Data as a Product: Building Data Products That Users Love

Data as a product is an approach that applies product management principles to data. Rather than treating data as a byproduct of operations or a technical asset managed by IT, data as a product treats datasets as first-class products with users, quality standards, documentation, and continuous improvement.

This shift in mindset transforms how organizations create, manage, and derive value from their data assets.

Why Treat Data as a Product

The Traditional Data Problem

In traditional approaches, data is an afterthought:

• Data is extracted from systems without considering downstream use
• Documentation is sparse or nonexistent
• Quality varies unpredictably
• Nobody owns data quality or availability
• Users spend more time finding and cleaning data than analyzing it

This approach treats data as exhaust rather than an asset.

The Product Mindset Difference

When data is a product:

• Data is designed for consumer needs
• Documentation is comprehensive and current
• Quality is guaranteed by SLAs
• Clear ownership ensures accountability
• Users can trust and immediately use data

The same thinking that makes great software products creates great data products.

Characteristics of Data Products

Discoverable

Users can find data products without tribal knowledge:

• Listed in searchable catalogs
• Tagged with relevant keywords and categories
• Described in business terms users understand
• Connected to related data products

If users can't find it, it doesn't exist.

Addressable

Clear, stable interfaces for accessing data products:

• Consistent endpoints or table names
• Versioned APIs or schemas
• Standard access patterns
• Published connection information

Users shouldn't need to figure out how to connect.

Trustworthy

Quality guarantees that users can rely on:

• Defined freshness expectations
• Accuracy and completeness standards
• Documented known limitations
• Historical reliability metrics

Trust is the foundation of data product value.

Self-Describing

Complete metadata that explains the data:

• Field definitions and business meaning
• Data types and valid values
• Relationships to other data
• Calculation methodologies
• Example queries and use cases

Users shouldn't need to guess what data means.

Interoperable

Designed to combine with other data products:

• Standard identifiers for linking
• Consistent naming conventions
• Compatible formats
• Documented relationships

Isolated data has limited value.

Secure

Appropriate access controls:

• Clear sensitivity classification
• Role-based access policies
• Audit logging
• Compliance with regulations

Security enables sharing with confidence.

Building Data Products

Identify Users and Use Cases

Start with who needs the data and why:

Who are the users?

• Analysts building reports
• Data scientists training models
• Business users making decisions
• Downstream systems consuming data

What decisions will this data inform?

• Strategic planning
• Operational optimization
• Customer understanding
• Financial reporting

Understanding users and use cases shapes everything else.

Define the Product

Specify what the data product includes:

Scope: What data is included and excluded?

Granularity: What level of detail is provided?

Freshness: How current is the data?

History: How far back does data go?

Format: How is data structured and delivered?

Clarity on scope prevents scope creep and confusion.

Establish Quality Standards

Define and commit to quality expectations:

Completeness: What percentage of records are fully populated?

Accuracy: How closely does data match reality?

Timeliness: When is data available after source events?

Consistency: How stable are values over time?

Validity: What percentage of values fall within expected ranges?

Publish these as SLAs that users can rely on.

Create Documentation

Build comprehensive documentation:

Overview: What the product is and who it's for

Schema: All fields with definitions and types

Methodology: How data is collected and transformed

Quality: Current quality metrics and known issues

Access: How to connect and query

Support: Who to contact with questions

Documentation is part of the product, not an afterthought.

Build Delivery Mechanisms

Enable users to access the data product:

Direct query access: SQL interfaces to underlying tables

API access: Programmatic retrieval for applications

Export capabilities: Bulk download for offline analysis

Streaming: Real-time access for operational use cases

Match delivery mechanisms to user needs.

Platforms like Codd Business Data Products help organizations create and manage data products with built-in documentation, quality monitoring, and governed access - making it easier to treat data with product discipline.

Implement Feedback Loops

Create channels for user input:

Usage analytics: Track who uses what, how often

Satisfaction surveys: Gather direct user feedback

Issue tracking: Log and address reported problems

Feature requests: Capture enhancement ideas

Feedback drives continuous improvement.

Data Product Lifecycle

Inception

Identify a data need that justifies product investment:

• Clear user demand exists
• Use case has business value
• Data source is accessible
• Team can maintain ongoing quality

Not all data warrants product treatment.

Development

Build the initial data product:

• Define scope and quality standards
• Create transformation pipelines
• Validate data quality
• Write documentation
• Set up monitoring

Ship when quality meets standards, not before.

Launch

Release to users:

• Announce availability
• Provide onboarding support
• Monitor for issues
• Gather initial feedback

Early user experience shapes perception.

Operation

Ongoing maintenance and improvement:

• Monitor quality metrics
• Address issues promptly
• Update for source changes
• Enhance based on feedback

Products require continuous investment.

Evolution or Retirement

Over time, products evolve or end:

• Major versions address significant changes
• Deprecation provides transition time
• Retirement follows migration support

Products have lifecycles - plan for them.

Data Product Roles

Data Product Manager

Defines strategy and roadmap:

• Understands user needs
• Prioritizes features
• Manages stakeholders
• Drives adoption

Brings product thinking to data.

Data Engineer

Builds and maintains pipelines:

• Creates data transformations
• Ensures reliability and performance
• Implements quality checks
• Manages infrastructure

Technical foundation of products.

Data Steward

Ensures quality and governance:

• Defines business rules
• Validates accuracy
• Maintains documentation
• Addresses quality issues

Guardian of data meaning and quality.

Domain Expert

Provides business context:

• Explains data meaning
• Validates business logic
• Identifies quality issues
• Connects data to use cases

Bridge between data and business.

Measuring Data Product Success

Adoption Metrics

• Number of active users
• Number of teams using the product
• Query volume trends
• New user growth

Engagement Metrics

• Query complexity and depth
• Time spent with data
• Feature usage patterns
• Repeat usage rates

Quality Metrics

• SLA compliance rates
• Error and issue frequency
• Time to resolve problems
• Data freshness adherence

Impact Metrics

• Decisions influenced
• Time saved for users
• Business outcomes enabled
• User satisfaction scores

Track metrics continuously and improve based on what you learn.

Common Challenges

Ownership Resistance

Teams may resist owning data products - it's additional responsibility. Address through clear incentives, executive support, and demonstrating value.

Quality Investment

Maintaining quality requires ongoing effort. Budget for this like you would product maintenance - it's not optional.

Documentation Debt

Documentation becomes stale without discipline. Build documentation updates into development processes.

Scope Management

Users want everything. Product managers must prioritize ruthlessly based on value and feasibility.

Getting Started

Organizations adopting data as a product should:

1. Select pilot product: Choose high-value, manageable scope
2. Assign ownership: Name product manager and team
3. Define standards: Establish quality and documentation requirements
4. Build product: Create initial version with full documentation
5. Measure and iterate: Track metrics, gather feedback, improve

Start small, prove value, then expand the approach across the organization.