What Are Data Products? Key Components, Benefits, Types & Best Practices

author-img
by Emily Winks, Data governance expert at Atlan.Last Updated on: January 20th, 2026 | 15 min read

Quick answer: What is a data product?

A data product is a reusable asset combining curated data, metadata, and context for specific business use cases. Organizations treat data products like consumer products with clear ownership and quality standards.

Key characteristics that define effective data products:

  • Reusable and self-contained: Designed once, applied across multiple use cases without rebuilding from scratch.
  • Rich metadata and context: Includes technical specifications, business definitions, ownership, and quality indicators.
  • Product-thinking approach: Managed with dedicated teams, user feedback loops, and continuous improvement cycles.
  • Built for discoverability: Easy to find through catalogs and search, with clear documentation explaining purpose and usage.
  • Governed and secure: Access controls, compliance measures, and quality standards built into the product lifecycle.

Below, we’ll explore: what data products are, key characteristics, benefits, types and examples, distinction from data as a product, how platforms streamline delivery.

Get Latest Gartner MQ D&A Governance Report →Take Atlan Product Tour

What are data products?

McKinsey defines data products as high-quality, ready-to-use datasets that people across an organization can easily access and reuse for various business opportunities.

Data products emerged as organizations struggled with fragmented data efforts. According to Gartner, “urgent demand for self-service data integration and distributed, domain-oriented data consumption has given rise to data delivered as a product — aka data products.”

Rather than treating data as a byproduct of operations, teams curate specific datasets with the same rigor applied to customer-facing products.

A data product might be a Customer 360 view consolidating information from CRM, billing, and support systems. It could be a machine learning model predicting churn risk. The format matters less than whether people can find it, trust it, and reuse it.

What are the 5 core components that make up data products?

Modern data products typically include several layers:

  • The data itself forms the foundation, cleaned and transformed for consumption.
  • Metadata provides context about lineage, quality, and business meaning.
  • Access interfaces allow different systems to consume the product through APIs, SQL queries, or direct connections.
  • Documentation explains what the product contains and how to use it effectively.

Organizations applying this approach report delivering new business use cases 90% faster than traditional methods. The reusability built into data products eliminates repeated work and creates compound value as more teams adopt shared assets.

Let’s explore each layer further.

1. The data layer


Clean, transformed data ready for consumption sits at the foundation. Teams apply business logic, handle data quality issues, and structure information for the intended use cases.

For example, a Customer 360 product might consolidate demographic information from CRM systems, transaction history from billing platforms, and engagement data from marketing tools.

2. The metadata layer


Semantic context about the data makes products understandable and trustworthy. That’s where ingesting and organizing different types of metadata plays a central role.

For instance, technical metadata captures column definitions, data types, and source system information. Business metadata adds descriptions in plain language, explaining what fields mean and how teams use them.

Meanwhile, operational metadata tracks refresh schedules, quality scores, and usage patterns.

3. The interface layer


Different consumers need different access methods. For instance, APIs serve applications that need programmatic access, whereas SQL endpoints allow analysts to query data directly. Meanwhile, file exports support teams using spreadsheets.

Well-designed products provide multiple consumption patterns without requiring consumers to understand underlying complexity.

4. The governance layer


Built-in controls ensure appropriate use.

For instance, access policies define who can discover and consume products, while sensitivity labels indicate handling requirements for regulated data. Audit logs track usage for compliance, and quality thresholds trigger alerts when metrics fall below acceptable levels.

In an active metadata platform powered by automation and AI, these controls operate automatically without manual intervention for each access request.

5. The documentation layer


Comprehensive documentation lives with the product rather than in separate systems.

For example, READMEs explain purpose and scope, while field glossaries define business terms. Meanwhile, usage examples show common patterns, and links connect to related products and source systems. Sample data helps consumers assess fit before building dependencies.

What are the key characteristics of data products?

