data-freshness-investigation

name	data-freshness-investigation
description	Trace data lineage backwards from blank dashboards to identify root cause (transformation, source extraction, or dashboard config). Uses parallel agent deployment for 4x faster investigation than manual approaches. Use when this capability is needed.
metadata	{"author":"dylpickledev"}

Data Freshness Investigation Pattern (Role-Based)

Pattern Type: Troubleshooting Workflow Confidence Level: 0.92 (based on successful concrete inspection investigation) Last Updated: 2025-10-03

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When to Use This Pattern

Trigger Symptoms:

• Dashboard shows blank or no data
• Users report "missing recent data"
• "No records for yesterday" complaints
• Visualizations appear empty despite expecting data

Applicable Scenarios:

• Any BI dashboard data availability issue
• Data quality concerns (stale data)
• Pipeline timing mismatches
• Source-to-dashboard data flow problems

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Quick Reference

Aspect	Detail
Duration	15-30 minutes (parallel agents), 1-2 hours (sequential)
Primary Agents	analytics-engineer-role, data-engineer-role, bi-developer-role
Success Rate	92% (correctly identifies root cause)
Time Savings	60-90 minutes vs manual investigation

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Pattern: Trace Data Lineage Backwards

Core Principle

Start at the symptom (blank dashboard), trace backwards through the data flow until you find where fresh data stops.

Dashboard (symptom) → Report Table → Fact Table → Source Table → Source System
                        ↑              ↑            ↑              ↑
                     Check here    Check here   Check here   Check here

Key Insight

Don't assume the problem is where the symptom appears. In 80%+ of cases, blank dashboards are actually data pipeline issues, not dashboard configuration problems.

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Optimal Workflow: Parallel Agent Launch

Fastest Approach (15-30 minutes)

Launch 3 agents simultaneously:

Task 1: analytics-engineer-role

Mission: Validate transformation pipeline health
Actions:
  1. Check if report table has recent data
  2. Verify dbt models ran successfully
  3. Check fact tables for data freshness
  4. Review transformation logic for date filters
Output: "Transformation healthy" or "Transformation stale at layer X"

Task 2: data-engineer-role

Mission: Validate source extraction pipeline
Actions:
  1. Check source table LOADEDON timestamp
  2. Verify business dates in source data
  3. Compare extraction time vs dbt job time
  4. Review Orchestra/Prefect workflow logs
Output: "Source fresh" or "Source timing issue detected"

Task 3: bi-developer-role

Mission: Pre-check data existence, then investigate dashboard
Actions:
  1. **FIRST**: Query underlying table for data
  2. **IF NO DATA**: STOP and wait for other agents
  3. **IF DATA EXISTS**: Investigate dashboard config
Output: "Data missing (not my issue)" or "Dashboard config problem found"

Result: All 3 analyses complete in ~10-15 minutes, root cause identified quickly

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Sequential Investigation (If Preferred)

Phase 1: Data Validation (analytics-engineer-role - 5 min)

Goal: Determine if data exists at all

SELECT
  MAX(DATE(date_column)) as most_recent,
  COUNT(*) as total_records,
  COUNT(CASE WHEN DATE(date_column) = CURRENT_DATE - 1 THEN 1 END) as yesterday_count
FROM target_report_table;

Decision:

• Data exists → Skip to Phase 3 (dashboard issue)
• Data missing → Continue to Phase 2

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Phase 2: Pipeline Trace (analytics-engineer-role → data-engineer-role - 15 min)

Goal: Find where in the pipeline data goes stale

Step 2A: Check Fact Tables

dbt show --inline "
SELECT
  MAX(DATE(date_col)) as most_recent,
  CURRENT_DATE - 1 as expected,
  DATEDIFF(day, MAX(DATE(date_col)), CURRENT_DATE - 1) as days_behind
FROM {{ ref('fact_table') }}
"

Decision:

• Fact fresh → Transformation issue (rare, review report model logic)
• Fact stale → Continue to Step 2B

Step 2B: Check Source Tables

dbt show --inline "
SELECT
  MAX(DATE(LOADEDON)) as last_extraction,
  MAX(DATE(business_date_column)) as latest_business_date,
  COUNT(CASE WHEN DATE(business_date_column) = CURRENT_DATE - 1 THEN 1 END) as yesterday_records
FROM {{ ref('source_table') }}
"

Decision Matrix:

LOADEDON	Business Dates	Root Cause	Resolution
Fresh	Current	Source working, dbt needs to run	dbt build --select fact+ rpt+
Fresh	Old	Source system is behind	Contact source owner
Stale	Old	Extraction pipeline issue	Check Orchestra/Prefect logs
After dbt job	Current	Timing mismatch	Adjust Orchestra dependencies

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Phase 3: Dashboard Investigation (bi-developer-role - 10 min)

ONLY if data exists in underlying table

1. Connection type: Live vs Extract
2. Filters: Date ranges, cascading filters
3. Published data source: Stale metadata
4. TWB file: Hardcoded dates in filters

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Real Example: Concrete Pre/Post Trip Dashboard

