Diakon Engine
The pipeline that turns a governance document into an objective, traceable, DMBOK-based score — no black box.
How the Diakon Engine works
Five stages, from the normative baseline to final recommendations. Every step is auditable.
Diakon Canon
Normative baseline: DMBOK + industry policies. The benchmark that defines "good governance."
Document
PDF with your organization's policies, manuals, or guidelines.
LLM
Language models score each DMBOK indicator, citing the relevant document excerpts.
Diakon Score
0-to-100 index per discipline, aggregated with a robust statistical model.
Recommendations
Prioritized actions to elevate your governance, discipline by discipline.
What does Diakon assess?
The 12 DMBOK v2 disciplines — the international reference framework for data management.
01
Data Governance
Authority, control, and accountability structure over data assets.
02
Data Architecture
Blueprint defining how data flows, is stored, and consumed.
03
Data Modeling & Design
Conceptual, logical, and physical data structures.
04
Data Storage & Operations
Infrastructure, availability, and data lifecycle management.
05
Data Security
Protection, classification, and access control for information.
06
Data Integration & Interoperability
Movement and consolidation of data across systems.
07
Document & Content Management
Lifecycle management of documents and unstructured content.
08
Reference & Master Data
Single version of truth for critical business entities.
09
BI & Data Warehousing
Analysis, reporting, and data-driven decision support.
10
Metadata Management
Cataloging, lineage, and data meaning.
11
Data Quality
Metrics, cleansing, and continuous quality assurance.
12
Data Ethics
Responsible use, bias, privacy, and social impact of data.
The Diakon Score in practice
One number per discipline, from 0 to 100. Simple to grasp, dense with meaning.
Solid, with room to grow
Excellent — keep it up
Critical — immediate action recommended
In development
How the score is calculated
No jargon, straight to the point.
1. The Diakon Canon defines what to measure
It's the benchmark: for each DMBOK discipline, there are specific indicators. For example, "does a data classification policy exist?" or "is there a documented data cleansing process?".
2. The AI reads and scores
The Diakon Engine reads your document and assigns a 0-to-1 score for each indicator — always citing the passage from the document that supports the rating. Nothing is made up.
3. The system calculates, not the AI
AI scores are combined in a statistical model (PLS-SEM) that generates the final score per discipline. The AI scores — the system calculates. Strict separation, no hallucination in the final step.
See the Diakon Engine in action
Schedule a demo with a real document from your organization. We return with your score within 2 business days.
Schedule a demo