Effective data products share seven foundational principles that distinguish them from raw datasets or ad-hoc analyses. These characteristics ensure products deliver sustained value rather than becoming abandoned assets.

Data products are:

  1. Discoverable: Data products appear in searchable catalogs with clear metadata. The product’s purpose and scope are immediately apparent without requiring deep technical knowledge.
  2. Understandable: Each product includes descriptions explaining what it contains, why it exists, and how teams use it. Documentation lives with the product rather than in separate wiki pages that drift out of sync.
  3. Addressable: Products have assigned owners responsible for quality and maintenance. Clear accountability means someone monitors product health, responds to issues, and evolves the product based on consumer feedback.
  4. Secure: Appropriate sensitivity labels and access controls protect data while enabling authorized use. Audit trails track who accesses products and how they use them.
  5. Interoperable: Products connect to broader data ecosystems through clear interfaces. Automated, column-level, cross-system lineage shows upstream dependencies and downstream impacts.
  6. Trustworthy: Certification labels indicate product quality and fitness for specific use cases. Quality metrics like completeness, accuracy, and freshness are visible to consumers.
  7. Natively accessible: Products integrate into existing workflows rather than requiring context switches. Browser extensions surface product information where people work. Integrations with notebooks, BI tools, and development environments make products easy to consume.

Modern platforms operationalize these seven characteristics through automation and intelligent workflows. As a result, teams spend less time on manual documentation and more time improving product quality.

What are the benefits of data products?

The product approach addresses persistent data management challenges while accelerating value delivery. Organizations shifting from project-based data work to product thinking report measurable improvements across multiple dimensions:

  • Accelerated time to value as new business use cases can be delivered 90% faster with established data products.
  • Reduced total cost of ownership, up to 30%, which includes technology, development, and maintenance expenses.
  • Improved data quality and consistency as product teams own quality standards and monitor them continuously.
  • Enhanced governance and compliance as teams govern curated products with clear scope and ownership.
  • Faster and more accurate decision-making with self-service access to trusted products.

What are the types and examples of data products?

Understanding how to build data products helps teams identify opportunities. Depending on the business problem they address, data products can take many forms.

The unifying factor across all the below data product types (and examples) is reusability. Each product serves multiple consumers and use cases rather than being built for a single purpose. This flexibility makes products valuable beyond their initial scope.

Let’s look at some popular data product types, with examples.

1. Entity-centric products


These products provide comprehensive views of important business entities. They serve multiple downstream use cases from reporting to machine learning.

Popular examples:

  • A Customer 360 consolidates demographic information, transaction history, support interactions, and product usage into a single source of truth.
  • Employee data products might combine HR records, performance data, and collaboration metrics.

2. Analytical products


Analytical data products transform raw data into insights ready for consumption by business users who need answers but not raw access. Dashboards and reports qualify as data products when treated with product discipline.

For example, a sales performance dashboard becomes a product when it has dedicated ownership, regular updates, and serves multiple stakeholders.

3. Predictive products


Machine learning models packaged as products deliver predictions to operational systems. These products typically include training data, model artifacts, scoring pipelines, and monitoring infrastructure.

Some examples:

  • A churn prediction model scores customers daily and surfaces results through APIs other applications consume.
  • Recommendation engines analyze behavior patterns and suggest next actions.

4. Decision support products


Some products automate parts of decision-making while leaving final judgment to humans. They balance automation with human oversight for high-stakes decisions.

For example, credit risk scores inform lending decisions without making them automatically. Fraud detection systems flag suspicious transactions for review.

5. Data-as-a-Service products


Organizations sometimes package data for external consumption. Such data-as-a-service products generate direct revenue while requiring robust quality controls and service-level agreements.

For instance, weather data APIs provide forecasts to logistics companies. Financial data feeds deliver market information to trading platforms.

Real-world examples:

  • A healthcare provider might create a patient risk stratification product identifying individuals needing preventive interventions.
  • A retailer could build an inventory optimization product balancing stock levels across locations.
  • A financial services firm might develop a regulatory reporting product ensuring consistent compliance across divisions.

