# ODGS Strategic Roadmap: From "Governance" to "AI Safety"

## 🏛️ Phase 1: Academic & Research Validation (The "Trust" Layer)
**Goal:** Establish ODGS as a scientifically backed standard for AI Safety.

### TU Delft (The Hague/Delft)
*   **Target Department:** Values, Technology & Innovation (VTI).
*   **Why:** They focus on "Responsible Innovation" and "AI Safety."
*   **Contact:** Innovation & Impact Centre (Ask for AI & Cybersecurity collaboration).
*   **Student Initiative:** Delft AI Safety Initiative (DAISI).
*   **Action:** Reach out to DAISI leadership. Propose ODGS as a "Practical Tool for AI Safety" for student projects.
*   **Research Labs:** AISyLab (AI & Security).
*   **Pitch:** "We have the protocol to prevent Semantic Hallucinations. Can we test it in your lab?"

### TNO (The Netherlands Organisation for Applied Scientific Research)
*   **Program:** Appl.AI (Directed by Freek Bomhof).
*   **Initiative:** "Responsible AI That Works".
*   **Pitch:** TNO is looking for practical implementations of ethical AI. ODGS is that implementation.

### Grants (NWO & AiNed)
*   **Opportunity:** AiNed Fellowship Grants or Open Mind 2025.
*   **Angle:** "Bridging the gap between EU AI Act regulation and Technical Implementation."

## 🏢 Phase 2: Corporate Pilots (The "Revenue" Layer)
**Goal:** Find "Design Partners" who are terrified of the EU AI Act.

### The Hague Security Delta (HSD)
*   **Action:** Join HSD as a partner.
*   **Target:** Zuid-Holland AI alliance members.
*   **Value Prop:** "The EU AI Act is coming in 2025. Your data isn't ready. ODGS fixes the 'Data Quality' requirement of the Act."

### The "Unprepared" Enterprise
*   **Insight:** 56% of Dutch companies are unprepared for the AI Act.
*   **Sectors:**
    *   **Financial Services:** ING, NN Group, Aegon (All have HQ/Presence in The Hague/Randstad).
    *   **Government:** Ministries in The Hague (Interior, Justice).
*   **Pitch:** "Don't hire 50 lawyers. Install ODGS."

## 📢 Phase 3: Social Media Marketing (The "Hype" Layer)

### 👔 LinkedIn Strategy: "The Thought Leader"
*   **Persona:** The "Chief AI Safety Officer."
*   **Content Pillars:**
    *   **Fear/Urgency:** "The EU AI Act is here. Your data stack is illegal." (Link to ODGS).
    *   **Education:** "How to prevent AI Hallucinations using JSON schemas." (Carousel).
    *   **Validation:** "Partnering with TU Delft to solve Algorithmic Accountability."
*   **Tactic:** Comment on every post by Dutch Data Protection Authority (AP) and European AI Office.

### 🐦 Twitter/X Strategy: "The Builder"
*   **Persona:** The "Open Source Hacker."
*   **Content Pillars:**
    *   **Build in Public:** "Just shipped the odgs CLI. It compiles JSON to Power BI TMSL. 🤯 #DataEngineering #OpenSource"
    *   **The "Swiss Army Knife":** "Did you know standard_metrics.json can generate dbt tests AND Tableau folders? One file to rule them all."
    *   **Rants:** "Why is 'Governance' so boring? It should be code."
*   **Hashtags:** #DataEngineering, #AI, #OpenSource, #TechTwitter.

