Adventure Works

Data Governance ..

Adventure Works

In this Use Case we're going to follow the journey of Adventure Works Cycles as they implement enterprise data governance using Pentaho Data Catalog. Through a combination of automation and machine learning, participants will learn to ingest, profile, and curate structured and unstructured data while building a secure and comprehensive inventory of data assets.

Target Audience	Role
Data Steward	A data steward is responsible for maintaining data quality, defining standards, and managing metadata within a data catalog to ensure data is accurate, well-documented, and properly governed for their assigned data domain.
Business Analyst	A Business Analyst uses the data catalog to discover, understand, and access relevant data assets, then translates that data into business insights and requirements that drive decision-making and process improvements.
Data Engineer	A Data Engineer builds and maintains the technical infrastructure that ingests, transforms, and loads data into the catalog, ensuring data pipelines are reliable, scalable, and properly integrated across systems.
IT Administrator	An IT Administrator manages user access, security permissions, and system infrastructure for the data catalog, ensuring the platform is secure, available, and properly configured for organizational needs.
Compliance Officier	A Compliance Officer monitors and enforces data policies, privacy regulations, and legal requirements within the data catalog, ensuring all data handling practices meet regulatory standards and organizational governance rules.

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Overview

Challenge: Adventure Works Cycles is expanding globally and must comply with GDPR, CCPA, and emerging data privacy regulations while maintaining operational efficiency. The company needs to implement comprehensive data governance to ensure data quality, lineage tracking, and regulatory compliance across their enterprise.

Journey: Follow Adventure Works as they transform from a data-scattered organization to a governance-first enterprise, learning to balance accessibility with security, compliance with innovation, and automation with human oversight.

Outcome: A fully implemented, best-practice data governance framework that serves as a blueprint for modern organizations navigating the complex landscape of data privacy, security, and business intelligence.

1

Data Discovery

Connect to the AW data source and ingest the metadata.

2

Mapping the AW Organization to PDC Roles

AW Users need to be authenticated - Keycloak - to access the PDC assets. PDC Roles authorizes which assets; with Communities determining the scope and permissions.

3

Glossaries, Terms & Tags

Glossaries / Business Terms serve as the critical bridge between technical data assets and business value, enabling everyone from C-suite executives to business analysts to speak the same data language when discussing key performance indicators, business rules, and strategic metrics.

4

Data Identification

Data Identification uses data dictionaries and pattern recognition to automatically discover, classify, and document data elements based on their structure, format, and semantic meaning across the organization's data landscape.

5

Data Processing

Once profiled the resulting Key Metrics display: Overall Data Quality, Sensitivity Level, Trust Scores, Risk Level, along with: Auditing - Timestamps & Ownership, Data Lineage - PK & FK detection,

6

Canvas, Collections & Products

A Collection is a way to logically group data assets, such as schemas, tables, and files, so that you can work with them more efficiently.

7

Business Rules

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