What is the difference between data products vs data as a product?

These related concepts address different aspects of data management, though they often work together.

Data products: The tangible outputs


Data products are concrete deliverables that teams create and maintain. A dashboard, a curated dataset, a machine learning model, or an analytical report qualifies as a data product when it meets the principles of discoverability, reusability, and ownership. Products are nouns: things that exist, can be cataloged, and have specific consumers.

Data as a product: The philosophy


The data as a product philosophy guides how you build those products. It means applying product management principles to data assets:

  1. Understanding user needs
  2. Measuring adoption
  3. Iterating based on feedback
  4. Treating quality as non-negotiable

The ‘data-as-a-product’ philosophy emphasizes building for consumers rather than simply collecting and storing data. Teams adopting this approach assign product managers to data initiatives just as they would for customer-facing features.

When to apply each concept


Organizations implement data products to solve reusability and consistency challenges. When multiple teams need similar data, creating a product eliminates duplication. The product approach works best when you can identify clear consumer groups and use cases.

The data-as-a-product philosophy guides how you build those products. Product thinking ensures you design for actual user needs rather than technical convenience. It creates feedback loops that improve products over time. Teams might apply product thinking to internal data assets even if they never expose them externally.

How they work together


Strong data product implementations combine both elements. Teams create tangible products while applying product management discipline. They measure product adoption, track consumer satisfaction, and evolve products based on real usage patterns.

Product managers own the roadmap, prioritizing improvements that deliver the most value to consumers.

Modern platforms support both aspects. They provide infrastructure for packaging and governing data products while enabling the workflows and metrics that product thinking requires.

For instance, catalog capabilities make products discoverable. Usage analytics inform product improvements. Collaboration features connect product teams with consumers.

The choice is not between data products or data as a product. Organizations need the tangible assets that products provide and the discipline that product thinking brings.

Together, they transform data from a compliance burden into a strategic asset.

How do modern platforms streamline data product delivery?

Organizations launching data product initiatives hit three persistent challenges:

  1. Product teams document metadata manually, creating work that never ends.
  2. Consumers struggle to find products because catalogs require constant maintenance.
  3. Quality monitoring depends on custom scripts that break when pipelines change.

These operational burdens prevent programs from scaling beyond initial pilots.

Active metadata management powered by automation


To get started, organizations need strong metadata management powered by automation. As explored earlier, data products can take different forms and emerge from various places within the data ecosystem.

Gartner points out that dynamic and diverse data products still need assembly. Hence, developing strong metadata management practices is crucial for successful data product development.

Traditional approaches to managing this diversity fall short as:

  • Teams extract lineage through batch jobs that run weekly.
  • Business definitions live in spreadsheets disconnected from actual data.
  • Discovery requires someone to manually catalog every new dataset.

Active metadata platforms capture context continuously as data flows through systems.

When transformations execute, lineage updates automatically without human intervention. Usage analytics surface which products drive decisions and which sit unused. Quality rules run on schedules product owners define, alerting them when thresholds fail.

Automation shifts effort from maintaining documentation to improving product quality.

The product marketplace experience


A product marketplace is where organizations package tables, dashboards, and models with complete business context. The marketplace products display everything consumers need to make confident decisions: sample data, field definitions, lineage, ownership, related assets.

In such a setup, domain experts curate products for their areas without becoming metadata administrators. Data governance frameworks apply consistently through centralized policies rather than manual reviews for every access request.

This transforms how teams interact with data. Search understands natural language rather than requiring exact technical names. Results rank by relevance and trust signals like certification status, usage patterns, and quality scores.

Real stories from real customers: Building trusted data products at scale

Porto: 40% governance cost reduction through efficient scaling

“While traditional data catalogs and open-source solutions may have been capable of a metrics catalog, the deeper level of cross-functional collaboration needed to execute Data Mesh meant that Atlan became Porto’s partner of choice going forward.”