The "Elevator Pitch"
Generative AI is a "Reasoning Engine," not a "Knowledge Base." It is great at syntax, terrible at facts.
When an Executive asks an AI Agent: "What was our Churn last month?", the AI looks at column names. If it sees churn_date, churn_flag, and churn_rate, it guesses. It hallucinates.
ODGS provides the "Grounding": It forces the AI to look up the human-codified definition first. "ODGS acts as a Headless Semantic Compiler."
"Metric Provenance." Just as art needs provenance to be valuable, AI answers need provenance to be trusted. ODGS provides the "Chain of Custody" for business logic. 
The Mechanism: "We bind Descriptive Logic (The Definition) to Physical Structure (The Warehouse) using Constitutional Guardrails (The Rules). This ensures the AI never hallucinates a metric and never executes an illegal query."
The Exit Value: "We are building the standard protocol for Metric Provenance—the only way to audit AI agents under the EU AI Act."


ODGS: The Swiss Army Knife for Data
One Protocol, Infinite Possibilities.
ODGS (Open Data Governance Schema) is not a single tool. It is a set of ingredients (JSON Schemas) that you can mix and match to build entirely different products as per your business requirements.


Think of it like LEGO blocks for Data Governance.


🧩 The Ingredients (The Schemas)
The Open Data Governance Schema (ODGS) is composed of five modular schemas, each serving a critical role in providing structured, verifiable context for both human users and AI agents.


Layer

Schema File(s)

Core Purpose (The Role)

The Critical Question It Answers

The AI Safety & Execution Benefit

1. Descriptive Layer (The Dictionary)

standard_metrics.json

Defines the Abstract Logic (What a metric is, e.g., the exact formula for Churn).

"How do we calculate Churn?"

Semantic Grounding: Ensures AI agents use verified, non-hallucinated business definitions.

2. Knowledge Graph Layer (The Context)

ontology_graph.json

Defines Relationships (How one business concept connects to another).

"How do 'Revenue' and 'Churn' relate?"

RAG Navigation: Allows the AI to traverse and find complete business context.

3. Constitutional Layer (The Guardrails)

standard_data_rules.json

Defines Permissibility (Business constraints and valid operations for a metric).

"Is this data valid?" / "Can I sum this number?"

Constraint Satisfaction: Prevents the AI from executing illegal or logically invalid queries (e.g., summing a ratio).

4. Context & Diagnosis Layer (The Map)

root_cause_factors.json, business_process_maps.json, standard_dq_dimensions.json

Defines the Process and Diagnosis (The Why data is broken and Where it's created).

"Why is the data broken?" (e.g., Excel Hell) / "Where is data created?"

Chain-of-Thought: Provides the AI with the narrative context to diagnose anomalies and process failures.

5. Execution Layer (The Atlas)

physical_data_map.json

Defines the Physical Structure (Maps logical URNs to specific warehouse tables and columns).

"Where does the data physically live?"

Executable Grounding: Enables the AI to generate accurate, traceable, and safe Text-to-SQL.





🍳 The Recipes (Product Categories)
You don't have to choose one. You have the underlying technology to power all of them. You can mix and match to build entirely different products as per your business requirements.






Product Category

Concept

Analogy

Use Case

Target Audience

1. The "Open Metric" (For Data Engineers)

A universal standard for defining metrics, independent of any tool.

"Markdown for Metrics."

You write the metric once in ODGS. It automatically works in dbt, Power BI, Tableau, and Excel. No more rewriting SQL.

Analytics Engineers, CTOs.

2. Operational Intelligence (For COOs & Process Owners)

Linking data quality issues directly to business process failures.

"The Check Engine Light for Business Operations."

The dashboard doesn't just say "Data is bad." It says: "The 'Order Amount' is wrong because of 'Excel Hell' in the 'Quote-to-Cash' process."

COOs, Process Improvement Teams.

3. Definition Management (For Data Stewards)

A central library where business terms are defined, approved, and versioned.

"GitHub for Business Logic."

Marketing wants to change the definition of "Lead." They open a Pull Request in ODGS. Finance reviews it. Once merged, it updates the dashboards automatically.

Data Stewards, Governance Teams.

4. Algorithmic Accountability (For AI Safety)

A safety protocol that ensures AI Agents use legally verified definitions.

