Case study 03
Data Quality & Reliability
Report → Source → Trusted
Tracing inconsistencies across data sources, analytical models, and business metrics to make reporting more trustworthy.
01Context
Analytics depends on upstream systems, business rules, analytical models, and reporting layers. An unexpected metric does not always mean the dashboard is wrong — it may come from backend behavior, source data, missing records, status differences, logic changes, transformations, or delayed data.
02Challenge
The challenge is identifying where the discrepancy actually begins.
03My role
Led and supported data investigation and validation initiatives across analytics and source systems:
- Investigated inconsistencies across backend logs and data sources
- Validated business logic and KPI definitions
- Reconciled outputs across systems
- Checked analytical models and reporting results
- Built automated data-quality checks
- Caught unexpected changes before they silently affected reporting
- Worked with technical teams to validate findings and support fixes
Across these investigations, quality-check logic helped uncover 20+ inconsistencies across backend logs and data sources.
04Signal tracing
A wrong number is a symptom. The cause is somewhere upstream.
Every investigation follows the business event backward — from report to model to logic to source — until the layer that diverges is isolated, fixed, and validated.
Something looks wrong
- Trusted KPI / Report
- Analytical model
- Transformation / Business logic
- Source data
- Backend / System event
Conceptual · one signal traced from the report back toward its source.
05Quality checks
Checked across the analytical chain — not only at the final dashboard.
Source
Availability, missing records, unexpected values
Model
Logic consistency, transformations, reconciliation
KPI
Definition consistency, unexpected metric shifts
Reporting
Output validation, downstream reliability
Freshness
Expected data availability and timing
06Example investigation
From “this looks off” to a check that catches it next time.
Conceptual representation of the process
- 01Reported KPI looks inconsistent
- 02Compare reporting output with the analytical model
- 03Reconcile the model with source / system records
- 04Identify where expected and actual behavior diverge
- 05Validate business logic with relevant teams
- 06Confirm the fix
- 07Add or improve a preventive quality check
07Approach
Detect, trace, and protect.
01
Detect
Spot unexpected values, missing data, metric shifts, or inconsistencies.
02
Trace
Follow the issue back across reporting, models, source data, and system behavior.
03
Reconcile
Compare representations of the same business event and determine the expected logic.
04
Validate
Confirm the root cause and verify the corrected behavior or output.
05
Protect
Turn recurring failure patterns into automated checks, freshness validation, and monitoring rules.
08Impact
Data inconsistencies uncovered across backend logs and data sources
Built across data sources, analytical models, KPIs, and reporting outputs
Issues identified and investigated before becoming trusted business numbers
Distinguishing whether discrepancies came from source systems, business logic, analytical models, or reporting
09What this demonstrates
Reliable analytics requires more than producing the correct query.
It requires understanding how the business event moves through the system, questioning unexpected results, tracing issues to their source, and building safeguards around the metrics people depend on.
Tools & capabilities