Data product (original) (raw)

What is a data product?

A data product is a reusable, self-contained package that combines data, metadata, semantics and templates to support diverse business use cases. It can include components such as datasets, dashboards, reports, machine learning (ML) models, pre-built queries or data pipelines.

Data products are developed with a product-thinking approach and by applying traditional product development principles. This approach involves understanding user needs, prioritizing high-value features and iterating based on feedback. Ultimately, it treats data as a product designed to solve specific user problems.

Data products are built to be discoverable, interoperable and actionable. They enable everyone—from business users and data analysts to data scientists, data stewards and engineers—to extract meaningful value from data trapped within an enterprise.

The concept of data products gained prominence in 2019 when Zhamak Dehghani, a director of technology for IT consultancy firm ThoughtWorks, introduced data products as a core component of the data mesh architecture. A data mesh is a decentralized data architecture that organizes data by specific business domains (such as marketing, sales and customer service) to provide more ownership to the producers of a given dataset.

Key characteristics of a data product

To function effectively, a data product must exhibit several key characteristics:

Discoverable

Stakeholders should be able to easily discover and find the right data product for their use case.

Understandable

A data product should include clear metadata and be structured according to specific business domains, enabling data consumers and domain teams to interpret and apply the information effectively.

Interoperable

Data products should integrate seamlessly with other systems to deliver consistent insights across platforms.

Shareable

Data products should be packaged as a cohesive unit that can be distributed easily across the organization, ensuring consistent usage and understanding among teams.

Secure

A data product should have access controls and security measures in place to ensure that only authorized users can access the data while maintaining compliance.

Reusable

A well-designed data product is built from modular components that can be repurposed to create new data products or derivative insights, increasing efficiency and reducing redundant efforts.

Why are data products important?

McKinsey reports that data-driven companies are 23x more likely to acquire customers and 19x more likely to be profitable. However, despite the growing demand for data-driven decision-making, many organizations continue to face obstacles such as data silos, vendor lock-in and compliance risks due to insufficient data governance frameworks.

To address these challenges, some organizations have adopted a data-as-a-product approach, treating data as a managed, consumable asset rather than a byproduct of operations.

Data-as-a-product methodologies emphasize structuring and governing data to inform business decisions and improve user experience. Building on that foundation, data products provide a structured, self-service approach to data management, reducing reliance on technical teams while supporting real-time decision-making.

Organizations that invest in data products can experience improvements in data access, interoperability, data storage and governance. Across industries, data products have the potential to enhance automation, support data-driven decision-making and help companies align their data strategies with long-term business objectives. By leveraging robust data platforms, machine learning models and visualization tools, organizations can empower teams to maximize their data.

Data products often achieve these advantages by empowering various roles within an organization:

• Data scientists and AI engineers gain faster access to data and relevant items, accelerating the development and deployment of AI and ML solutions.
• Data engineers benefit from automated testing, deployment and data curation, ensuring pipelines meet the data quality standards and service-level agreements specified in data product contracts.
• Data analysts and consumers receive timely, reliable data that aligns with their domain-specific needs and can be quickly updated without depending on a central IT team.
• Data stewards can maintain strong governance and compliance through data contracts, setting clear guardrails that protect data and keep it secure.

Data-as-an-asset vs. data-as-a-product

The way organizations manage data has evolved from a passive, asset-based approach to an active, product-driven strategy.

Data-as-an-asset (traditional approach)

Traditionally, companies have treated data primarily as something to gather and store. This approach puts data in a central data warehouse or source system, organizing it by subject area (such as finance or marketing) and assigning ownership to centralized teams. Success is often measured by data volume, such as terabytes stored, with the hope that by simply having more data, employees will use it.

However, metadata is typically defined by IT departments and not business-friendly for data consumers. As a result, many efforts with data assets revolve around descriptive analytics and reporting, looking backward at what happened rather than using data proactively to solve business questions.

Data-as-a-product (new approach)

In contrast, viewing data as a product shifts the focus from storage, to usage and value creation. Data products experience a data product lifecycle and are designed, tested and iterated upon—much like software products that follow an Agile or DataOps methodology.

Ownership is domain-specific (for example, a marketing data product managed by marketing experts), which keeps data relevant and high-quality. Data is also curated for specific consumption needs, with rich metadata that is driven by the business. This ensures that data products are easily discoverable and understandable by business users.

Because data owners take responsibility for data products, there is continuous monitoring of the usage, quality and value derived from a product via feedback loops with end users.

