6 Reasons Why Applying Product Thinking to Data Makes Sense

How can I translate data science into tangible business value? Although data is the most valuable business asset in the 21st century, organizations struggle to deliver business value from data.

And there are many reasons why.

Initially, businesses struggle with developing a holistic strategy for deriving value from data on a large scale. The challenge is not limited to technical skills alone but also includes procedural, structural, and cultural dimensions.

The softer aspects, like organizational culture, staff motivation, and interdepartmental teamwork, are crucial in this context. Such factors frequently obstruct the efficient use of data, creating a discrepancy between the possible benefits of data-driven decisions and the irreal influence on business results.

The solution to effectively leveraging data in organizations blends the skills and approaches from two fields: digital product management and data science. This merging has led to the development of “Data Product Management”, a practice that applies the strategies and user-focused tactics from digital product management to the work of data teams.

We apply product thinking to data - boom.

Last but not least, AWS has identified data product management in their study, 2024 CDO Insights: Data & Generative AI, as one of the approaches to creating visible value.

But why does applying product thinking to data make sense? I came up with 6 main reasons:

1. Treating Data as a Product: At its core, product thinking involves seeing data not just as a byproduct of business operations but as a product in its own right. This shift in perspective encourages us to focus on delivering value to users, whether they're internal teams making strategic decisions or customers interacting with our services.
2. User-Centric Approach: By applying product thinking, we start with the user's needs and problems. This approach ensures that the data products we develop are not only useful but also usable, providing insights that are accessible and actionable for the end user.
3. Iterative Development: Just like any other product development process, applying product thinking to data emphasizes the importance of feedback loops. It encourages continuous iteration based on user feedback, ensuring that the data product evolves to meet changing needs and expectations.
4. Cost Efficiency and Savings: When we align product development with customer and business need sit helps prevent high costs and ensures better investment. If we avoid the development of features or products that don't align with our customer demands, we can achieve cost savings.
5. Cross-Functional Collaboration: Product thinking fosters collaboration across different teams—data scientists, engineers, business analysts, and user experience designers—to ensure that the data product is well-designed, functional, and aligned with business goals.
6. Sustainable Scalability: Viewing data through a product lens prompts us to consider scalability from the outset. It pushes us to design data architectures and systems that can grow and adapt over time, ensuring the longevity and effectiveness of our data products.

Personal takeaways

As I navigate this journey, the concept of applying product thinking to data has been raising the question in my head "Why have we not done that from the start?!"

It's not just about managing data more effectively; it's about transforming data into a strategic asset that drives decision-making, innovation, and customer satisfaction.

I'm eager to continue exploring this field, learning new strategies, and sharing my discoveries.

For those of you at the intersection of data and product management, how have you applied product thinking to your work with data? I'd love to hear your experiences and insights. Reach out for a virtual coffee!