Data Integrity Command Center

Accuracy Score

●

99.4%

Error Rate: 0.6%

Timeliness (Lag)

●

40ms

Real-time Stream

Accessibility

●

99.9%

API Uptime

Relevance ($d=0$)

●

62%

Data used for Decisions

The Quality Core

Overall Trust Score: 92/100

Timeliness Stream

Latency vs SLA

Resilience & Consistency

✓

Schema Validation

Integrity Check passed on 14 Tables

PASS

⚠

Failover Test

Resilience degradation in Region US-East

WARN

Compliance Summary

PII Exposure

0

Access Reviews

100%

End-to-End Velocity: 124ms

Source Ingest

45ms

Kafka Stream A

Transformation

68ms

Spark Jobs

Serving Layer

11ms

Redis Cache

Processing Time vs Volume

Bottleneck Detection

Job: nightly_agg_v2 +4.2s

Skewed partition detected in Shuffle phase.

API: /user/history +200ms

Missing index on 'transaction_date'.

S3 Connector Normal

Throughput stable at 40MB/s.

Data Sensitivity Breakdown

100%

Scanned

Risk Exposure Heatmap

GDPR Compliant

Data Domain	Encryption	Access Level	Risk Score
Customer_Profile (PII)	AES-256	Restricted (L4)	Low
Transaction_Logs	TLS 1.3	Internal (L2)	Low
Marketing_Leads	Partial	Public (L1)	Med

Access Anomalies (Zero Trust Monitor)

User / Service Principal Action Timestamp

service_account_bi_tool Bulk Export (50k rows) 10:42:01

admin_j_doe Policy Update 09:15:33

unknown_ip_84.22.11 Auth Failure (x3) 04:12:00

The Data Value Equation ($V \propto 1/d^2$)

Data value decays exponentially with distance ($d$) from the core decision-making moment. We must identify and purge "Zombie Data" ($d > 2$).

Storage Cost vs. Query Utility

Zombie Asset Scheduler

tmp_analytics_dump_2024

Last access: 180 days ago

DELETED

logs_archive_q1

Last access: 92 days ago | Cost: $450/mo

user_behavior_raw_v1

Last access: 60 days ago | Cost: $1.2k/mo

Asset Utilization (Inverse-Square Distribution)

Optimization: Active