Business Data Products Explained: From Raw Data to Business Value

Business data products represent a shift in how organizations think about data. Rather than treating data as a raw material that analysts must refine for each use, data products package data with context, quality guarantees, and interfaces that make it directly usable for business decisions.

This product-oriented approach addresses the persistent gap between data potential and data value.

What Makes Data a Product

Beyond Raw Data

Raw data requires significant effort before use:

• Understanding what columns mean
• Determining data quality and limitations
• Figuring out how to access and query
• Validating that results are correct
• Maintaining knowledge over time

Each analyst repeats this effort, and each may reach different conclusions.

The Product Wrapper

A data product adds layers that make data usable:

┌─────────────────────────────────────────────┐
│           Business Data Product              │
├─────────────────────────────────────────────┤
│  Interface Layer                             │
│  - API access                               │
│  - Semantic layer integration               │
│  - BI tool connectivity                     │
│  - Conversational access                    │
├─────────────────────────────────────────────┤
│  Context Layer                               │
│  - Business definitions                     │
│  - Metric calculations                      │
│  - Documentation                            │
│  - Lineage information                      │
├─────────────────────────────────────────────┤
│  Quality Layer                               │
│  - Data validation                          │
│  - Freshness guarantees                     │
│  - Accuracy monitoring                      │
│  - SLAs and commitments                     │
├─────────────────────────────────────────────┤
│  Data Layer                                  │
│  - Underlying data sources                  │
│  - Transformations and joins                │
│  - Storage and performance                  │
└─────────────────────────────────────────────┘

The product is not just data - it is data made ready for use.

Components of a Business Data Product

1. Defined Domain

Each data product addresses a specific business domain:

Customer data product: Everything about customers - profiles, segments, behavior, health scores

Sales data product: Revenue, pipeline, conversion, performance metrics

Product data product: Usage, adoption, features, performance

Clear domain boundaries prevent overlap and confusion.

2. Semantic Definitions

The product includes precise business definitions:

product: sales_data_product
metrics:
  - name: monthly_recurring_revenue
    definition: |
      Sum of contracted monthly values for active subscriptions.
      Excludes one-time fees, services, and overages.
    calculation: |
      SUM(subscription.monthly_value)
      WHERE status = 'active' AND type = 'recurring'
    owner: finance_ops
    certified: true

  - name: net_revenue_retention
    definition: |
      Revenue from existing customers this period divided by
      their revenue last period. Accounts for expansion,
      contraction, and churn.
    calculation: |
      (starting_revenue + expansion - contraction - churn)
      / starting_revenue
    owner: finance_ops
    certified: true

Users know exactly what they are getting.

3. Quality Guarantees

Data products commit to quality standards:

Freshness: "Data is updated daily by 6 AM EST" Completeness: "Customer records are 99%+ complete for required fields" Accuracy: "Revenue figures match finance systems within 0.1%" Availability: "99.9% uptime during business hours"

These are not aspirations but commitments that are monitored and enforced.

4. Consumption Interfaces

Multiple ways to access the product:

API access: Programmatic queries for applications Semantic layer: Governed metrics for BI tools Conversational: Natural language questions Direct SQL: For advanced analytics needs Exports: For systems that need file-based integration

Users choose the interface that fits their workflow.

5. Documentation

Comprehensive documentation embedded in the product:

Business documentation: What the product provides, for whom Technical documentation: How to access and query Data dictionary: Every field explained Lineage: Where data comes from, how it transforms

Documentation is part of the product, not an afterthought.

Building Business Data Products

Step 1: Identify High-Value Domains

Start where data products will have the most impact:

Questions to assess value:

• How many people need this data?
• How often do they need it?
• What decisions depend on it?
• How much effort is currently required to use it?

Prioritize domains with high demand and high current friction.

Step 2: Define the Product Scope

For each product, clarify boundaries:

What is included: Core entities, metrics, dimensions What is excluded: Related but separate domains Quality commitments: SLAs you will maintain Access model: Who can use it and how

Clear scope prevents scope creep and sets expectations.

Step 3: Build the Semantic Layer

The semantic layer is the heart of the data product:

• Define all metrics with precise calculations
• Map relationships between entities
• Establish business rules and filters
• Document everything thoroughly

This layer provides the business context that makes raw data usable.

Step 4: Implement Quality Controls

Quality must be actively managed:

Validation rules: Check data against business rules Monitoring: Track quality metrics continuously Alerting: Notify when quality degrades Remediation: Processes to fix quality issues

Quality commitments require operational support.

Step 5: Create Consumption Interfaces

Expose the product through needed channels:

• Connect to semantic layer platforms
• Create API endpoints if needed
• Enable BI tool access
• Set up conversational access through platforms like Codd AI

Multiple interfaces increase product utility.

Step 6: Establish Operations

Data products require ongoing attention:

Ownership: Clear accountability for the product Maintenance: Regular updates and improvements Support: Help for users with questions Evolution: Adding capabilities based on feedback

Treat it like a software product with a development lifecycle.

Data Product Patterns

Domain-Oriented Products

Products organized around business domains:

• Customer Data Product
• Sales Data Product
• Marketing Data Product
• Product Usage Data Product
• Financial Data Product

Each product is comprehensive for its domain.

Entity-Centric Products

Products focused on key business entities:

• Customer 360 Product
• Order Analytics Product
• Product Performance Product

Deep coverage of a single entity type.

Use-Case Products

Products built for specific analytical needs:

• Executive Dashboard Product
• Sales Performance Product
• Customer Health Product

Optimized for particular consumption patterns.

Governance for Data Products

Product Ownership

Every product needs clear ownership:

Product owner: Business stakeholder accountable for value Technical owner: Data team member responsible for implementation Steward: Person monitoring quality and usage

Shared ownership models work best.

Certification Process

Establish trust through certification:

Development: Product being built, not for production use Reviewed: Technically complete, under business review Certified: Validated for business decisions Deprecated: Being phased out, use alternatives

Clear status prevents misuse.

Change Management

Products evolve carefully:

Change request: Proposed modifications documented Impact analysis: Understanding of downstream effects Review: Approval from stakeholders Deployment: Controlled rollout with validation Communication: Users notified of changes

Changes should not surprise consumers.

Measuring Data Product Success

Adoption Metrics

• Number of active users
• Query volume and trends
• Use cases supported
• New use cases enabled

Quality Metrics

• SLA compliance rates
• Data quality scores
• Incident frequency
• Time to resolution

Business Metrics

• Decisions supported
• Time saved versus alternative approaches
• User satisfaction scores
• Return on investment

Track metrics that demonstrate value.

The Product Mindset

Building data products requires shifting mindset:

From: Data as byproduct of operations To: Data as product deliberately designed for consumers

From: Analysts figure out how to use data To: Data products are ready for use

From: Quality is nice to have To: Quality is a product commitment

From: Documentation is optional To: Documentation is part of the product

This mindset shift - supported by platforms like Codd AI that make it practical to create and maintain business data products - is what turns data from a theoretical asset into an actual source of business value.