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Data Stability Scorecard

Score the resilience of your data engineering and integration stack in under 20 minutes

Overview

Fragile data systems increase OpEx, slow decisions, and delay AI projects. Stable systems do the opposite. They move data reliably across warehouses, lakes, APIs, and applications with clear ownership and predictable cost.

This checklist helps you:

How to Score Each Question

0: Not true at all

1: Partially true. Informal or inconsistent

2: Mostly true. Some gaps remain

3: Fully true. Documented, monitored, and in regular use

SECTION A

Architecture and Integrations

Max Score: 18 points
Do we have an up-to-date diagram of all key data sources, pipelines, warehouses, or lakes, and downstream tools?
Does each integration and pipeline have a clear technical owner who understands its purpose and dependencies?
Can we state which reports, dashboards, models, and applications break if a given source or pipeline fails?
Do we know our critical paths and which pipelines must recover first after an incident?
Do our APIs, event streams, and shared tables follow defined contracts with documented schemas and SLAs?
Do we have a simple disaster recovery or "cold start" runbook for key data systems?
SECTION B

Pipelines and Orchestration

Max Score: 18 points
Do our production pipelines run under a central orchestrator or scheduler rather than scattered, unmanaged cron jobs?
Do all jobs have explicit success and failure criteria so runs cannot appear "green" while dropping or ignoring records?
Do our pipelines use automated retry, backoff, and dead letter patterns instead of relying on manual reruns?
Are our key pipelines idempotent so reruns do not create duplicates or corrupt aggregates?
Do new or changed pipelines follow a consistent path from dev to test to prod with checks at each stage?
Do we have defined latency and freshness SLAs for critical data products, with clear owners and response plans when targets slip?
SECTION C

Data Quality and Observability

Max Score: 18 points
Do our critical tables have automated checks for freshness, volume, and basic distributions, with alerts on failures?
Do we detect and surface schema changes on key sources and tables before they break downstream consumers?
Do our dashboards or tools show both infrastructure health and data health in a unified view?
Do alerts route to a channel or on-call rotation that responds, with alert noise tuned to avoid fatigue?
Do data incidents result in tickets that capture root cause, impact, and a permanent fix, not only a quick patch?
Do we review major data incidents at least once per quarter and remove at least one recurring root cause each cycle?
SECTION D

Governance, Security and Compliance

Max Score: 18 points
Have we classified sensitive fields such as PII, PHI, and financial identifiers in our schemas or catalogs?
Do we enforce least privilege access for sensitive data so users only see what they need, without shared admin accounts?
Can we show clear lineage from sources through transformations to final outputs for regulated or board-level reports?
Do we enforce data retention and deletion rules in warehouses, lakes, or storage systems that align with legal and contractual requirements?
Do we review changes that touch sensitive or regulated data for privacy, security, and compliance impact before deployment?
Do we maintain an inventory of third-party tools that handle sensitive data and hold them under contracts with clear data protection terms, such as DPA or BAA?
SECTION E

Cost and Reliability

Max Score: 18 points
Can we list the five most expensive data jobs or workloads and explain their main cost drivers?
Do we track the cost and time impact of reprocessing and backfills separately from regular platform spend?
Do we define reliability targets for uptime and data quality on key products and use them in planning and prioritization?
Do we have clear rules for when to scale infrastructure up or down so we act before hitting capacity limits?
Do we review and clean up idle, duplicate, or obsolete jobs and tables on a regular cadence?
Do we review data platform costs with finance or operations at least twice per year and agree on actions after each review?
SECTION F

Analytics and AI Readiness

Max Score: 18 points
Do core business metrics have documented definitions and trusted source tables, with one system of record per metric?
Do analytics and AI teams work mainly from curated datasets, marts, or feature stores rather than raw, unmodeled data?
Do our training and inference pipelines for AI follow the same observability, lineage, and access control standards as other data flows?
Can we trace model features and outputs back to source data and transformations, and explain what each model used?
Do new AI use cases pass through a light review for data risk, privacy impact, and load on shared systems before they scale?
Do AI and analytics initiatives map to clear business outcomes, with data engineering work tied to them in a visible roadmap?

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