What is a Data Product? A Guide to Cost, Value, and Strategy.

Imagine you run a global retail chain where each regional office independently generates sales reports using different metrics and tools. You probably end up with conflicting insights that get in the way of your inventory and pricing decisions. The solution? Data products that turn your raw, disjointed data into unified, reusable assets that your teams can trust and reuse across the business.  

Unlike traditional data strategies, which focus on collecting and storing information, data products prioritize delivering measurable value through user-focused solutions, embodying a product-oriented approach to data.  

According to a report from Confluence, 72 percent of IT leaders cite substantial benefits from adopting a data product strategy. Yet, 68 percent of enterprise data remains unused, leaving valuable insights hidden and untapped.  

AI and modern data product platforms, like Domo, streamline the process of turning raw data into strategic assets that fuel growth. In this article, we will discuss what data products are and why they matter, and how modern teams can build AI‑powered data products with Domo.  

What is a data product? 

Let’s start with a definition of a “data product.” A data product is a reusable, self-contained data asset enriched with metadata, APIs, and governance, designed to deliver value by addressing specific business needs.  

Think about a healthcare provider’s app for patient care. It aggregates real-time patient data from electronic health records, uses machine learning to predict the chances of patient readmission, and provides customized care recommendations to doctors through a secure interface. Similarly, data products integrate raw data, processing, and interactivity to provide valuable information that you and your team can act on. 

While unrefined raw data sets require manual analysis, data products are ready-to-use solutions. A standalone dashboard can help with visualization, but it lacks the governance and automation features of a data product. 

Core components of a data product 

A data product is built upon several key components to ensure its usability. These include:

1. Data sets 

These serve as the foundation of the data product, providing the raw material we need to extract insights. Data sets can take various forms according to your specific business needs, such as:  

• Curated tables: These are structured data sets that have been cleaned, enriched, and organized for analytical purposes. Curating the data includes removing duplicates, handling missing values, and standardizing formats to ensure reliability. 

  Example: A financial institution might keep a table of customer transactions with timestamps, amounts, and categories to track spending trends or detect fraud.  

• Materialized views: These are precomputed aggregations or snapshots of data that optimize query performance for complex analyses.  

  Example: A logistics company stores daily shipment totals in a materialized view, which helps managers assess operational efficiency without running resource-intensive queries on raw data. 

2. Domain model 

A semantic layer that uses business-friendly terms for the underlying data. It defines key metrics and attributes using terminology that resonates with business users, simplifying technical complexities to ensure a shared understanding of the data. 

3. Access layer 

Specifies how users interact with and consume the data product. It includes APIs for programmatic access, visualization options in business intelligence (BI) tools, and granular access control for data security. 

4. Data products catalog 

A centralized registry or marketplace where users can find and understand available data products. This includes documentation, ownership information, and usage guidelines for accessing these products. 

Now that we have defined data products and their components, let’s explore the costs of not adopting them.

The cost of not adopting data products 

The cost of not adopting data products can be greater than the cost of implementing them. Maintaining traditional data strategies can erode a company’s competitive advantage and lead to significant losses. 

What do you lose by not adopting data products: 

Missed opportunities 
Without easy-to-use data products, organizations may fail to identify and capitalize on critical business opportunities, leading to lost revenue and reduced market competitiveness. Under-monetized dashboards with valuable insights are often too difficult to access or understand. 

Slower decisions 
Delays in accessing and analyzing data directly impact decision-making speed. Manual processes and siloed data sources slow time-to-insight, hindering business agility. 

Inefficiencies 
Manual data wrangling, duplicated team efforts to access and prepare similar data, and ungoverned “shadow IT” solutions indicate poor data product management. Gartner notes that organizations without unified data strategies face up to 20 percent higher operational costs due to inefficiencies. 

A lack of data products makes businesses reactive instead of proactively using their data. By implementing data products, companies can enhance their processes and create new opportunities.  

Now, let’s take a look at the value data products provide. 

The value of data products 

Data products deliver strategic advantages across every layer of an organization. In fact, 92 percent of enterprises report achieving measurable value from their data and analytics investments.   

Data products can: 

Drive business value 
Data products boost revenue and efficiency by turning insights into action. For example, a retailer can use a customer segmentation tool to increase sales through targeted marketing campaigns tailored to specific buyer behaviors. 

Foster innovation 
Data products accelerate experimentation and development of new data-driven applications through trusted data sources. 

Enhance user experience 
Intuitive interfaces and real-time insights make data accessible to all, not just analysts.  

Streamline processes 
Automation reduces duplicate data efforts and inconsistencies, cuts costs, and simplifies data integration for greater operational efficiency. 

These benefits make data products essential for modern organizations, turning data into a strategic asset that drives value and innovation. But what key characteristics should a data product have in order to deliver on this promise? 

Key characteristics of a data product 

For a data asset to operate effectively as a product, it needs to have certain key characteristics to ensure its reliability, usability, and value to consumers. 

Discoverable, understandable, and trustworthy 
A data product should be easily found. It should be packaged with clear metadata and documentation so users can easily find and interpret its contents. Automated quality checks, versioning, and lineage tracking ensure data accuracy, giving consumers confidence that the figures they see today will match those tomorrow. 

Addressable, accessible, and secure 
Data products should offer secure and convenient access through interfaces like APIs. Access must be governed by security measures that protect sensitive information and ensure compliance with data regulations. 

