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Designing a Consumer-Aligned Data Product

Overview

Designing a consumer-aligned Data Product isn't just about connecting data pipelines—it's about crafting insights that matter to your business. In this module, you’ll dive into a structured, right-to-left approach to design a consumer-aligned Data Product that meets real-world business goals with precision.

You’ll learn to translate business needs into well-modeled, high-quality Data Products that are secure, scalable, and insight-driven.

Learning goals

By the end of this module, you’ll be able to:

  • Define meaningful use cases and success metrics
  • Discover and evaluate existing Data Products
  • Plan source-to-consumption design with transformations
  • Design semantic models with governed access
  • Implement quality checks and delivery paths

📘 Scenario

Imagine an online retail company aiming to boost its sales next year. The business team has tasked the Product Manager with developing a Data Product that can uncover valuable insights from customer purchase behaviors. The goal is to identify cross-sell opportunities, helping sales and marketing teams fine-tune their strategies to increase revenue.

As a Product Manager, you must design this Data Product, ensuring it captures essential metrics like cross-sell opportunity scores, purchase frequency, and total customer spending. Designing a Data Product requires meticulous planning and a deep understanding of the business needs. To effectively design the Data Product, consider the following key aspects:

Step 1: Define the use case and metrics

Identify Business Problems

Anchor your design by answering: What decisions will this Data Product support? Talk to stakeholders, understand their pain points, and define the insight they seek.

Example: Unlock customer purchase trends to drive cross-sell strategies.

Define metrics that matter

Pin down measurable outcomes. These become your Data Product’s north star.

Key Metrics:

  • Cross-sell Opportunity Score
  • Purchase Frequency
  • Customer Lifetime Value (CLV)

Checklist:

  • ✅ Are the metrics trackable using current data?
  • ✅ Do they align with business KPIs?
  • ✅ Are they actionable?

Step 2: Discover reusable Data Products

Before building anything new, explore the Data Product Hub for existing assets.

Search & evaluate

  • Use tags, filters, and lineage views
  • Check if any existing product meets your needs partially or fully

Evaluation questions:

  • Are the outputs up-to-date and accurate?
  • Do the included dimensions/measures align with your use case?

Step 3: Design the right data model

Work backwards from your outcome metrics and identify required inputs.

Start with key metrics

In the first step, we have already defined the measurable outcomes in terms of key metrics that align with your business objectives.

Example: Cross-sell opportunity score, purchase frequency, and total spending.

Break down metrics

Identify specific measures (quantitative data points) and dimensions (categorical attributes).

  • Measures: Sales revenue, frequency, etc.
  • Dimensions: Product, customer, time, geography

Evaluation questions:

  • What are the measures directly tied to each metric (e.g., revenue, counts, percentages)?

  • Which dimensions are critical to segmenting or filtering the data (e.g., customer, product, geography)?

  • How do measures and dimensions interact to create actionable insights?

Identify source tables

Identify the data sources containing these dimensions. We require them to build the Data Product. Examples include CRM data, sales transactions, and product inventory data.

  • Customer demographic data (customer_info)
  • Transaction data (purchase)
  • Product information (product)

Evaluation questions:

  • Which storage system holds the customer /Sales information?
  • Which datasets have the necessary measures and dimensions?
  • Are there any gaps in the data that need to be addressed?

Data exploration and understanding

Explore and understand the content and structure of your datasets using Metis and Workbench. This will help you understand the raw data.

Use tools like Metis or Workbench to:

  • Discover required fields
  • Validate data types and cardinality
  • Note any transformations or joins required

Evaluation questions:

  • What are the key transformations needed?
  • Are there any missing or outlier values that need to be handled?

Step 4: Identifying the need for ETL process

The ETL process is critical for converting raw data into a usable format. It involves extracting data, transforming it based on business logic, and loading it into a storage system. This process enables the creation of source-aligned Data Products, which represent data in its original form with minimal transformation. These source-aligned products can then be used to create consumer-aligned Data Products.

If your data is already in a usable, structured format, you can skip this step.

Create ETL or Flare workflows to:

  • Clean and normalize fields
  • Join multiple sources
  • Maintain lineage

ETL essentials:

Design an ETL pipeline to process raw data into structured datasets.

  • Extract: Retrieve data from various sources.
  • Transform: Clean and aggregate data for analysis.
  • Load: Store processed data in a central data warehouse.

Evaluation questions:

  • What transformations are essential for data preparation?
  • How often should the ETL process run to maintain data freshness?

Define data quality checks

Embed quality checks to validate data integrity during the ETL process.

Quality checks include:

  • Completeness: Ensure no missing values.
  • Uniqueness: Avoid duplicate records.
  • Consistency: Validate data types and formats.

Evaluation questions:

  • Which quality checks are most critical for this product?
  • How will issues be detected and resolved?

Step 5: Define the semantic model

The semantic model acts as the business-facing lens into your data. It simplifies exploration and enforces structure.

When to create a semantic model

Creating a semantic model can be particularly beneficial in situations such as:

  • When data needs to be accessible to a wide range of users with varying levels of technical expertise
    For example, a marketing team can easily view customer data through simplified metrics like "Total Sales" instead of raw database fields.

  • When there is a need for a single, consistent view of data across different departments or systems
    For instance, different departments like finance and sales may define revenue differently. A semantic model standardizes the definition of revenue across systems.

  • When complex data needs to be made more understandable and usable for business decision-making
    For example, renaming column names to more business-friendly terms can help stakeholders make better decisions.

  • When building applications to answer data-related questions using large language models (LLMs)
    Semantic models help structure and clarify data, making it easier for LLMs to generate accurate and context-aware answers.

Model components

  • Tables: Purchase, Product, Customer
  • Measures: Purchase frequency, total spend
  • Dimensions: Region, age group, product category
  • Relationships: Customer → Purchases → Products

Evaluation questions:

  • Identify entities to define logical tables
  • What are the critical dimensions and measures?
  • How will relationships between entities be represented?
  • Does the model capture all necessary metrics?

Step 6: Ensure data security and compliance

Design with data governance and privacy in mind.

Define Policies

  • Who gets access to which fields or rows?
  • Use RBAC and ABAC models

Apply masking

Mask sensitive PII like emails or phone numbers.

Evaluation questions:

  • Have we classified sensitive data?
  • Are access controls reviewed regularly?

Step 7: Define SLOs and quality gates

Great Data Products are reliable. Define Service Level Objectives (SLOs) to maintain trust.

SLO categories

  • Architecture: Performance, uptime
  • Quality: Completeness, freshness, validity
  • Governance: Masking, lineage, access audits, compliance with regulations like GDPR, HIPAA, etc.

Quality checks

  • Validate schemas
  • Ensure uniqueness (e.g., customer_id is unique)
  • Track nulls and outliers

Step 8: Enable output and delivery

Identify how end users will consume the Data Product and the methods used for data delivery, such as APIs or connecting with BI tools.

APIs and BI tools

  • Use Lens to expose datasets
  • Define API contracts (GraphQL, REST)

Evaluation questions:

  • Do APIs meet downstream tool requirements?
  • Are outputs discoverable and documented?

Next step

You’ve sketched a complete design blueprint. Now it's time to bring it to life.

Each next module will guide you through building this design into a working consumer-aligned Data Product. Let’s build it—one module at a time!

👉 Start with configuring source connectivity