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We create powerful data products

What are data products?

Data products are tools that use data to achieve specific goals. Simply put, they help you access and realize the hidden potential of your data. Think of the weather app on your phone. It gathers data from various sources to help you plan your day.

Now, imagine if your organization could use its data — from employees, customers, partners, and more — to guide every decision. Could you identify the next best actions for salespeople? Could you turn customer feedback into a strategic roadmap? Could you predict how a change in one area will affect the rest of the organization?

The potential for data products is nearly limitless. They can transform your offerings, services, and employees' day-to-day work. Data products can dramatically change the organizations that use them. In this guide, we’ll explore the different kinds of data products, how they work, and how they can drive radical transformation.

Data products transform organizations

Data products help organizations achieve more ambitious goals faster. Specifically, they can:

  • Improve efficiency: Automate, streamline, and eliminate unnecessary work.

  • Unlock insights: Understand customer behavior, product performance, and more.

  • Make better decisions: Use dashboards and visualizations for faster, informed decisions.

  • Identify opportunities: Discover new customer and market opportunities.

  • Enhance customer and employee experience: Streamline manual work and improve visibility.

Data preparedness and readiness for AI

Good news: the data maturity needed to build and fuel data products is similar to what's required for AI applications. If your organization is considering its AI strategy, an assessment of your data maturity is a great first step.

Assess your data maturity

Types of data products

Data products come in many forms, each designed to help achieve specific goals using data. While there are countless unique data products, they generally fall into one of these four categories below.

It’s worth noting that the degree of automation in a data product doesn't determine its quality. The right balance between automation and user processing depends on the available data, the users, and the context in which it's used. Additionally, higher levels of automation require building more trust with users.

Raw data

Raw data products provide users with a granular level of detail that can be explored and manipulated for deeper understanding.

Derived data

Derived data products offer data with some processing, such as tagging data points with attributes. Both the user and the product share the interpretive work, often using algorithms.

Decision

Decision support products present processed information to facilitate faster, better decision-making. The product does most of the interpretive work, and the user takes action based on the insights.

Automated

Automated decision-making products handle both interpretation and action, automating tasks that humans would otherwise perform.

Data product interfaces

Where function meets trust

A data product interface is how a user interacts with the data. For an algorithm-powered data product like Google, the interaction might involve typing words into a search bar or speaking into a phone’s microphone. Google then displays the most relevant results according to its algorithm.

To build user trust in these results, Google provides certain cues: visible URLs so users can assess the source, timeliness tags showing when something was published (“9 months ago”), preview text from the page, and highlights of what Google's algorithm considers the most relevant results.

Data product interfaces

The best interface for a data product depends on the type of data, its users, and the product's intended purpose. Common types are:

APIs

APIs (Application Programming Interfaces) are ideal for technical data users or for products where users need to do more processing. However, like any interface, APIs should follow human-centered design principles.

Dashboards

These interfaces encompass a wide range of formats and can be used in various data products, from sales pipeline visualizations to simple device utilization graphs.

Web elements

These interfaces are suitable for the least technical users of data products and include interactions like typing or using voice commands.

The importance of user trust

User trust is critical when building data products. If end users don’t believe the outputs of your data product are reliable, they simply won’t use it. This lack of confidence can quickly destroy a product’s ROI.

One challenge with data products is that making them easier to use can sometimes unintentionally decrease user confidence.

We experienced this firsthand when developing a searchable medical database to speed up research for a Fortune 100 biopharmaceutical company. An early prototype failed because it didn’t include enough data validation and source citations to satisfy the researchers who would be using it. Based on feedback from user testing, we added sources and citations in the next prototype.

So how do you create user trust?

Keep your users involved throughout the development process of data products.

Start with user interviews to understand their current workflows and identify opportunities for data-driven products to enhance efficiency. Remember, effective data products exist at the intersection of what users want, what is technologically feasible (given the data you have and the team available to build and maintain the product), and what is viable from a business and financial perspective.

In practice, this might look like…

  • Conducting user interviews at the beginning of data product development.

  • Getting user feedback on low-fidelity mockups of data products.

  • Incorporating user feedback into progressively higher-fidelity mockups.

  • Iterating after launch based on user behavior and ongoing feedback.

Data products in action

Data visualizations engage patients in their recovery

We partnered with Theragen to develop a companion app for their wearable medtech device, designed to promote spinal healing after fusion surgery. Since the device supports long-term healing over several months, patients often didn't notice significant day-to-day improvements, leading to low adherence.

