Unified Data Field

UDF Method Blueprint – v1

Unified Data Field (UDF)
A system-independent framework for defining, enriching, and validating enterprise data entities across domains
© 2025 Hans de Waal / Arero7. All rights reserved. Unauthorized use or reproduction is prohibited.
🌍 🇳🇱 Nederlandstalige versie

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🌱 Part of the Arero7 Pipeline

FDL → UDF → UIF

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🧩 1. FDL – Field Definition Language

The Field Definition Language (FDL) is a human-readable, structured format (inspired by YAML) used to describe real-life business entities and relationships. It bridges the gap between business semantics and structured data, making it possible for anyone — not just developers — to define their operational world in a way that is both understandable and machine-actionable.

• FDL allows you to describe your business in semantic blocks

• It separates definition from system implementation

• Definitions can be stored, versioned, and published (e.g., via GitHub)

Example (FDL snippet):

yaml

Entity: Customer
Origin: Sales
RequiredFields:
- name
- contact_email
- industry

Once defined in FDL, the entity is projected into the Unified Data Field (UDF) — where its full lifecycle and enrichment path begins.

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🌾 2. UDF – Unified Data Field

UDF is a structured field where data entities live, grow, and interact. It’s system-independent, meaning it’s not bound by the limitations of your ERP, CRM, or warehouse systems. It reflects the real journey of data through your organization.

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🔹 Step-by-Step Framework

1. Define Enterprise Domains
Examples:

• Sales

• Finance

• Legal

• Operations

• IT

• Customer Service

Each domain plays a unique role in the lifecycle of a data entity.

2. Create Your First Data Entity
Select a core entity:

• Customer

• Article

• Location

• Transaction

• Contract

Then define:

• Entity Name

• Originating Domain

• Minimum Viable Data Set from the origin domain

3. Map the Data Trail (Journey)
Trace how the entity flows through the organization.

Example:
Customer originates in Sales → validated in Legal → enriched in Operations → consumed by Finance for billing.

Each domain enriches the entity with fields and validation rules. This forms a data enrichment trail.

4. Define Validation Logic
Set:

• Minimum Output: what’s needed for operational use

• Maximum Output: full enriched potential

• Validation Rules: uniqueness, consistency, completeness

These validations ensure high data quality and a path toward Golden Records.

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🧠 3. Strategic Impact – Enabling the AI-Ready Enterprise

As enterprises step into the third wave of AI, the real bottleneck isn’t algorithms — it’s data clarity and context.
FDL + UDF provides exactly that.

✅ AI models need structured, validated, explainable data
✅ UDF enables organizations to define and trace how data evolves
✅ FDL makes definitions transparent and portable, usable by humans and AI alike
✅ UDF ensures that AI doesn’t consume isolated, incomplete, or conflicting data

“If you don’t define your data semantically first, you’ll never trust what AI spits out later.”

This framework turns your enterprise into a semantically-aligned, AI-ready organization.

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🛠️ Use Cases

• Data governance & quality programs

• AI data preparation & explainability

• BI and KPI standardization

• Master Data Management (MDM) foundation

• Process modeling & optimization

• Enterprise knowledge capture

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🧪 Incubation Status

UDF is the first official framework being incubated under Arero7.
FDL specifications are in development.
First MVP targets: Customer & Article entities in midsize enterprises.

Interested in co-creating or piloting the method?

🔗 arero7.com/seeds/udf
✉️ hans@arero7.com

© 2025 Hans de Waal / Arero7. All rights reserved.
First published on arero7.com/seeds/udf — July 15, 2025