"The FDA Label for AI Data."

An AI Agent answers "What is our risk exposure?". ODGS ensures it uses the exact regulatory definition, preventing "Semantic Hallucinations."

AI Engineers, Chief AI Officers, Regulators (EU AI Act).



💡 The Market Thesis
"Semantic Layer War"
"Snowflake/Databricks solved ‘storage’. They haven't solved ‘meaning’."

The Current State: We are currently in the "Tower of Babel" phase of data.
Snowflake stores the data.
dbt defines it in SQL.
Looker defines it in LookML.
PowerBI defines it in DAX.
Result: "Revenue" means four different things in four different tools.
The Opportunity: The winner of the next 3 years won't be another storage engine. It will be the Rosetta Stone—the standard that translates "Revenue" once and feeds it everywhere.
Why ODGS Wins: Existing solutions (Cube, AtScale) are Engines (software you have to buy and run). We are proposing a Standard (a schema you just adopt). Standards usually beat proprietary engines in the long run (e.g., JSON beat XML, Kubernetes beat Docker Swarm).





Concept

Market Trend

The Problem

The ODGS Angle

1. The "Headless Semantic Layer"

The Semantic Layer market is exploding (dbt, Cube). Companies are tired of defining metrics in 10 different BI tools.

Existing solutions are either tied to a specific BI tool or require a heavy server.

"Serverless Semantics." ODGS is just a file, lightweight, free, and portable. It compiles to native code (TMSL, SQL).

2. "Governance as Code" (GaC)"

"Infrastructure as Code" (Terraform) revolutionized DevOps. Data teams want the same for governance.

Governance is currently done in PDFs or expensive tools (Collibra) that don't integrate with Git.

"Terraform for Data Governance." Policies, rules, and definitions are managed in Git, and CI/CD pipelines enforce them.

3. The "AI Ground Truth" Protocol

Generative AI is entering the Enterprise, but Hallucination is the #1 blocker.

LLMs don't know your business logic and guess, which is dangerous for regulated industries.

"The Context Engine." ODGS provides the structured, verified context that LLMs need to answer business questions accurately.

4. The "Open Data Exchange"

Companies need to share data definitions with partners/regulators (e.g., ESG reporting).

Sending PDFs of definitions is error-prone.

"The PDF for Data." A standard format to exchange meaning along with the data.


🏗️ From Theory to Reality: The Ecosystem
ODGS is not a theoretical whitepaper; it is a live protocol currently powering a suite of production applications.

(The Definition Engine): The "GitHub for Business Logic." It generates the standard_metrics.json that powers the ecosystem. (Status: Live).
(The Process Engine): Links data anomalies to process failures, proving the Context & Diagnosis Layer. (Status: Live).
(The Execution Engine): A Generative UI that translates natural language into safe visualizations, proving the Execution Layer. (Status: Live).
🛡️The Compliance Shield: ODGS and the EU AI Act
As Generative AI moves from "experimental toys" to "enterprise infrastructure," it enters the scope of global regulation. The EU AI Act (the world's first comprehensive AI law) mandates strict governance for "High-Risk AI Systems."

Current LLMs are "Black Boxes"—they cannot explain how they derived a specific number. This creates a compliance nightmare. ODGS transforms the AI into a "Glass Box," providing the transparency and governance required by law.

Here is how the ODGS Protocol specifically maps to the core Articles of the EU AI Act:






1. Meeting Article 10: Data Governance & Management
The Requirement: The Act mandates that high-risk AI systems must be built on data sets subject to "appropriate data governance and management practices," including examination for possible biases and data lineage.

The ODGS Solution:

Codified Lineage: ODGS provides the "Chain of Custody" for business logic. It binds the definition (Descriptive Logic) to the warehouse (Physical Structure).
Governance as Code: By treating governance like software (managed in Git), ODGS creates an immutable audit trail of who defined a metric and when it was changed, satisfying the requirement for traceability.
2. Meeting Article 13: Transparency & Interpretability
The Requirement: AI systems must be designed so that their operation is sufficiently transparent to enable users to interpret the system’s output and use it appropriately.