Success is measured by how data improves decision-making, drives revenue or reduces costs, rather than simply by how many terabytes are stored. As a result, data product initiatives can solve business questions with advanced analytics, such as predictive and prescriptive modeling.

Components of a data product

A well-structured data product consists of several components that enable functionality and usability within an organization’s data ecosystem:

• Data sources: The origins of the data, encompassing databases, data warehouse, data lakes, data lakehouses or real-time data streams.
• Data pipelines: Automated workflows responsible for ingesting, cleaning, transforming and loading data into a structured format suitable for analysis.
• Data models and schemas: Defined structures that standardize data organization, enhancing accessibility and semantic consistency. Often, these rely on SQL for querying and transformations.
• Interfaces and APIs: Mechanisms that facilitate integration with business applications and apps, ensuring seamless and secure data access.
• Visualizations and dashboards: User-friendly tools that present insights through interactive reports or analytical displays, aiding in data interpretation.
• ML models: Predictive algorithms that analyze patterns within the data, supporting informed decision-making through advanced computing.
• Security and governance controls: Policies and measures that ensure compliance with data governance regulations, track data lineage and manage access controls to maintain data integrity and security.

Types of data products

Data products can be categorized based on the data’s quality and refinement levels. Types of data products include:

Source-based

Data products from source systems. This raw (or with minimal transformation) type of data product is often the foundational building block for use cases such as data science and generative AI.

Master-based

Data products that have been curated and consolidated into master data that standardizes key business entities (such as customers or products) to ensure consistency across systems.

Insight-based

Data products that are refined, processed and designed to support decision-making and generate actionable insights.

Data product lifecycle

By following a structured, product management lifecycle, data teams can build data products that are continuously valuable, scalable and aligned with evolving business needs.

​The key stages of a data product lifecycle include:

1. Define: Define the business objective, use case, design specification and data contract. This includes attributes like terms, conditions and service level agreements.
2. Development: Build the data product components, such as tables, views, models, files and dashboards. Then, ​test against the data contract.
3. Package: Curate the data product components into a reusable package, enriched with business and technical metadata for easy discovery within a data catalog or other data storage tool.
4. Govern: Manage the access permissions of the data product per the data contract.
5. Publish: Publish your data product to a portal for discovery.
6. Consume: Allow consumers across the organization to easily access the data product to address various challenges. Gather consumer feedback for enhancements for future iterations.​
7. Monitor and iterate: Conduct ongoing activities like monitoring usage, quality and access. Implement release management for version changes to published data products.​​
8. Retire: Retire the data product due to reasons like lack of usage or non-compliance. Deprecate the product, inform consumers, archive products and clean up resources.

Data product use cases

Organizations across industries rely on data products to drive business value, support strategic initiatives and solve critical business problems.

Real-life examples of data products include:

• A large national bank implemented a single customer data product that powers 60 diverse use cases—ranging from real-time credit risk scoring to AI chatbots—across multiple channels. As a result, the bank earned an additional 60millioninannualrevenueandprevented60 million in annual revenue and prevented 60millioninannualrevenueandprevented40 million in losses.
• A consumer packaged goods (CPG) firm introduced data products to streamline data usage, improving efficiency and scalability. By deploying over 50 cross-functional teams to implement data-driven solutions, the company increased EBITDA by 18% over two years.

Building and scaling data products

Successfully developing data products requires a strategic approach that includes understanding data consumption, mapping data interactions, testing market value and iterating for scale.

Analyzing data consumption patterns

The first step in creating a data product is analyzing current data consumption within the organization. This step involves identifying target users, understanding the data they consume and why that data is important to them.

Reviewing data usage in terms of volume, frequency, sensitivity and type provides insights into which datasets hold the most value. By prioritizing high-impact user groups, organizations can help ensure initial efforts focus on areas with the greatest potential for business impact.

Mapping the data journey

Once data consumption patterns are clear, the next step is mapping the data journey. Creating detailed maps of real-world data interactions helps visualize how data flows across different systems and teams.

These maps can serve as a foundation for brainstorming new revenue-generating use cases for data products. Developing hypotheses on how data products can improve business processes can help organizations begin to explore ways to turn raw data into meaningful, actionable insights.

Iterating and scaling

With validated insights, the next step is to iterate and scale. Rather than relying solely on central IT teams, organizations can foster agility and innovation by empowering business domains and teams to refine and enhance the data product. Once improvements are made, the project can be expanded to more teams and domains, ensuring that the data product scales effectively and continues to drive business value.