Why deliver your data as a product, but not as an application is important and critical

Delivering data as a product rather than an application is increasingly recognized as a critical strategy for organizations that want to maximize the value of their data assets. Here’s why this approach matters and how it addresses key challenges:

1. Data Products Prioritize Reusability and Scalability

• When data is treated as a product, it is designed to be consumed by multiple users, systems, or applications, rather than built for a single, specific use case. This means that data products are typically created with reusable APIs, standardized formats, and consistent quality standards.
• By focusing on reusability, data products can more easily scale across different business functions or applications, driving efficiency and enabling faster innovation by eliminating the need to duplicate data efforts across the organization.

2. Fosters Data Democratization and Accessibility

• Delivering data as a product makes it accessible to different teams and users across an organization. With well-documented data products, different business units can leverage data independently, fostering a culture of data-driven decision-making.
• In contrast, data delivered through specific applications often remains siloed within that application’s ecosystem, restricting who can access it and use it. This siloing limits the organization’s ability to derive insights from data comprehensively and diminishes the return on investment in data.

3. Enhanced Data Quality and Governance

• Data products are typically subject to quality control and governance standards because they are designed for broad use. This is essential for building trust in the data: users know that the data they are working with is consistent, accurate, and updated according to agreed-upon standards.
• In contrast, data within a specific application might not be as rigorously governed, especially if the application has narrow use cases. Misaligned governance between applications can create inconsistencies and duplicate work, leading to higher maintenance costs and lower quality insights.

4. Enables Agility and Faster Decision-Making

• Data products allow organizations to move faster because teams can access data on demand without waiting for custom data pulls or application development. When data is treated as a product, it can be consumed directly by users, data scientists, or other systems, allowing teams to respond more quickly to market changes or operational needs.
• In contrast, an application-centric approach may require more time to make data adjustments, extend functionality, or pivot, as every new data requirement often needs development within the specific application. Data as a product removes this dependency, making data access more flexible and streamlined.

5. Supports Modern Data Architecture and Decentralization

• Many organizations are moving towards decentralized data architectures, such as data mesh, where different domains own and manage their data as products. By treating data as a product, teams within various domains can independently develop and share data without needing centralized application development.
• An application-centric approach usually reinforces centralization, as applications are developed and maintained by IT or a specific team, which can create bottlenecks. Data as a product aligns with modern, decentralized architectures, making it easier to manage and scale data operations across multiple teams.

6. Promotes Interoperability and Integration with Other Technologies

• Data products are often designed to be technology-agnostic and interoperable, meaning they can plug into different analytics, machine learning, and reporting platforms without extensive reworking. This interoperability is essential for leveraging modern analytics tools and staying competitive in a rapidly evolving tech landscape.
• Applications, however, can lock data into specific ecosystems, making it difficult to use outside of that application. This can hinder the ability to adopt new technologies or integrate data with other systems, limiting the flexibility needed to adapt to changing market demands or to enhance analytics capabilities.

7. Improves the Customer Experience

• Delivering data as a product enhances customer experience because it allows for more accurate, timely, and comprehensive insights into customer needs and behaviours. For example, if a company’s marketing, sales, and customer service teams all have access to consistent, up-to-date customer data, they can offer more personalized, responsive interactions.
• If data is siloed in specific applications, different teams may have fragmented views of the customer, which can lead to inconsistent and disjointed interactions. Data as a product approach ensures a unified view, enabling better alignment and a more cohesive customer experience.

Conclusion

Delivering data as a product rather than an application makes data more reusable, accessible, and scalable, supports faster and more reliable decision-making, and aligns with modern data architecture principles like decentralization. It addresses many of the limitations of application-centric approaches, such as data silos, limited accessibility, and poor scalability. For organizations looking to be agile, data-driven, and innovative, adopting a data-as-a-product strategy is becoming essential.