Danrlei Alves, Senior Data Governance Analyst

Porto

🎧 Listen to podcast: Porto: 40% governance cost reduction through efficient scaling

Book a Personalized Demo →

Launching products faster while protecting sensitive data: Austin Capital Bank’s modernization

“We needed a tool for data governance, an interface built on top of Snowflake to easily see who has access to what. With Atlan, we launched new products with unprecedented speed while ensuring sensitive data is protected through advanced masking policies.”

Ian Bass, Head of Data & Analytics

Austin Capital Bank

🎧 Listen to podcast: Austin Capital Bank From Data Chaos to Data Confidence

Ready to move forward with data products?

Data products transform how organizations capture value from their data investments. The shift from ad-hoc datasets to managed products eliminates duplication, accelerates delivery, and makes comprehensive governance achievable.

Start by identifying high-impact use cases where reusability matters most. Customer analytics, regulatory reporting, and operational dashboards make strong initial products.

Next, apply the seven principles of discoverability, understandability, addressability, security, interoperability, trustworthiness, and native accessibility. Measure adoption and iterate based on consumer feedback.

Lastly, adopt modern platforms that automate the operational complexity of product delivery at scale. The result is data that people actually use to make better decisions faster. Atlan helps organizations build robust data product portfolios.

Let’s help you build it

Book a Personalized Demo →

FAQs about data products

1. What’s the difference between a dataset and a data product?


A dataset is simply a collection of data, often created for a single purpose and then left static.

A data product is a curated, maintained asset with clear ownership, documentation, quality standards, and interfaces for consumption. Products are designed for reuse across multiple use cases while datasets typically serve specific projects.

Think of datasets as ingredients and data products as complete meals prepared with consistent recipes.

2. How long does it take to build a data product?


Initial data products often take 4-8 weeks to reach production quality, including time for understanding consumer needs, establishing data pipelines, adding metadata, and setting up governance controls. The timeline shortens significantly for subsequent products as teams leverage existing infrastructure and processes.

3. Who should own data products in an organization?


Product ownership typically aligns with domain expertise. Marketing owns customer behavior products, finance owns financial reporting products, and engineering owns operational products. The owner understands business context and consumer needs, though they partner with data engineers for technical implementation.

Successful programs assign dedicated product managers who bridge business and technical teams, treating data products with the same rigor as customer-facing products.

4. How do data products support AI and machine learning initiatives?


Data products provide the curated, quality-controlled inputs machine learning models require. Rather than data scientists repeatedly extracting and cleaning training data, they consume products with documented lineage and quality metrics. This accelerates model development while ensuring consistency across models that use common data sources.

Research indicates GenAI initiatives require product-quality data to deliver value, with potential economic impact of $2.6-4.4 trillion annually across use cases.

5. What metrics should we track for data product success?


Key performance indicators include consumption metrics (monthly active users, query volume), reusability (number of use cases served), quality measures (completeness, accuracy, freshness), and business value (ROI of enabled use cases).

Gartner recommends organizations prioritize delivery of reusable, composable minimum viable data products and establish shared KPIs between producing and consuming teams to measure product success effectively.

6. Can we build data products without new technology platforms?


Organizations can apply product thinking to existing tools, though dedicated platforms accelerate success. The critical requirements are cataloging for discovery, metadata management for context, and governance for quality control.

Many teams start with spreadsheet-based product catalogs before graduating to specialized platforms. However, manual approaches struggle to maintain accuracy as product portfolios grow, making automation increasingly valuable.

Share this article

signoff-panel-logo

Atlan is the next-generation platform for data and AI governance. It is a control plane that stitches together a business's disparate data infrastructure, cataloging and enriching data with business context and security.

Book a DemoStart Tour
 

Atlan named a Leader in 2026 Gartner® Magic Quadrant™ for D&A Governance. Read Report →

[Website env: production]