Unlocking Business Value with Data Products

Data Product Definition

A Data Product is an integrated and self-contained combination of data, metadata, semantics and templates. It includes access and logic-certified implementation for tackling specific data and analytics (D&A) scenarios and reuse. A Data Product must be consumption-ready (trusted by consumers), up to date (by engineering teams) and approved for use (governed). Data Products enable various D&A use cases, such as data sharing, data monetization, analytics and application integration. - Gartner®

Data as a Product

Treating data as a product is a transformative approach that shifts the focus from simply collecting and storing data to delivering purposeful, user-centric data solutions. This philosophy emphasizes designing, building, and maintaining data with the end-user in mind—ensuring that data is not only accessible, but also reliable, understandable, and ready for use.

Traditional data management often leads to problems like inefficiencies, disconnected systems (silos), and low confidence in data insights. In contrast, the data-as-a-product approach applies proven product management practices to the entire data lifecycle. This includes clear ownership, consistent quality checks, and a strong focus on what users actually need from the data.

In this model, data is treated as a complete product: something that is discoverable, understandable, reusable, and valuable on its own. These products are built with security and governance in mind, and they follow standards that make them easy to integrate with other systems.

Implementing this approach requires a cultural shift within organizations—from managing pipelines to delivering products. It encourages cross-functional collaboration and continuous iteration, enabling teams to deliver high-quality data assets that directly support business goals.

By embracing the data-as-a-product philosophy, organizations can overcome the shortcomings of traditional data practices and unlock new levels of agility, trust, and value from their data.

Data Products in DataOS

A Data Product in DataOS is a self-contained unit that packages data, metadata, and analytics into a ready-to-use solution. It is developed and managed by dedicated teams to solve specific business problems or support analytics and business intelligence needs.

A Data Product encompasses:

• Data: The core data itself, which can come from various sources.

• Metadata: Information about the data, including its origin, quality, and relevance.

• Transformation Code: Scripts or programs that process and prepare the data for analysis.

• Input and Output Definitions: Specifications for how data is ingested and delivered.

• Discovery and Observability: Mechanisms for finding and monitoring the Data Product.

• APIs: Interfaces for programmatic access to the data (consumption options).

• Documentation: Explanations and instructions for understanding and using the Data Product.

• SLOs (Service Level Objectives): Performance and quality standards for the Data Product.

• Governance: Rules and policies for managing access, security, and compliance.

• Platform Dependencies: Resources like compute and storage required to run the Data Product

Strategic role of Data Products in business

Data Products bridge the gap between raw data and business insights. They:

• Enhance Decision-Making: Provide timely, reliable data to inform strategies.

• Promote Reusability: Enable teams to leverage existing data assets for multiple purposes.

• Ensure Compliance: Incorporate governance to meet regulatory requirements.

• Facilitate Collaboration: Allow cross-functional teams to access and work with consistent data sets.

  For instance, a marketing team can use a Customer Segmentation Data Product to tailor campaigns, while the sales team utilizes the same data to identify high-value leads.

Think of Data Products as purpose-driven solutions designed to transform raw data into insights you can act on. Picture this:

• Need to understand why some customers shop regularly while others don’t?

  • Data Products have the insights you need to uncover the reasons.

• Wondering which products are frequently bought together?

  • That’s what they’re built for.

• Trying to identify your most loyal customers?

  • Data Products offer a precise and reliable approach, enabling you to easily analyze customer purchasing behavior.

Key characteristics of a Data Product

A Data Product is defined by critical characteristics that ensure its usability, functionality, and value.

Discoverable

A Data Product is designed to be easily discovered by users. It includes appropriate metadata, tags, and descriptions, enabling users to find and understand its purpose and contents quickly.

Addressable

A Data Product is assigned a unique identifier, making it easily referencable and accessible within a data ecosystem. This ensures that users can reliably access and work with the Data Product without ambiguity.

Understandable

A Data Product is presented in a manner that is easily understandable to users. It employs clear and intuitive visualizations, documentation, and explanations, facilitating users' understanding of the data and its implications.

Natively accessible

A Data Product is made available in its native format, ensuring seamless integration with different tools and systems commonly used by data consumers. This eliminates the need for complex conversions or transformations, allowing for direct access and utilization.

Trustworthy and truthful

A Data Product adheres to rigorous quality assurance processes and data governance principles. It ensures that the data is accurate, reliable, and transparently sourced, instilling trust and confidence in the insights or outputs it provides.

