Data Product Slides

Data As Product Built for Outcomes Not Dashboards.

Data as a Product encapsulates data, business logic, and delivery into a unified, scalable asset—enabling organizations to move beyond merely observing information toward operationalizing intelligence.
Here are the slides and step-by-step guide to help you learn data products and gain practical, hands-on experience.

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15 pages
~45–60 min read
Slide-aligned
Quick Start
Start with Slide 1
Overview
Why + What + frameworks
Drivetrain
Objective → levers → data
Who should learn

CIOs, CDOs, and CPOs must master data products to turn data investments into scalable, measurable business outcomes..

Recommended learning path

A simple sequence that mirrors the slide progression—from fundamentals and definitions to experimentation, drivetrain, and org design.

Jump to all pages
  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
    Using Knobs
    Slide 12
  13. 13
  14. 14
    User Centric DP
    Slide 14
  15. 15

Slides and Guide

Each card below to a standalone slide and guide page aligned to a slide in the Data Product deck.

Top
Data Product 101: Overview
Slide 1

Data Product 101: Overview

Start here: the 'Why' and 'What' plus core frameworks.

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From Resource to Product
Slide 2

From Resource to Product

The strategic shift and business case for treating data as a product.

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The Data Product Mindset
Slide 3

The Data Product Mindset

Core principles and the paradigm shift from projects to products.

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What is a Data Product?
Slide 4

What is a Data Product?

Architecture: pillars, ports (interfaces), and deployable units.

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Data Product Attributes
Slide 5

Data Product Attributes

The six baseline characteristics that make data usable and trustworthy.

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Software vs Data Products
Slide 6

Software vs Data Products

How state, testing, lifecycle, and failure modes differ.

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Balancing R&D and Validation
Slide 7

Balancing R&D and Validation

The builder’s dilemma: accuracy vs. time-to-market.

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Data Product Experimentation
Slide 8

Data Product Experimentation

Dual engines: intelligence (technical) and value (market) validity.

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The Drivetrain Approach
Slide 9

The Drivetrain Approach

Objective → levers → data → models: build prescriptive products.

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Data Product Operating Model
Slide 10

Data Product Operating Model

Centralized vs hub-and-spoke vs full data mesh.

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Data Product Operating Model
Slide 11

Data Product Life Cycle

End to End Journey to Build and Govern

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Data Product Operating Model
Slide 12

Build Data Products with Knobs

Levers to Control Data Product Output

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Data Product Operating Model
Slide 13

3 Pillars of Data Products

Align User Task and Data

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Data Product Operating Model
Slide 14

Value Chain of Data Products

Data Product to Cognitive Intelligence to Impactful Result

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Data Product Operating Model
Slide 15

Data Product Usage

Data Product Capability Spectrum - 5 Ways to Use Data Products

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Why Agentic AI need Data Products

Data products provide the structural backbone for AI and agentic systems. They ensure consistent context for LLMs, reliable feature stores for ML models, high-quality retrieval layers for RAG architectures, and enforceable data contracts for real-time decisioning. For agentic AI specifically, stable and well-defined data products enable autonomous agents to reason, act, and update workflows without constant human correction.

File list (
  • 1-data-product-101
  • 2-data-as-resource-vs-data-as-product
  • 3-data-product-mindset
  • 4-what-is-data-product
  • 5-data-product-attributes
  • 6-software-product-vs-data-products
  • 7-inherent-tension-algorithm-usability
  • 8-data-product-experimentation
  • 9-drive-train-approach
  • 10-data-product-operating-model