Data Architecture & Knowledge Engineering

Making data
legible,
actionable, trusted.

Datenvalue transforms diverse datasets into structured, searchable, policy-compliant knowledge assets — the foundation that powers AI agents, graph intelligence, and confident decision-making.

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From raw data
to reliable knowledge

Every organisation holds more information than it can use. The gap between data and knowledge is an architecture problem. We close it.

01 — Model

Data Modeling & Schema Design

Schemas, ontologies, taxonomies, and metadata frameworks for structured and unstructured datasets. Entity, relationship, and attribute models that support graph-based reasoning and discovery.

02 — Verify

Data Quality & Assurance

Review incoming datasets for completeness, accuracy, and consistency. Identify and resolve duplication, missing values, formatting inconsistencies, and entity ambiguity. Establish lasting validation standards.

03 — Enrich

Classification & Enrichment

Tagging, categorisation, and metadata strategies that improve searchability and retrieval. Entity resolution and normalisation across multiple datasets. Controlled vocabularies and classification systems.

04 — Govern

Policy, Compliance & Governance

Review datasets and workflows for compliance with privacy, security, licensing, and data governance requirements. Auditability, provenance tracking, and explainability across all data assets.


Twenty years of making data work harder

Datenvalue is the independent practice of Unmil Karadkar — a data architect and knowledge engineer with over two decades of experience turning complex, fragmented data into precise, usable assets.

Based in Graz, Austria, working internationally. Fluent in the full spectrum from raw ingestion to graph-powered intelligence.

20+ Years in data architecture & knowledge engineering
4 Core disciplines: model, verify, enrich, govern
Data that could still be doing more
RAW DATA STRUCTURED KNOWLEDGE INTELLIGENCE

Precise, documented,
built to last

Data architecture work that can't be explained, audited, or handed over isn't finished. Everything Datenvalue produces is documented, traceable, and built to outlast the engagement.

Step 01

Understand

Deep review of existing data sources, systems, and goals. We map what you have before designing what you need.

Step 02

Design

Schema development, ontology design, and metadata strategy tailored to your specific reasoning and retrieval needs.

Step 03

Build & Validate

Implementation with rigorous quality assurance — entity resolution, normalisation, validation processes, and health monitoring.

Step 04

Govern & Hand over

Full documentation, governance frameworks, and provenance tracking so your team can maintain, extend, and trust what was built.


Let's talk about your data

Whether you're starting from scratch or untangling years of accumulated complexity, the first conversation is always free.

Principal Unmil Karadkar
Location Graz, Austria — working internationally
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