Data and Analytics Assessment

Data Engineering, Platforms and Observability Maturity Assessment

Evaluates data engineering maturity across platform strategy and architecture, pipeline engineering standards, data testing and release, data observability and reliability, platform security and identity hand-offs, platform cost and sustainability, AI and ML data supply, and performance and improvement.

Already have an account? Log in to start

At a glance

Questions: 49
Domains: 8
Working time: Initial assessment: 4 to 6 hours of facilitated sessions with data platform, data engineering, data operations, data security and data product owners. Evidence gathering: architecture and pipeline standards, repositories and orchestration history, test and release records, monitoring and incident records, access reviews, recovery test results and cost reports.
Repeat: Every six months, and after a major platform migration, a serious data incident or a significant change in AI data use.
Roles: 6
Updated: October 2026

Who it is for

Process Owner/Full Model Assessor
Data Platform Engineering
Data Engineering
Data Operations and Reliability
Data Platform Security
Data Product Owners and Consumers

Head of data engineering, head of data platforms, data platform product owner. Secondary: data engineers and analytics engineers, DataOps and SRE leads, data security, FinOps partners, data product owners and the chief data officer.

What you will gain

Shows where data pipelines still depend on manual steps and individual engineers, where bad data can reach consumers untested or unnoticed, where platforms are exposed, unrecoverable or overspending, and where AI systems receive data nobody controls. Gives each gap a concrete next step and a measurable target.

About this assessment

Data teams often find out that a pipeline failed when a business user spots a wrong number. Pipelines are hand-scheduled, SQL changes go live without review or tests, schema changes break reports, backfills overwrite history and nobody can say how fresh a critical data set is.

This assessment follows the order in which a dependable data platform is built: an approved architecture, environments and patterns, a platform run as a product with planned capacity and visible debt; then pipeline engineering rules for version control, orchestration, schema change, templates and AI-assisted code; automated data tests, safe test data, contract tests, controlled release, rollback and backfills, and quality rules running inside pipelines; monitored data signals with automated lineage, data service level objectives, alerting, incident triage, recovery, review, anomaly detection and automated remediation; platform access, identities, logging, encryption and disaster recovery; cost by data product, storage lifecycle, guardrails and carbon; the data supplied to AI models, retrieval indexes and agents, including unstructured content; and measurement, benchmarking and improvement.

It tests data-specific practice and the hand-off to each neighboring model through named Input documents: Data Governance and Management, Metadata, Catalog and Lineage, Master Data and Data Quality Management, Data Classification and Handling, Data Products and Data Sharing, Privacy Program Operations, Records and Information Management, DevOps Engineering, SRE, DevOps and ITSM Convergence, Non-Human Identity and Secrets Management, IT FinOps and Cloud Financial Management, Business Continuity and Disaster Recovery Management and AI Pipeline and MLOps. Each question is scored on whether the practice is documented, implemented and effective.

What we assess: 8 domains

Each domain is a group of related questions, scored on its own and rolled up into your overall result.

01 · 6 questions

1 Data Platform Strategy and Architecture

Reference architecture, platform service model, environment separation, approved batch, streaming and lakehouse patterns, capacity and performance planning, roadmap and technical debt

02 · 6 questions

2 Pipeline Engineering Standards

Pipeline engineering standard, version control and review, managed orchestration, schema change control, reusable templates and AI-assisted pipeline code

03 · 6 questions

3 Data Testing and Release

Automated data tests, safe test data, contract tests at publish, release and rollback, backfill controls and business quality rules running in pipelines

04 · 9 questions

4 Data Observability and Reliability

Monitored signals with automated lineage, data service level objectives, monitoring coverage, alert routing, incident triage, recovery and replay, post-incident review, anomaly detection and automated remediation

05 · 5 questions

5 Platform Security and Identity Hand-offs

Access by role and data level, pipeline service identities, data plane logging, encryption by data level, and platform backup and disaster recovery

06 · 5 questions

6 Platform Cost and Sustainability

Cost allocation to data products, storage tiering and lifecycle, compute and query guardrails, AI data workload costs, and energy and carbon of data workloads

07 · 5 questions

7 AI and ML Data Supply

Training data snapshots, retrieval data pipelines, agent-run data operations, label and deletion propagation into AI copies, and unstructured content ingestion

08 · 7 questions

8 Performance and Improvement

Reliability measures, delivery speed and debt trend, consumer feedback, external benchmarking, technology evaluation, engineering knowledge reuse, and KPIs and improvement

KPIs to track

Measures that show whether the improvements hold.

