Five Common Mistakes When Implementing Data Products (and How to Avoid Them)

If you work in data, you've likely encountered the term "data products." Whether you're just starting to explore the concept or already deep into implementation, understanding both the benefits and challenges of data products is essential. When done right, they enhance data accessibility, drive business value, and foster innovation. However, common pitfalls can slow progress and create skepticism about the effectiveness of data products.

In this article, we’ll examine five frequent mistakes organizations make when implementing data products and provide guidance on how to navigate these challenges successfully.

Mistake #1: Building data products without applying the "data as a product" methodology

A fundamental misunderstanding between "data as a product" and "data products" can lead to misaligned implementation strategies. While the terms are often used interchangeably, they serve distinct purposes:

• Data as a product: This is a methodology that applies product management principles to data, ensuring quality, governance, scalability, ownership, and value. This approach requires a consumer-centric mindset and lifecycle management.

• Data products: These are the actual outputs—such as datasets, dashboards, APIs, or machine learning models—delivered to consumers. However, without the "data as a product" methodology, these outputs can become isolated, inefficient, and difficult to scale.

Why methodology matters

Without a structured approach to their creation, implementation, and distribution, data products often lack reusability and scalability, limiting their long-term value. If each data product is developed for a single use case without broader applicability in mind, organizations waste resources maintaining redundant or narrowly focused assets. This is akin to designing a new physical product for every individual customer—an approach that quickly becomes unsustainable.

How to avoid this mistake

• Apply product-thinking principles to data initiatives, ensuring data products are designed for broad applicability and scalability.

• Establish clear ownership and lifecycle management processes.

• Prioritize usability and alignment with business objectives rather than purely technical outputs.

Mistake #2: Underestimating the effort needed for change

Successfully implementing data products requires more than just technical execution—it demands a shift in organizational mindset. Many companies underestimate the effort required to embed data product thinking into their company culture.

Key cultural challenges of launching data products

• Leadership buy-in: Without executive sponsorship, data product initiatives often become side projects with limited funding and prioritization.

• Goal alignment: While leadership endorsement is crucial, engaging data teams and business users is equally important. Everyone must understand how data products impact their roles and the broader organization.

• Overcoming resistance: Employees may hesitate to adopt new approaches due to uncertainty or competing priorities.

How to avoid this mistake

• Gain active leadership support to secure funding, remove barriers, and drive alignment.

• Educate teams on the value of data products through training and real-world success stories.

• Set clear, measurable goals that link data product success to business outcomes.

• Provide incentives and recognition for teams that successfully adopt and contribute to the data product framework.

Mistake #3: Neglecting a discovery layer for data products

A well-designed discovery layer is critical for making data products easily accessible and reusable. Without one, users struggle to find the right data, leading to duplication, inefficiencies, and distrust in available data assets.

The risks of an inadequate discovery layer

• Low findability: Users spend excessive time searching for relevant data, slowing down decision-making.

• Data duplication: Without a clear view of existing products, teams recreate similar data assets, increasing costs and governance complexity.

• Lack of context: Data without proper documentation (metadata, lineage, ownership) can be misinterpreted or misused.

How to avoid this mistake:

• Implement a centralized discovery platform, such as a data catalog, to provide visibility into available data products.

• Ensure all data products include rich metadata, ownership details, and clear descriptions.

• Make search and discovery intuitive by integrating the platform into existing workflows and tools.

Mistake #4: Retrofitting legacy data deliverables instead of designing for the future

Many organizations attempt to implement data products by repackaging existing data deliverables. While this may seem like a practical shortcut, it often leads to long-term challenges.

How retrofitting can fail

• Lack of ownership: Many legacy data assets don’t have clearly defined ownership, leading to poor governance and accountability.

• Insufficient metadata: Legacy assets may lack the documentation necessary for effective discovery and reuse.

• Mismatched design: Pre-existing deliverables are often too rigid or too customized to be adapted into scalable, consumer-centric data products.

How to avoid this mistake:

• Start with high-value use cases to build data products from the ground up 

• Gradually replace legacy data assets with well-designed data products that align with business objectives.

• Establish clear ownership and governance structures to ensure accountability.

Mistake #5: Failing to measure the value of data products

Without clear value metrics, data products risk being deprioritized, underfunded, or even abandoned. Measuring impact ensures that data products align with business objectives and continue to deliver tangible benefits.

What happens when value isn’t measured

• Lack of justification for investment: If business leaders don’t see measurable outcomes, they may divert funding elsewhere.

• Low adoption rates: Without tracking usage and feedback, data products may fail to meet consumer needs.

• Missed opportunities for improvement: Continuous enhancement relies on data-driven insights.

How to avoid this mistake:

• Define key performance indicators (KPIs) such as adoption rates, user satisfaction, and cost savings.

• Gather consumer feedback regularly to refine and enhance data products.

• Monitor usage metrics to track demand and assess impact on business processes.

• Share success stories and case studies internally to highlight the value of data products.

Conclusion

Implementing data products effectively requires both technical discipline and cultural transformation. By avoiding these common pitfalls—misunderstanding "data as a product," underestimating cultural change, neglecting discovery layers, retrofitting legacy deliverables, and failing to measure value—organizations can unlock the full potential of their data.

Approaching data products with a strategic, consumer-centric mindset ensures they deliver long-term value, driving efficiency, innovation, and better decision-making across the enterprise. With the right methodologies, leadership support, and performance tracking in place, your data products can become a cornerstone of business success.

Curious to learn how a data catalog can help you launch data products successfully? Book a demo to learn more.