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title Data Trust Engineering Use Cases
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Data Trust Engineering (DTE) Use Cases and Trust Dashboard

Welcome to the Data Trust Engineering (DTE) Use Cases and Trust Dashboard, practical examples that demonstrate DTE principles in action. These open-source tools and examples embody DTE's principles of trust, engineering rigor, and AI-readiness as outlined in the Data Trust Manifesto.

Mission for Examples & Design Patterns

The examples in this guide are designed as illustrations, not end-to-end products. Each one shows how DTE principles can be applied in practice using open-source components. Think of them as recipes that teams can fork, adapt, and extend—not polished systems.

Over time, these examples will also evolve into a library of design patterns for Trust Engineering.

  • Patterns for shift-left quality (contracts at ingestion)
  • Patterns for lineage and observability (OpenLineage integration, monitoring pipelines in real time)
  • Patterns for AI trust (fairness, explainability, drift checks)
  • Patterns for AI evaluations (systematic LLM/RAG evals, confidence scoring, guardrail testing)
  • Patterns for observability (metrics, logging, tracing tied to trust indicators)
  • Patterns for technical debt management (dbt tests, Great Expectations)

The goal is to provide starting points and reusable patterns that make trust engineering approachable, repeatable, and community-driven.

Purpose of the Use Cases

These use cases are not full-blown projects or attempts to rebuild the entire ecosystem of governance and AI tools. The ecosystem is already too large and fragmented. Instead, the purpose is to act as a guide with examples, showing how open-source components can be applied to real trust problems.

  • Baseline, not best-in-class: The goal is not to compete with enterprise tools, but to set a baseline reference that's easy to understand and extend.
  • Trust Dashboard as MVP: This is the anchor artifact — a lightweight starting point that demonstrates DTE principles (trust, rigor, AI-readiness) in a tangible way.
  • Examples, not platforms: Each use case is a recipe (e.g., data contracts at ingestion, lineage with OpenLineage, fairness checks with Fairlearn), not a production-ready service.
  • Community-first: The structure invites contributions, but the priority is to show how to start, not to provide a polished ecosystem out of the gate.

The DTE Trust Dashboard

The DTE Trust Dashboard is a practical, open-source artifact that visualizes real-time trust metrics. It empowers teams to build trusted, scalable systems for high-stakes use cases such as AI training, regulatory reporting, and GenAI safety.

Implementations

  • HTML Version (DTE_Trust_Dashboard.html) Built with Chart.js for lightweight, static visualization.
  • Streamlit Version (app.py) Built with Streamlit and Plotly for interactive, Python-based visualization.

Both versions monitor key metrics:

  • AI Fairness (Fairlearn) — fairness scores across protected attributes (e.g., gender, age).
  • Model Explainability (Evidently AI) — feature importance visualization.
  • Guardrails Adherence — radar chart showing adherence to DTE principles: trust, certification by use case/risk/value, observability.
  • GenAI Safety (Evidently AI) — metrics for toxicity, bias, hallucination, privacy leakage, factual accuracy.
  • Model Performance (MLflow) — accuracy, F1, AUC-ROC over time.

DTE Principles in Action

  • Trust: Transparent, real-time metrics build confidence for enterprises, users, and AI systems.
  • Engineering Rigor: Robust implementation with lightweight open-source tools (Chart.js, Plotly, Superset).
  • Collaboration: Open, extensible design inviting community contributions.

Getting Started

HTML Version

# Open in browser
open tools/data-trust-dashboard/DTE_Trust_Dashboard.html
# Or host via GitHub Pages or a simple web server

Streamlit Version

cd tools/data-trust-dashboard
python3 -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate
pip install -r requirements.txt
streamlit run app.py
# Access at http://localhost:8501

Extend the Dashboard

  • Data Quality: Integrate Great Expectations.
  • Lineage: Connect Apache Atlas.
  • AI Governance: Add Fairlearn, Evidently AI, MLflow, or SDV.

Practical Use Cases for DTE

The following use cases demonstrate how to implement DTE principles with open-source code. Each includes a description, its alignment with DTE, and a code artifact.

1. Trust Dashboard

See The DTE Trust Dashboard above — a working example implementation.

2. Agentic Workflows Trust

Validate agent decisions with logged outputs for auditability. Artifact: LangChain + Great Expectations.

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from great_expectations.dataset import PandasDataset
import pandas as pd, json

def validate_data(input_data: str) -> str:
    df = pd.read_json(input_data)
    dataset = PandasDataset(df)
    result = dataset.expect_column_values_to_be_unique("id").to_json_dict()
    return json.dumps({"success": result["success"], "details": result["result"]})

tools = [Tool(name="DataValidator", func=validate_data, description="Validates uniqueness")]
llm = OpenAI(model="gpt-3.5-turbo", api_key="your-openai-key")
agent = initialize_agent(tools, llm, agent_type="zero-shot-react-description")

data = pd.DataFrame({"id": [1, 2, 2, 4]}).to_json()
result = agent.run(f"Validate this data for uniqueness: {data}")

3. RAG / GraphRAG

Ground LLMs in trusted knowledge graphs. Artifact: Neo4j + LangChain.

from langchain_community.graphs import Neo4jGraph
from langchain.chains import GraphQAChain
from langchain.llms import OpenAI

graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")
graph.query("""
CREATE (p:Principle {name: "Transparency"})
CREATE (p2:Principle {name: "Accountability"})
CREATE (p)-[:RELATED_TO]->(p2)
""")

llm = OpenAI(model="gpt-3.5-turbo", api_key="your-openai-key")
chain = GraphQAChain.from_llm(llm, graph=graph)
print(chain.run("What principles are related to Transparency?"))

4. Data Products with Contracts

Formalize schemas and SLAs. Artifact: dbt + Open Data Contract Standard.

# datacontract.yaml
datacontract:
  id: user_analytics
  version: 1.0.0
  schema:
    fields:
      - name: user_id
        type: string
        required: true
        unique: true
      - name: event_timestamp
        type: timestamp
        required: true
-- dbt model: models/user_analytics.sql
{{ config(materialized='table') }}
SELECT user_id, event_timestamp, COUNT(*) as event_count
FROM {{ source('raw', 'user_events') }}
GROUP BY user_id, event_timestamp