KPI
Consumer-Detected Incidents - Share of data incidents first reported by consumers rather than by monitoring. Target: 10% or fewer.
Data Objectives Met - Share of critical data sets with agreed objectives that met them over the last quarter. Target: 90% or higher.
Critical Pipelines with Blocking Tests - Share of critical pipelines whose automated data tests run on every change and block release on failure. Target: 100%.
First-Run Pipeline Success - Share of scheduled production pipeline runs completing on the first attempt, before any automatic or manual retry, over the month. Target: 98% or higher (model target).
Data Change Lead Time - Median days from first committed pipeline change to production. Target: 7 days or fewer, aligned with the DORA high performance band (one day to one week).

Framework alignment

Questions are written in plain language and mapped to:

DAMA-DMBOK2 Revised
ISO/IEC 20547-3:2020
ISO/IEC 27001:2022
ISO/IEC 5259-3:2024
ISO/IEC 21031:2024
ISO 8000-61:2016
NIST SP 800-218
NIST AI RMF 1.0
The Site Reliability Workbook Ch. 5
The Site Reliability Workbook Ch. 6
The Site Reliability Workbook Ch. 13
OWASP Top 10 for LLM Applications 2025
OWASP Top 10 for Agentic Applications 2026
FinOps Framework 2026
APQC PCF
COBIT 2019

Works best with

Practices this assessment relies on. Assessing them too gives a fuller picture.

Data Governance and Management (v1.9.6): architecture principles, data roadmap and enterprise data KPIs (1.1, 1.6, 8.7).

Metadata, Catalog and Lineage (v1.9.6): automated lineage in the Data Catalog (4.1); Master Data and Data Quality Management (v6.9.6): quality rules, quarantine and data issues (3.5, 3.6, 4.5).

Privacy Program Operations (v1.9.6): rules for personal data in test and non-production environments (3.2); Enterprise Architecture (v7.9.6): reference architectures and technology radar (1.1, 8.5).

Capacity Performance Management (v6.9.6): load and stress tests (1.5); API and Integration Lifecycle (v6.9.6): event streams and delivery guarantees (1.4); Continual Service Improvement (v8.9.6): improvement knowledge base and register (8.6, 8.7).

Data Classification and Handling (v9.9.6): access, logging, encryption and permitted AI use by level and AI data copies (5.1, 5.3, 5.4, 7.4, 7.5); Data Products and Data Sharing (v1.9.6): contracts and the product register (2.4, 3.3, 4.3).

DevOps Engineering (v1.9.6): software engineering, golden paths, AI coding rules, delivery pipeline, test strategy and metrics (2.1, 2.2, 2.5, 2.6, 3.1, 3.2, 3.4, 8.2, 8.4); SRE, DevOps and ITSM Convergence (v4.9.6): service record, infrastructure as code, capacity feed, change registration, observability, SLOs and runbooks (1.2, 1.3, 1.5, 3.4, 4.1, 4.2, 4.4, 4.8, 4.9).

Incident Management (v9.9.6), Post-Incident Review and RCA Quality (v4.9.6) and Business Continuity and Disaster Recovery Management (v6.9.6): priority, reviews, backup and recovery (4.5, 4.6, 4.7, 5.5).

Non-Human Identity and Secrets Management (v4.9.6): vault, workload authentication, key requirements and agent identities (5.2, 5.4, 7.3); DevSecOps (v6.9.6): supply chain controls for connectors and pipeline packages (2.1); IT FinOps and Cloud Financial Management (v5.9.6): attribution, guardrails, AI unit costs and carbon reporting (6.1, 6.3, 6.4, 6.5).

AI Pipeline and MLOps (v11.9.6): dataset versions, retrieval indexes and agent runtime controls (7.1, 7.2, 7.3, 7.5); Records and Information Management (v1.9.6): retention and AI copy disposal (6.2, 7.4).