The ODGS Solution:

Metric Provenance: Just as art needs provenance to be valuable, AI answers need provenance to be trusted. When an AI agent answers a question using ODGS, it does not guess; it looks up the human-codified definition first.
Citation of Source: The AI can explicitly state: "I calculated this metric using the definition in standard_metrics.json v1.2." This fulfills the legal requirement to explain the "logic" behind the decision.
3. Meeting Article 15: Accuracy, Robustness, and Cybersecurity
The Requirement: High-risk AI systems must achieve an appropriate level of accuracy and robustness, specifically minimizing errors and "hallucinations."

The ODGS Solution:

Constitutional Guardrails: ODGS acts as a safety layer that defines "Permissibility". It prevents the AI from executing illegal or logically invalid queries.
Prevention of Semantic Hallucination: By anchoring the AI to a verified schema, ODGS ensures the AI uses the exact regulatory definition, significantly reducing the error rate compared to ungrounded LLMs.
Executive Summary: ODGS is not just a semantic layer; it is an Automated Compliance Protocol. It provides the standard for "Metric Provenance"—the only effective way to audit AI agents under the EU AI Act.



🔐Conclusion: The Rosetta Stone for the AI Era
We are currently living in the "Tower of Babel" phase of data. We have solved storage (Snowflake/Databricks), but we have not solved meaning. Today, "Revenue" means four different things in four different tools, leading to broken dashboards and hallucinating AI agents.

The winner of the next era of data won't be another proprietary "Engine" that you have to buy and run. It will be a Standard Protocol—a universal schema that translates meaning once and feeds it everywhere. Just as JSON replaced XML and Kubernetes won the container war, standards always beat proprietary engines in the long run.

Why ODGS?
ODGS is not just a set of JSON files; it is the "chain of custody" for business logic. By decoupling the definition of data from the execution of data, we enable a future where:

AI Agents are Safe: They stop guessing and start looking up the "Ground Truth," ensuring compliance with the EU AI Act.
Governance is Agile: Rules are managed like code (Git), not locked in PDF documents.
Metrics are Universal: You define a metric once, and it works natively in Tableau, PowerBI, and your AI Chatbot simultaneously.
The Protocol for Trusted AI
We are entering an era where AI agents will outnumber human analysts. In this world, the biggest risk is not that the AI won't work—it's that it will work incorrectly and we won't know why.

Current solutions try to fix this by making LLMs smarter. ODGS fixes this by making the Data smarter.

By treating Governance not as a policy document, but as executable code, ODGS provides the missing link between Human Intent and AI Execution.

We solved Storage (Snowflake).
We solved Compute (Databricks).
ODGS solves Trust.
The Call to Action: Join the ODGS consortium
We are not building a proprietary engine; we are ratifying the standard for Metric Provenance. We are actively collaborating with leading academic institutions (TU Delft) and policy think tanks (TNO) to align ODGS with the strictest interpretations of the EU AI Act.





For Research Partners: Join us in defining the "Physics of AI Trust."
For Enterprise Pilots: Become a Design Partner. Secure your data stack against the EU AI Act before the enforcement deadline.
For Engineers: Fork the repo. Build the adapters. Own the standard.
For Data Leaders: Stop buying "Semantic Layers" that lock you into a vendor. Adopt a Headless Standard that makes your definitions portable.
For AI Engineers: Don't let your agents fly blind. Give them the Context & Diagnosis layer they need to reason effectively.
For Regulators: Demand "Glass Box" transparency. If an AI cannot cite the provenance of its answer, it should not be answering high-risk questions.
The ingredients are ready. The recipes are proven. It is time to stop building the tower, and start speaking the same language.

Adopt the Protocol.