Interoperable and composable

A Data Product is designed to integrate and interact with other Data Products and systems seamlessly. It follows standardized protocols and interfaces, enabling interoperability and composability, thus allowing users to combine and leverage multiple Data Products for comprehensive analysis.

Valuable

A Data Product provides users with intrinsic value without further processing or integration. It delivers meaningful insights, actionable information, or standalone functionalities that can be used to support decision-making and drive desired outcomes.

Secure

A Data Product incorporates robust security measures to protect the data's confidentiality, integrity, and availability. It implements access controls, encryption mechanisms, and privacy safeguards, ensuring the data is safeguarded from unauthorized access or breaches.

Goal-oriented and business-aligned

A Data Product has a clear purpose and is aligned with specific business objectives. It is designed to solve particular problems or provide distinct value to its users, ensuring that its development and deployment are goal-oriented and impactful.

Responsive

A Data Product is responsive to user needs and environmental changes. It has mechanisms for receiving feedback, adapting to new requirements, and evolving based on user interactions and external factors, ensuring it remains relevant and useful over time.

Reactive

A Data Product is capable of reacting to real-time data and events. It is designed to process and respond to new information dynamically, allowing users to make timely decisions based on the most current data available.

Data Product development with DataOS

DataOS is the platform that helps you develop, manage, process, and deploy Data Products across your organization. It makes the entire lifecycle of Data Products— from ingestion and storage to analysis and delivery—much easier to handle. By bringing all these functions together in one system, DataOS streamlines the process, helping you make better decisions and improve operational efficiency along the way.

Types of Data Products

DataOS classifies Data Products into distinct types, each serving specific roles within the data ecosystem. Understanding these types aids in designing and utilizing Data Products effectively.

Source-aligned Data Products or Entity-First Data Products

Entity-first Data Products focus on the characteristics and origins of the underlying data sources. These products emphasize data quality, governance, and compliance with organizational standards and policies. They are often aligned with the data domains of the organization and are sometimes referred to as Source-aligned Data Products.

Key Features:

• Data is structured around the source systems or entities, such as customers, products, or transactions.
• Prioritizes consistency and accuracy of data.
• Typically supports organization-wide reporting and operational processes.
• Serving as foundational datasets for further processing.

Consumer-aligned Data Products or Model-First Data Products

Model-first Data Products are designed with a focus on the end-user’s needs and use cases, emphasizing semantics and context. Instead of starting with the raw data, these products prototype the desired outcomes first, and then the underlying components are organized to achieve those outcomes. These are also known as Consumer-aligned Data Products.

Key Features:

• Data is modeled to align with user-specific use cases and decision-making processes.
• Highly flexible and often tailored to specific business objectives.
• Emphasizes intuitive interaction for consumers of the data.

Aggregate Data Products

These Data Products combine multiple datasets to provide a comprehensive view across various domains or departments.

Key Features:

• Integrated View: Merges data from diverse sources for holistic analysis.
• Strategic Alignment: Supports organization-wide KPIs and decision-making.
• Complex Transformations: Involves advanced data processing and aggregation techniques.

FAQs

Q1: Why are Data Products important?
Data Products enable businesses to:
1. Gain actionable insights.
2. Reuse, share, and monetize data effectively.
3. Improve decision-making through analytics.
4. Seamlessly integrate data into applications.

Q2: Is consumption part of a Data Product?
Yes, consumption is linked to the consumption-ready layer of a Data Product.

Q3: Is a report/dashboard considered a Data Product?
No, a report or visualization is a way to consume a Data Product but is not a Data Product itself.

Q4: Are tables and schemas included in a Data Product?
Yes, tables and schemas are integral components of a Data Product.

Q5: Can one Data Product serve as input for another?
Absolutely, Data Products can interconnect to support various workflows.

Q6: What makes a Data Product consumption-ready?
A Data Product is considered consumption-ready when it is trusted by consumers, maintained up-to-date by engineering teams, and governed with proper approvals for use.

Q7: Can Data Products evolve over time?
Yes, Data Products are designed to be:
Responsive: Adaptable to user feedback and changing requirements.
Reactive: Capable of processing real-time data for timely decision-making.

Q8: Can multiple consumer-aligned Data Products use the same set of source-aligned Data Products?
DataOS offers the flexibility to build a variety of Data Products with potentially shared underlying data sources. This means that multiple Data Products aligned with different consumers can use the same set of source-aligned Data Products. For example, you could create Data Products using Customer, Product, and Sales data to support marketing campaigns, cross-sell opportunities, and customer 360 use cases.