[CX-DATA / Analytics & Systems]

CX Analytics Lab

Designed to replace one-off reporting with metrics that can be traced back to a reproducible event model.

A reproducible analytics layer for customer-operations events, metrics, and quality checks.

Status
[planned]
My role
Data model / pipeline / quality / operational analytics
Evidence model
Tests / CI / runtime proof

01

Events

Synthetic CX data

02

Normalize

Typed model

03

Metrics

SQL / Python

04

Quality

Validation gates

05

Insight

Operational signal

Live system traceMedia capture pending

[01 / Target impact]

What changes for the business.

Target: consistent definitions for FRT, CSAT, handling time, Cases, and Messaging.

Target: a synthetic pipeline that is safe to publish and straightforward to reproduce.

Target: an analytics layer that helps explain what changed, where, and why.

[02 / PROOF]

What is already verified.

    01Scope defined for Cases, Messaging, events, agents, CSAT, FRT, and handling time.
    02Shares the same synthetic operations universe as the AI and Salesforce flagships.

[03 / UNDER THE HOOD]

Architecture, controls, and implementation.

Technical detail lives here so the business story stays readable first.

Architecture
Python / SQL / synthetic events / data-quality gates / visualization
Control boundary
Synthetic data only. Metric definitions and transformations must remain reproducible.
Role
Data model / pipeline / quality / operational analytics
Next slice
Build the synthetic event model and first reproducible pipeline.

System boundaries

  • — No employer data.
  • — No proprietary operational reporting.
  • — Every published metric requires a reproducible definition and transformation path.

Technology surface

PythonSQLData modelingQuality testsVisualization
System index