Data is the Product

Product managers often rely on data to make decisions for their products. This qualitative or quantitative data is often a source of insight and inspiration to solve user problems. So what does it mean when we say, "Data is the Product"?

When data is the product, it means that data itself—or insights derived from it—is the primary value delivered to users. Unlike traditional products, which are physical or software-based, data products focus on collecting, processing, and delivering actionable information. It is about giving back the information generated by the user in such a way that it becomes useful for the individual or group of users on the platform or product. The simplest example is the reviews or feedback that users give for any services or products they use. This data can be used by product managers to understand how users like their product or service, but users also use the same data to make purchase decisions. Google Maps primarily tells directions from one point to another, but the data it collects about user locations is used to estimate traffic conditions. This is one of the coolest ways of using data as a product feature.

While data is useful, there are certain considerations that would help product managers create a successful product.

Focus on customer needs

You cannot forget customer pain points and needs. A data product should be created with the user in mind. It’s not just about providing information but presenting it in a way that users find easy to understand and act on. For example, Spotify creates personalized playlists by analyzing user data. Instead of showing raw data about songs played, it curates music recommendations, making it simple and enjoyable for users to discover new music. Zomato's Reorder feature is a great example of a data product. Users often want to reorder something they liked, and rather than making the user go through the hard work of finding, selecting, and ordering the same food again, Zomato makes it easier for customers to order again. The key point is that someone actually figured out the customer pain points before deciding on these features.

Maintain high quality of data

Another major consideration for data products is the quality of the data. High-quality data is like a strong foundation for a house. It must be accurate, timely, and reliable so users can trust it for their decisions. Imagine using a weather app that shows outdated or wrong forecasts—it would lose its usefulness. In contrast, Google Maps relies on accurate location data to show real-time traffic conditions, ensuring users trust its directions. Users want predictability, and to provide it, it is important to have high-quality data in place.

Work on continuously improving the product or feature

Like any other product, data products should improve over time. Feedback from users can reveal areas where the product isn’t meeting expectations. For instance, Netflix constantly updates its recommendation algorithms based on how users engage with its platform. If many users skip certain movie genres, Netflix adapts by refining its suggestions to better match preferences. Similarly, if Google Maps users consistently avoid the alternate routes suggested due to traffic, it is important to understand their behavior. Maybe it is because of security concerns with unknown routes, and Google might need to build trust by using data like, “50 users reached the same destination through this route.” Data products have many opportunities for continuous improvement.

Privacy and Compliance

Handling data ethically is essential. Users need to feel confident that their information is safe and used responsibly. Laws like GDPR and CCPA set strict guidelines for how companies handle data. For example, Apple emphasizes user privacy in its products, ensuring transparency about what data is collected and how it’s used. This builds trust and loyalty among its users. Any use of data that hurts user sentiments could lead to sudden drops in user engagement. Applications should provide the ability to control the usage of their data for decision-making. This builds trust and gives users a sense of control over their information.

Building Data as a Product

1. Identify User Needs:

   Start by understanding what users are looking for in the data. Are they trying to spot trends, predict outcomes, or dive into detailed metrics? For instance, fitness apps like Fitbit identify user goals, such as tracking steps or monitoring sleep, and tailor their data delivery accordingly.

2. Data Collection:

   Reliable systems are needed to collect data from various sources. Ensuring the data is accurate and free from bias is crucial. For example, e-commerce platforms gather data on user behavior—like browsing and purchase history—to improve product recommendations.

3. Data Processing:

   Raw data isn’t very helpful on its own. Advanced tools like AI and machine learning transform this data into meaningful insights. Think about how LinkedIn processes user activity to suggest jobs or connections, making the platform more useful for professionals.

4. Delivery Mechanism:

   The way data is delivered matters. Dashboards, APIs, reports, or visualizations should match what users find most convenient. For example, business intelligence tools like Tableau provide interactive dashboards, helping users explore and understand data visually. While this way of presenting information is good for professionals, a normal user would appreciate a more integrated user flow to make the information actionable.

5. Monetization:

   Data products can contribute to revenue growth as well as customer retention. These advanced features can be part of premium or pro accounts. Once the data becomes important for the customer, they attach both sentimental value and need value to the information, ultimately driving revenue.

Challenges and Considerations

1. Data Silos: Integrating disparate data sources to provide a cohesive product is a technical and organizational challenge.

2. Ethical Use: Balancing data monetization with user privacy requires robust governance.

3. Scalability: As the volume of data grows, maintaining performance and accuracy becomes complex.

Treating data as a product requires a shift in mindset. Organizations must recognize the value of their data assets and build systems to maximize their potential. By focusing on user needs, maintaining high data quality, and ensuring ethical use, companies can turn data into a powerful product that drives growth and innovation.