Understanding Data Products

Author: Wavicle Data Solutions

Companies are increasingly adopting the concept of data products to tap into the full potential of their data assets. Rather than treating data as a byproduct of operations, businesses are now recognizing it as a valuable product in itself—designed, maintained, and consumed with purpose. This shift ensures that data is more accessible, reliable, and actionable across the organization.

   

At the same time, the emergence of data mesh provides a modern framework for managing these data products, empowering domain teams to take ownership of their data and deliver value through decentralized but governed data practices. So, understanding the different types of data products and their acceptance criteria is important for building a data-centric culture where data products are created and leveraged to their full power. Keep reading for a better understanding of what data products are and how to ensure that your data assets are accessible and usable across your organization.

   

Different types of data products  

In modern data ecosystems, not all data serves the same purpose or carries the same value. Data products come in various forms, each fulfilling specific roles within an organization’s data strategy.

   

Whether it’s raw data, refined insights, or automated decision-making systems, understanding the different types of data products is essential for leveraging data effectively. The classification below will help you maximize the value you extract from data, ensuring that each type of data product meets the needs of its users in unique ways.

  

• Raw data: Unprocessed data stored in systems like databases or data lakes forms the foundation of many analytics efforts. This raw data can come from transactions, sales, or marketing systems and is typically accessed via SQL queries or other extraction tools. 
• Derived data: Once raw data has been processed, aggregated, or transformed, it becomes derived data. Derived data provides more refined insights, like customer segments or campaign performance tables, accessible through SQL or APIs.  
• Algorithms/models: These data products use raw or derived data to generate predictions or insights. For instance, a demand forecasting model or a customer churn prediction algorithm processes data to provide strategic business recommendations.  
• Decision support/dashboards: Tools like reports and dashboards enable users to make data-driven decisions by visualizing data and key performance indicators (KPIs). These products offer a visual interface for stakeholders to interact with data in real time.  
• Automated decision-making: These systems use data to make decisions without human intervention, such as intelligent agents processing refunds or autonomous vehicles making real-time driving decisions.  

 

Acceptance criteria of a data product  

When defining and creating data products for your organization, it’s critical to have a set standard those data products must meet. Those standards should ensure that the results can be used across your organization, are of high quality, and uphold your company’s best practices for how you protect and access data.

  

Start with the items on this checklist to ensure your data products meet core requirements and set your business up for success and innovation. Your data products should be: 

 

• Discoverable: Users should be able to easily find data products through a well-documented catalog or marketplace.  
• Self-describable: Each data product should clearly explain its use, origin, and quality, providing examples of successful implementation.  
• Trustworthy: There should be service-level agreements (SLAs) that guarantee data quality, refresh schedules, and processes for issue escalation.  
• Interoperable: Data should follow standardized keying methods, making it easy to join with other data products and minimizing duplication.  
• Addressable: Once a user is approved for access, they should be able to consume the data via multiple methods, such as reports, APIs, or sandboxes.  
• Secure: Data security measures should ensure that only the right users have access to the appropriate data, with encryption and masking where necessary.  

 

Take a scalable approach to data products  

As businesses become more data-driven, treating data as a product is no longer just an option—it’s a necessity. From raw data to complex models and automated systems, each type of data product plays a vital role in driving insights and decision-making. By establishing clear acceptance criteria and leveraging frameworks like data mesh, organizations can create a sustainable, scalable approach to data management. This allows for greater innovation, collaboration, and ultimately, a more competitive edge in the market.

  

By aligning data product thinking with data mesh principles, organizations can foster a more agile, scalable, and collaborative data ecosystem. This leads to faster innovation, better decision-making, and ensures that data products remain a valuable asset across the business.

  

If you’re looking to understand how to implement data products within your organization or need help aligning your data strategy with modern approaches like data mesh, contact Wavicle. Our team will help you explore how you can maximize the potential of your data assets.