Although almost every company in the world recognizes the power of data, most struggle to unlock its full potential. Companies such as Google, Amazon, and Uber that primarily deal with data are among the most valuable in the world, in terms of market capitalization, business performance, and innovation. One of the key reasons for their success is managing data like a product.

So, what is a data product? A data product is the application of data and analytics for improving business performance including monetization. Generally, when a firm develops a monetizable product, whether it’s digital or non-digital, the goal is to create something that meets the needs of a wide and diverse range of users. A data product strikes a balance between a core/base product with universal appeal while ensuring it can be customized for specific use cases.

Data products like Google Analytics, Netflix’s Recommendation Engine, Spotify Discover Weekly, Salesforce Einstein, NielsenIQ, and ZoomInfo are considered true data products, while databases (like Oracle, PostgreSQL, Snowflake, and Talend) and CRM/ERP systems (like SAP S4 HANA, Workday, and HubSpot) are not? So, how is a data product different from databases or data-centric applications like the ERP or the CRM system? Here are the six key characteristics of an ideal data product:

1. Purpose-Driven and Persona-Centric: A data product serves a specific purpose tailored to its audience, offering context-sensitive solutions designed with a specific persona in mind.
2. Actionable Insights: A data product goes beyond data capture and storage, providing actionable insights in near real time such that users can apply to make informed decisions to measure and improve business processes. Netflix’s recommendation engine adapts to a user’s watching habits near real-time, while Salesforce Einstein provides immediate predictions and insights based on live sales data.
3. Data Augmentation: A data product combines various data types – Zero-Party (ZPD), First-Party (FPD), Second Party (SPD), and Third-Party Data (TPD) – to offer deeper, more relevant insights. These products are designed to handle large volumes of data and scale seamlessly with increased usage. Google Analytics, for example, is very adaptable to managing data in websites of various sizes and shapes.
4. Security and Privacy Compliance: A data product adheres to strict security and privacy standards, ensuring that the data is protected.
5. User-Centric Design: The data product design is intuitive and aligns with specific user workflows, making it easy to navigate while presenting data in a meaningful context.
6. Monetization Potential: Data products often create new revenue streams for organizations through licensing, subscriptions, APIs (Application Programming Interfaces), embedded analytics, and more. Netflix’s recommendation engine keeps users engaged, increasing retention rates. NielsenIQ offers insights into consumer behavior, preferences, and trends in Retail and CPG firms.

How are these data products created and managed? Here are the key principles of data product management for a forward-looking approach to product management.

1. Prioritize Customer-Centric Solutions: The true mark of a successful data product is its ability to solve real problems and meet users’ needs, not just present data. Every feature should be crafted to drive meaningful improvements in business performance, informed by in-depth user research, feedback, and an understanding of evolving industry trends and business needs.
2. Embrace Continuous Iteration: In the fast-paced world of data, standing still isn’t an option. Data product managers must be ready to iterate constantly, refining algorithms, expanding data sources, and adjusting outputs to keep the product relevant, accurate, and impactful. The early versions of the data product may rely on small datasets, but as the data product scales, it must evolve seamlessly to handle increasing data volume, variety, and velocity – without sacrificing performance.
3. Master the Art of Trade-Offs: Data product management is a delicate balancing act. Managers must navigate trade-offs such as short-term wins versus long-term sustainability, cost versus functionality, and speed versus quality. Successful data product managers stay laser-focused on customer needs while strategically prioritizing features that align with the business’s overarching objectives and long-term vision.
4. Adapt to Change with Agility: The landscape of technology, market demands, and customer preferences is ever-shifting. Data product managers must remain agile, constantly reassessing and adjusting their product strategies to stay ahead of market shifts, technical challenges, and competitive threats. Continuous learning, anticipating risks, and drawing insights from user behavior are essential to keep the product relevant and competitive throughout its lifecycle.
5. Measure Impact, Not Just Features: Defining and tracking key performance indicators (KPIs) is essential for understanding a product’s true success. Metrics like user engagement, customer satisfaction, and revenue growth provide a clear window into how well the data product is delivering value. Data product managers must focus on outcomes that reflect tangible business results, ensuring the product evolves to meet user expectations and business goals.

While a data product is an innovative way of enhancing business productivity, the true power of a data product lies in its ability to leverage data for improved business performance. As businesses increasingly rely on data to maintain competitive advantage, defining the scope and adopting the principles or best practices of data product management will be essential to unlocking the full potential of data. The key principles of data product management discussed here can help data product managers and businesses to not only unlock new revenue streams, but also help reduce costs, mitigate risks, and stay competitive in today’s VUCA (volatility, uncertainty, complexity, and ambiguity) business environment.