Data Products - Minimum Lovable Governance Masterclass

Lightweight governance that makes data products trusted, scalable, and business-ready

What you'll learn

Why legacy data governance models don’t work for data products.

How to apply Minimum Lovable Governance (MLG) in practice.

Simple ways to link business goals with SLAs and data quality.

How to use agreements and standards to build trust.

Lightweight methods to scale governance across teams.

Requirements

No prior experience with data governance frameworks required.

A basic understanding of how organizations use data (analytics, reporting, or digital services) is helpful but not mandatory.

Curiosity about how to move beyond traditional data management toward product thinking.

Description

Are you tired of data governance that feels like red tape instead of real impact?Most organizations still treat governance as a compliance checklist—something to slow down innovation, not accelerate it. Meanwhile, business leaders keep asking: “Where’s the ROI from all this data?”This course introduces Minimum Lovable Governance (MLG), a modern approach designed specifically for data products. Instead of endless rules and policies, you’ll learn how to embed just enough governance to deliver trust, quality, and business value—without killing agility.What you’ll discover inside:How to shift from managing data as an asset to delivering data as a product.Practical frameworks like the Open Data Product Specification (ODPS) that bring governance to life.How to automate SLAs, data quality, and compliance checks as code.Clear methods to measure business outcomes, not just technical outputs.Tools and templates you can apply immediately in your own organization.Terraforming Data Product Governance bookWhy this matters:Drive business value: Turn raw data into products that solve real problems.Boost your career: Master a future-ready skill set in high demand worldwide.Make governance lovable: Learn to balance control with speed, trust with innovation.Whether you’re a data professional, manager, or business leader, this course gives you a proven blueprint to move beyond old-school data management and become a champion of data product value creation.

Overview

Section 1: Introduction

Lecture 1 Course Introduction — setting the stage

Lecture 2 From Enterprise Architecture to Data Products — Strategic Alignment-by-Design

Lecture 3 From Data Management to Data Products — Measuring Value-by-Design

Lecture 4 From Raw Data to Data Products — Value Creation-by-Design

Lecture 5 From Data Contracts to Data Products — Outcomes-by-Design

Lecture 6 Why Governance Needs a Rethink

Section 2: The Minimum Lovable Governance Model

Lecture 7 Core Components and Use of Minimum Lovable Governance

Lecture 8 Use cases library

Lecture 9 Connecting SMART Objectives and KPIs to Data Products

Lecture 10 Minimum Lovable Governance Toolkit

Lecture 11 Standards in Context – Compliance Where It Matters

Lecture 12 Archetypes define what kind of product we’re building

Lecture 13 Templates show how to build it consistently through lifecycle

Lecture 14 Components encode the governance rules

Lecture 15 Data Product Design Guide - binding all together

Section 3: Case: Business Objective and 5 Data Products

Lecture 16 The Wake-Up Call & MLG as the Answer

Lecture 17 Steering with MLG (Practical Fixes)

Lecture 18 From Promises → Automation (ODPS as Code)

Section 4: Integrating MLG & ODPS with Modern Data Architectures

Lecture 19 Prominent Data Management Approaches

Lecture 20 From Medallion Layers to Data Products: Applying ODPS for Maximum Value

Lecture 21 From Raw Vault to Business Value: Using ODPS with Data Vault 2.0

Section 5: Congratulations! Next steps

Lecture 22 Final words

Data leaders who want governance to drive measurable business outcomes, not just compliance.,Product managers and data product managers ready to apply product thinking to governance.,Business and technology managers looking for a practical way to align data investments with ROI.,Governance professionals who feel existing frameworks are too heavy and want a lightweight alternative.,Anyone working with data products who wants tools and methods to make them trusted, scalable, and business-ready.