Interoperable, truthful, and valuable 
To maximize their value, data products should follow defined standards to ensure compatibility with other data products and systems, such as BI tools and machine learning (ML) pipelines. They need to serve as a single, accurate source of truth for data, avoiding duplication and inconsistencies. 

By incorporating these characteristics, data products become more than just data assets. But how do they differ from other data concepts, such as data as a product and data platform? 

Data products vs data platform vs dashboards 

Data products, dashboards, and data platforms are sometimes used interchangeably but serve distinct roles in the data ecosystem. They differ significantly in their purposes and outputs.  

For example, consider a retailer who wants to optimize inventory across a network of stores. A data product, such as an automated inventory management system, can integrate their real-time stock data, predict demand using machine learning, and provide them with actionable restocking recommendations.  

A dashboard, in contrast, might offer the retailer a visual snapshot of the current inventory levels in stores and sales trends, helping them quickly monitor changes, but stopping short of automation or in-depth analysis. Behind the scenes, a data platform like Domo provides the infrastructure to help manage dashboards and data products to unify data from warehouses, point-of-sale systems, and suppliers. 

Let’s clarify these concepts by differentiating between a data product vs a data platform and a dashboard. 

Aspects	Data product	Data platform	Dashboard
Primary goal	Delivers actionable, reusable solutions for business needs.	Provides infrastructure for data management and analysis.	Visually display data and KPIs, often as part of a broader data product but limited mainly to reporting.
Output	Tangible tools like dashboards or AI apps.	Tools and services for creating data products.	Static or semi-interactive visual reports or charts highlighting key metrics and trends.
Function	Combines data, processing, and interactivity for end-user value.	Enables data integration, storage, and analytics.	Displays data, often with limited interactivity or processing.
Use cases	Customer segmentation apps, inventory tools, forecasting systems.	Data integration, app development, governance frameworks.	Sales performance dashboards, financial KPI tracking, operational health monitoring.
Data integration	Integrates data into user-friendly, governed solutions.	Connects multiple data sources for unified management.	Relies on pre-integrated data to display visually, without processing or automation.

Let’s explore some real-world examples of data products and use cases that show how data products drive innovation, engagement, and even create new revenue streams. 

Data product use cases 

Data products deliver actionable insights across industries, transforming raw data into strategic tools. Here are some real-world examples of data products showcasing their impact. 

Predictive maintenance 

An energy or manufacturing firm uses data products that ingest sensor streams and maintenance history to predict equipment failures, schedule proactive upkeep, minimize downtime, and extend asset lifecycles. 

• For example, Dal-Tile, a global tile manufacturer, merged data from three ERP systems and equipment-maintenance records into a Domo-powered predictive-maintenance product. This product delivers real-time alerts on repair needs across 300+ plants and replaces week-long reporting cycles, which minimizes unplanned downtime and improves budget adherence. 

Inventory management tool 

A manufacturer creates a data product for real-time visibility into stock levels, supplier performance, and demand forecasts. Users adjust reorder points and production plans automatically, reducing waste and stockouts. 

• For example, Odele tracks sales velocity and SKU-level inventory across hundreds of stores in Domo. This allows them to identify out-of-stock locations and redeploy inventory to high-demand markets, ensuring products remain available without overstocking. 

Customer segmentation app 

A retail company builds a data product that ingests purchase history, web-behavior data, and demographics to identify high-value segments for targeted campaigns. Insights drive personalized promotions, boosting engagement and sales. 

• For example, NorthShore uses Domo to power its customer acquisition and retention programs. It analyzes order frequency, average order value, and product-mix data to refine segment-based promotions and optimize marketing spend. 

Supply chain optimization 

A logistics provider integrates data from suppliers, warehouses, and transport fleets into a single data product, visualizing key metrics like on-time delivery, route efficiency, and carrier performance to drive cost savings and service improvements. 

• For example, RWI Logistics uses Domo to centralize data from its transportation management system, customer support calls, and daily operations into one dashboard. They added heat-map analytics to track inbound call volumes, automating staffing schedules. This reduced missed calls by about 50 percent and minimized overstaffing. 

These real-world examples demonstrate how data products can drive efficiency, enhance decision-making, and create real business value across industries. But delivering this kind of impact requires the right platform.  

So, how does Domo help modern teams take raw data and turn it into powerful, AI-powered data products? 

Building AI-powered data products with Domo 

As an AI and data products platform, Domo helps businesses prepare, visualize, automate, distribute, and build fully governed data products, all in one SaaS solution. Domo simplifies the process of building a data product for modern teams by unifying ETL, analytics, App Studio, and AI services.  

Key capabilities include: 

• Data integration: With over 1,000 pre-built connectors, Domo can ingest data from cloud apps, on-premises systems, flat files, and proprietary APIs. 

• Self-service analytics: Domo’s App Studio offers a low-code canvas for creating custom analytics applications. It allows you to drag charts, forms, and actions onto pages with reusable themes and layout controls. 

• AI and automation: Domo AI and Agent Catalyst inject intelligence directly into data products, and autonomous AI agents that execute end-to-end workflows while respecting governance policies. 

• Embedded analytics: With Domo Everywhere, data products can be white-labeled and embedded into external portals, websites, or customer applications. This helps partners and end customers with self-service analytics and drives new revenue opportunities. 

Ready to start? Watch the ACE Hands-On Webinar on building actionable data products in Domo.