The importance of data products

What are data products?

Data products are tools that integrate data analysis and business logic into user-friendly packages to meet specific organizational requirements. They depart from traditional static reports and basic dashboards by offering dynamic visualizations that integrate data and analytics with the business context.

The data products market is growing rapidly as organizations seek to use their data for AI, analytics and BI initiatives. Organizations seek more than raw data; they want packaged insights. Domo, Qlik and Looker are key innovators, each offering platforms enabling businesses to build sophisticated data products. These platforms allow users to create data-driven applications and visualizations, facilitating informed decision-making and driving business growth in an increasingly data-centric world.

As a real-world example, a digital consumer products company developed a "Customer 360" data product that collects information from multiple touchpoints -- such as its CRM system, website interactions and social media engagements -- to generate detailed customer profiles.

The data product provides role-specific views that are customized to meet business needs. Marketing teams can develop targeted campaigns using segmentation and personalization tools. Product managers can evaluate feature usage patterns alongside customer satisfaction correlation metrics, while customer support teams can access a comprehensive history of customer interactions to deliver context-aware services. It also gives executives detailed insights into customer acquisition expenses and their lifetime value.

This system creates tailored views matching user needs and technical abilities to enable access to complex data without specialized analytical expertise. AI capabilities detect potential customer churn risks while suggesting optimal customer engagement tactics and pinpointing product problems early.

The cultural shift required

For organizations to think about data as a product, a cultural shift to emphasize the importance of data across the business as a valuable asset is often needed. Even the quality of data entry matters.

Data quality mindset. Frontline employees who input customer information and operations staff who track production data must recognize how their work affects subsequent analysis. Data accuracy should be a collective responsibility across an organization, rather than confined to the IT and data teams.

Data literacy. All organizational employees should possess adequate data literacy skills to properly interpret and use insights from data products. Business outcomes depend on data impact, making ongoing training and clear communication essential.

Product thinking. Data teams need to move their focus from project-based work to product-centric development that emphasizes user requirements and continuous enhancement while assessing success based on business outcomes instead of just technical performance indicators.

Cross-functional collaboration. Data specialists and business domain experts who know how to apply insights work together closely to create successful data products.

Measuring business impact

Well-designed data products generate substantial business impact in three ways:

1. Accelerated time to value. Ready-to-use analytics drastically reduce the gap between collecting data and executing business actions. Businesses can access crucial insights within hours or days instead of waiting several months.
2. Innovation catalyst. Organizational access to data enables widespread innovation. Teams can explore innovative methods while rapidly testing theories and making data-driven adjustments instead of relying on instinct.
3. Cross-functional alignment. Data products unify business performance perspectives across departments, eliminating barriers and fostering synchronized organizational activities and the democratization of data.

Data products: A foundation for AI advancement

The fundamental basis for generative AI and AI agents to reach their full transformative power lies in well-structured data products. This relationship works in several key dimensions:

Quality-first intelligence. The effectiveness of generative AI and autonomous agents depends entirely upon the data quality they use for training and operation. Well-designed data products provide AI systems with high-quality data that is both contextually relevant and properly governed to prevent the "garbage in, garbage out" scenario.

Semantic understanding. Contemporary data products embed extensive metadata, business glossaries and relationship maps to give AI systems the necessary context about their data sources. The semantic layer allows generative AI to accurately understand information within specific domains instead of making broad generalizations.

Trusted retrieval. AI systems require dependable access and retrieval systems to obtain accurate information for responses and actions. Implementing clear interfaces and versioning alongside defined permissions establishes a reliable retrieval layer that guarantees AI systems access to current and suitable information.

Feedback integration. AI agents with users and systems produce necessary feedback signals that identify information gaps, ambiguities and new requirements. Feedback integration results in continuous improvement, enhancing both the data product and the AI agent's effectiveness over time.

As organizations continue to adopt data product strategies, data product teams emerge to combine technical expertise with deep business knowledge. These cross-functional teams are becoming essential to competitive advantage, transforming data from a mere resource into a central driver of value creation.

Stephen Catanzano is a senior analyst at Enterprise Strategy Group, now part of Omdia, where he covers data management and analytics.

Enterprise Strategy Group is part of Omdia. Its analysts have business relationships with technology vendors.