10 Key Ways Data Products Can Outperform Traditional Reporting in 2026

We’ve been talking a lot about how innovative companies are realizing the need to enhance their solutions with more customer-facing data products. The examples range from predictive analytical models that help healthcare professional identify risks (the kind of things we do at Juice) to everyday consumer products as simple as the New York Times’ Wordle Puzzle. Wordle now comes with a WordleBot that analyzes your guesses — a feature that has made the game even more sticky for me:

DJ Patil (former U.S. Chief Data Scientist) defines data products as "a product that facilitates an end goal through the use of data.” I’ve described data products as turning analytics inside-out to deliver value to your customers. But the question we most often get is: How are data products different from the customer reporting we already provide? 

Here are ten important differences between customer reporting and data products: 

1. Instead of summarizing data, solve a problem.

Most traditional reporting simply regurgitates data in a semi-aggregated format, leaving the user to decipher its meaning. A true data product, however, begins by identifying a customer’s specific pain points and investigating how data can deliver the insight required to solve them. It is not about displaying what is available; it is about deploying data to drive better decisions and tangible outcomes.

Resource: Unlock the Full Value of Data: Manage It Like a Product (McKinsey)

2. Instead of starting from the data, start from the customer.

Report writers often survey the data available to them and ask, "How can we deliver all this information?" This approach inevitably leads to overwhelming self-service analytics tools or AI bots that dump responsibility onto the user. This is a strategy that rarely succeeds. Effective data products must begin with a fundamental question: "How can we make our customers smarter and more effective in their roles?" The design process must flow from the user's needs backward to the data, not the other way around.

Resources: Data Product Management Best Practices & Principles (Nexla)

3. Instead of stopping at showing the data, guide users to specific actions.

Customer reporting is often satisfied with merely making data accessible. Data products, however, must achieve more — they must compel users to take action. The design process should begin at the end: What specific actions do you want the user to take? How can the interface provide the precise information needed to facilitate that decision? The goal is not just to inform, but to move the user toward a beneficial outcome.

Resource: The Analytics Pyramid

4. Instead of focusing on metric values, deliver context for decisions.

Key metrics are only as valuable as the context surrounding them. Data products must wrap raw metrics in layers of context such as goals, benchmarks, historical comparisons, and trends. Without this, a number is just a number. With context, users immediately understand the "health" of the metric and know exactly how to react to the figures they are seeing.

Resource: How to Add More Meaning to Your KPIs

5. Instead of passive objectivity, bake-in best practices, predictive models, and/or recommendations.

The philosophy of "letting the data speak for itself" is akin to a chef serving raw ingredients and expecting the diner to cook the meal. Data products should embody your expertise. While the customer knows their business pain, you understand the data's potential. A successful data product is "opinionated", it integrates predictive models, best practices, and automated recommendations to guide the user toward the best possible course of action.

Resource: The Advantages of Data-Driven Decision-Making

6. Instead of trying to show more data, reduced to only the data needed.

When presenting information, more is seldom better. Customer reporting tends to expand indefinitely, sprawling into dozens of dashboards and reports. Data products, however, should strive for minimalism. By reducing the noise and focusing strictly on the essential data required for the task at hand, you reduce the cognitive load on the user, making the insights clearer and the product more impactful.

Resource: Top 30 Best Dashboard UI/UX Design Principles You Must Know in 2025

7. Instead of putting the burden on users to figure it out, strive to reduce burden.

Customer reporting often abdicates responsibility, effectively telling the customer, "Here is the data; you figure out what is important." Data products recognize a critical truth: few people inherently enjoy wrangling data. Most users simply want to perform their jobs better. The data product must facilitate this goal by removing friction, streamlining the path to insight, and minimizing the effort required to extract value.

Resource: How to Identify & Fix User Friction

8. Instead of being designed for analysts, data products are designed for decision makers.

Many customer reporting solutions operate under the false assumption that the end-user desires to dig in and analyze the data themselves. Data products serve a different audience: the front-line decision-makers. These individuals are preoccupied with their core responsibilities and often have little interest in learning complex new tools. The product must deliver immediate value without requiring them to become amateur analysts.

Resource: Frontline AI: Applications Across Industries

9. Instead of “show me the data”, strive to make the data invisible.

The most advanced data products of the future will effectively make the data invisible. Consider the evolution of Google Search: it anticipates your need and places the answer at the very top of the results, sparing you the effort of clicking through links. Data products should aspire to this standard—hiding the complex data machinery to surface the answer directly, minimizing the interface between the user and the solution.

Resource: The Zero-Click Cataclysm: How AI Is Redefining Search

10. Instead of a cost-center for your business, become a profit center and differentiator.

Customer reporting is frequently viewed as a "necessary evil"—a task that organizations feel compelled to perform but do not relish. In sharp contrast, companies that treat customer data as a strategic asset recognize its potential to generate value. By building data products, organizations can create new revenue streams, differentiate their offerings, and transform their data operations from a cost center into a profit engine.

Resource: 3 ways to build a culture of data monetization

That’s where Juicebox comes in — it is the quickest, best path to turn your data into differentiated, revenue-generating data products.