throughput is throttled to stay under your organisation's rate limit, so live operations are never affected.

  3. Incremental loads

    Hourly or nightly runs query only what changed, with a look-back window for late-arriving segments and re-evaluated conversations. HTTP 429 and 5xx responses are retried with back-off and logged.

  4. Land, model, document

    Raw JSON into a staging schema, then merged into a versioned relational model in SQL Server or Azure. Reference data is kept as slowly changing dimensions. Everything is documented in a data dictionary.

  5. Report and monitor

    Power BI, Fabric or your warehouse read from curated views. The pipeline itself is monitored: run status, row counts and freshness alerts go to the same on-call team that watches your databases.

The person behind the practice

Ajay Kumar, Genesys Cloud CX architect and developer

Every Genesys engagement at DatabaseOnline is led by an engineer who holds the certifications for the work. All of them are issued by Genesys and can be verified on Credly.

Ajay Kumar

Ajay Kumar

Genesys Cloud CX architect · full-stack developer

Ajay brings more than fifteen years of web and mobile application development to the Genesys practice. He holds Genesys Cloud CX certifications across Architect, Scripting, Developer, contact centre administration, Quality Management, Workforce Management, Outbound and, since September 2026, Speech and Text Analytics, plus the AI suite: Digital Bots and Knowledge, Predictive Engagement and Predictive Routing.

Before Genesys he built and ran e-commerce, rental, grocery, hospitality and travel platforms with REST APIs and secure admin portals, working full-stack in HTML5, JavaScript, jQuery, Angular, Node.js, PHP and WordPress. That combination is why our Genesys integrations are built like software, not stitched together from scripts.

Verify his credentials on Credly

Skill areas

  • Architect: call flows, IVR, routing strategies
  • Scripting: agent scripts, external data, callbacks
  • Platform API: OAuth, Analytics, Conversations, Notifications, SDKs
  • Data Actions and integrations
  • Contact centre administration: ACD, roles, divisions, wrap-up codes
  • Quality Management: evaluations, calibration, recording policies
  • Workforce Management: forecasts, schedules, adherence
  • Outbound: campaigns, dialing modes, DNC lists
  • Speech and Text Analytics: transcription, sentiment, topic spotting
  • AI: Digital Bots, Knowledge Workbench, Predictive Engagement and Routing
  • Reporting and analytics: queue, agent and outbound reports
  • Implementation: telephony, Edge, trunks, licensing
  • Full-stack: HTML5, JavaScript, Angular, Node.js, PHP, WordPress

Genesys credentials, issued by Genesys and verified on Credly

Results

What it looks like when it works

Contact centreBelgiumGenesys Cloud, 450 agents

Genesys dashboards and finance never agreed. Now they read from the same tables.

Situation
Service-level and handle-time figures differed between the Genesys Cloud dashboards, a monthly export and the finance model. History was limited to what the export tool could reach.
What we did
Platform API extraction of conversation details and queue aggregates into Azure SQL, a documented star schema with one agreed set of definitions in SQL, Power BI on top and the pipeline itself under 24x7 monitoring.
  • 3 yrsof conversation history queryable, from 30 days before
  • 14 minnightly load of 1.2 million segments, rate limits respected
  • 1set of KPI definitions shared by operations and finance
"The argument about whose number is right is over. It is in the database."Head of customer operations, contact centre

Why teams call us

The problems we get asked to solve

Why do our Genesys Cloud reports not match our finance numbers?

Almost always a definitions problem: which interval boundary, which time zone, whether transfers count once or twice, whether a conversation is attributed to the first or last queue. With the raw detail data in your own database, the definitions become explicit SQL that both sides can read and agree on.

How can we keep more history than Genesys Cloud retains?

Once the data is in your database, retention is your decision. We back-fill what the API still exposes and keep everything from that day forward.

Why do our own scripts hit Genesys Cloud API rate limits?

The Platform API is generous but not infinite, and analytics queries have interval and paging limits that are easy to trip over. We run asynchronous jobs where they belong, throttle where needed and reconcile automatically.

Can Genesys Cloud data be joined to CRM, ERP or workforce data?

That is exactly why it belongs in SQL Server. Joining conversations to customers, orders or schedules becomes a query, not an export.

Can we use Genesys Cloud data in Power BI?

Yes. Once the data is in SQL Server or Azure SQL, Power BI reads it like any other database: scheduled refresh, your own measures and no API limits at report time. We deliver the model with a data dictionary and example Power BI measures.

Can you also build scripts and integrations inside Genesys Cloud?

Yes. Ajay Kumar holds the Genesys Cloud CX Scripting and Developer certifications, which cover agent scripts, data actions and integrations that push or pull data during a conversation, for example a screen pop from your SQL Server CRM data.

Talk to a Genesys data specialist

Tell us which report you cannot build today.

We will tell you which endpoints hold the data, how long the back-fill takes and what it costs to keep it flowing.

Contact DatabaseOnline