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Features and importance of Data Products

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®

In today’s data-driven world, businesses are constantly looking for ways to leverage data to make informed decisions, optimize operations, and drive revenue growth. A Data Product is an integrated, self-contained data solution that serves as a key enabler of these business goals. It combines data, metadata, and analytical capabilities into a consumable format that solves specific business problems or supports data analytics and business intelligence efforts.

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.

Data Products directly align your data efforts with real business outcomes, providing measurable results and clarity for decision-making.

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.

Features of Data Products

  • Purpose-Driven Design Data Products solve specific challenges. For instance, they can uncover purchase behavior trends or help plan targeted email campaigns for better customer engagement.
  • Self-Service Enablement Forget reinventing the wheel. Data Products let you set up workflows—like segmenting customers for personalized offers—without starting from scratch.
  • Modularity Are you already using tools like Tableau or Power BI? Data Products integrate seamlessly, making it easy to expand your capabilities. Use them to analyze cross-sell opportunities or track product affinity in real-time.
  • Governance and Quality Assurance Data Products prioritize accuracy and compliance. For example, you can ensure data reliability when running loyalty programs for high-value customers.
  • User-Centric Interface Data doesn't have to be complex. With intuitive, user-friendly interfaces, Data Products makes exploring key metrics like campaign click-through rates or average order values easy, without requiring advanced knowledge of data analytics or data science.

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 must prioritize security through access controls, encryption, and compliance.

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.

Types of Data Products

Data Products are categorized based on how they are designed and structured to meet specific needs. Let’s break down the two main types, highlighting their unique characteristics.

Entity-First Data Products or Source-aligned 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.

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.

FAQs

Q1: 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.

Q2: 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.

Q3: 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.

Q4: 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.