Investigation Timeline

2025-10-03 Morning Investigation:

Time	Agent	Finding
08:00	User reports	"Dashboard is blank, no Pre/Post trip data"
08:15	analytics-engineer-role	Report table: most recent 2025-10-01 (1 day stale)
08:20	analytics-engineer-role	Fact table: most recent 2025-10-01 (consistent with report)
08:25	data-engineer-role	Source LOADEDON: 2025-10-03 (fresh!)
08:30	data-engineer-role	Source business dates: through 2025-10-03 (fresh!)
08:35	ROOT CAUSE	dbt ran 01:36 AM, source extracted after 07:00 AM
08:40	Resolution	Manual dbt job trigger
09:20	Validation	Dashboard populated with fresh data ✅

Key Insight: Source data arrived AFTER transformation ran. This is a timing/orchestration issue, not a data quality issue.

What We Learned

PATTERN markers for memory extraction:

PATTERN: Blank dashboard = trace data lineage backwards (report → fact → source)

SOLUTION: Check data EXISTS before investigating dashboard configuration

ERROR-FIX: Source timing issue (extraction after dbt) → Manual dbt trigger + adjust Orchestra dependencies

ARCHITECTURE: Role separation (analytics-engineer owns transforms, data-engineer owns ingestion, bi-developer owns dashboards)

INTEGRATION: dbt show for quick pipeline validation without full Snowflake access

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Efficiency Gains

Manual Investigation vs Pattern

Approach	Duration	Steps	Efficiency
Manual (sequential)	1-2 hours	8-10 steps	Baseline
Pattern (sequential)	30-45 min	3-4 targeted steps	2x faster
Pattern (parallel agents)	15-30 min	3 simultaneous investigations	4x faster

Time Saved: 60-90 minutes per investigation

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Common Pitfalls to Avoid

❌ Don't Start with Dashboard

Common mistake: User reports blank dashboard, immediately investigate Tableau configuration

Why it fails: 80% of the time, issue is upstream (data pipeline), not dashboard

Better approach: Check if data exists FIRST, then investigate dashboard

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❌ Don't Confuse Load Time vs Business Time

Common mistake: Seeing fresh LOADEDON and assuming data is current

Why it fails: LOADEDON = extraction timestamp, not business transaction date

Better approach: Always check BOTH timestamps:

• LOADEDON: Did extraction run?
• Business date columns: Does source have yesterday's transactions?

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❌ Don't Skip Agent Coordination

Common mistake: One person manually checks everything sequentially

Why it fails: Wastes time, misses expertise, takes 2+ hours

Better approach: Launch parallel agents with domain expertise

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Escalation Decision Tree

User Reports Blank Dashboard
    ↓
Check if data exists (SQL query - 30 sec)
    ↓
├─ Data Missing
│   ↓
│   Trace to fact table
│   ↓
│   ├─ Fact stale → Check source table
│   │   ↓
│   │   ├─ Source fresh → dbt job needs to run (analytics-engineer-role)
│   │   └─ Source stale → Extraction issue (data-engineer-role)
│   │
│   └─ Fact fresh → Transformation logic issue (analytics-engineer-role)
│
└─ Data Exists
    ↓
    Dashboard investigation (bi-developer-role)
    ↓
    ├─ Extract stale → Refresh extract
    ├─ Filter incorrect → Update filters
    └─ Data source stale → Refresh metadata

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Success Criteria

Pattern Application is Successful When:

• ✅ Root cause identified within 30 minutes
• ✅ Correct agent assigned to resolution
• ✅ No time wasted on wrong troubleshooting path
• ✅ User communicated with accurate timeline
• ✅ Long-term fix identified (not just workaround)

Pattern Application Needs Improvement When:

• ❌ Takes >1 hour to identify root cause
• ❌ Wrong agent assigned (e.g., BI dev troubleshooting data pipeline)
• ❌ Multiple false starts or backtracking
• ❌ User given inaccurate information
• ❌ Issue recurs without prevention plan

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Prevention Strategies

Proactive Monitoring

Implement data freshness checks:

-- Alert if data >24 hours stale
SELECT
  table_name,
  MAX(business_date) as most_recent,
  DATEDIFF(hour, MAX(business_date), CURRENT_TIMESTAMP) as hours_stale
FROM monitoring.data_freshness
WHERE hours_stale > 24;

Orchestra Dependency Management

Ensure proper job sequencing:

# Source extraction must complete before dbt
dbt_job:
  dependencies:
    - source_extraction.complete

User Communication

Set expectations:

"Data typically available by 8:00 AM. If viewing earlier, yesterday's data may not be processed yet."

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Related Patterns

• Pipeline Timing Pattern: Orchestration dependencies and job sequencing
• Data Quality Pattern: Source validation and testing strategies
• Escalation Pattern: Role-based responsibility and communication

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Pattern Confidence: 0.92 Based On: Concrete Pre/Post Trip Dashboard investigation (2025-10-03) Success Rate: 100% in test case (correctly identified timing issue) Recommended Use: All blank dashboard / missing data investigations

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