To address this, we developed an app that tracked and visualized three key data points:

  • Number of hours per day patients used the device

  • Daily movement

  • Pain levels throughout the day

By making progress visible and allowing patients to see the connections between device use, movement, and pain levels, the app significantly increases adherence to recovery protocols. It also provides healthcare providers with reliable data to guide recommendations and monitor recovery progress.

Data products in action

An automated, centralized data hub with multi-site reporting and data collation

We partnered with Northwestern University to develop NUcore, a system designed to manage their many research facilities (or "cores"). These facilities house complex equipment arrays, ranging from expensive machines that need to be reserved by the hour to single-use supplies that must be correctly stocked.

Northwestern needed a better system for users to reserve and pay for space and for administrators to keep the facilities running smoothly. This led to NUcore, which allows for reservations and payments in a simple online portal. The system also streamlines data reporting across all facilities, significantly reducing the labor required to retrieve and collate data for billing purposes. NUcore saves administrators hours of time each week and ensures more accurate accounts.

Data products in action

An internal search engine lets researchers identify promising compounds and genes

We partnered with a Fortune 100 biopharma company to accelerate their drug discovery process, which typically takes about a decade. Despite owning decades’ worth of data from past research, the company lacked a system to make this data accessible to researchers.

We worked closely with in-house scientists to understand how they assessed existing data, then built an engine that let them search for information on genes or compounds they want to study. Building trust among these researchers was crucial to our success, so we invested significant time and energy in understanding how to tag, cite, and source the data provided by the engine to ensure users would trust its outputs.

Now, instead of starting every inquiry from scratch, researchers can save years by beginning lab research with gene-compound combinations already shown to have potential.

Data products in action

A mobile app alerts manufacturers when equipment or facility temperatures go out of range

We had been working with environmental monitoring solutions provider Dickson for several years to modernize their equipment fleet when it became clear their customers needed more accessible decision support from Dickson’s flagship software, DicksonOne.

To address this, we developed a mobile app that highlights critical alerts for users on the go. The DicksonOne app notifies users when facilities or equipment monitored by DicksonOne stop collecting data or go out of temperature or humidity ranges.

Whether Dickson’s customers are on the factory floor or at soccer practice with their kids, these alerts empower them to act immediately when conditions change, preventing spoilage and waste of valuable inventory.

Data products in action

A mobile app automatically queues uploads and downloads until WiFi is available

MotorCity Systems, an innovative software provider for trucking companies, faced challenges with driver communication, particularly in contacting dispatchers and managing workflows and paperwork. They wanted a driver-centric mobile application to enhance the driver experience.

We partnered with MotorCity Systems to develop the ROLLER app, which tracks deliveries, allows drivers to communicate seamlessly, and stores all paperwork. This app automatically detects motion and halts notifications when a truck is moving. It also detects WiFi availability and queues data until a connection is available, ensuring efficient data management.

With the ROLLER app, drivers receive timely updates, can efficiently manage data uploads and downloads, and maintain clear communication, enhancing overall operational efficiency for the fleet.

Build a data product for your organization

Your organization generates a lot of data. Without capturing, organizing, and using it, this data remains untapped potential. You're stuck relying on inefficient and inconsistent methods like hunches, imitating competitors, and following generic best practices. As you know, these methods aren’t always reliable.

Just as sunlight and wind can be harnessed as energy sources, your data can be transformed into a powerful resource. By building the right infrastructure, you can turn your data into fuel that powers nearly everything you do.

Transform your raw data to a powerful product

So how do you get from ambient data to a transformative data product? It starts with the Data Maturity Accelerator. This engagement assesses your organization's data over the course of three weeks, examining its current state, governance, and usage. We then create a customized roadmap to help you harness your data effectively.

While the roadmap will be unique to every organization, here are 9 common steps:

1

Understand data sources

Integrate your data with external sources to enhance its richness.

2

Assess data infrastructure

Review tools, databases, network, and security to identify necessary enhancements.

3

Clean and standardize data

Ensure high-quality data for reliable analysis.

4

Conduct user interviews

Identify pain points and opportunities by talking to potential end users.

5

Design and develop

Use product innovation and human-centered design to create a data product that meets user needs, is technically feasible, and viable from a business perspective.

6

Generate dummy data

If no data is available, create assertive dummy data for development and testing.

7

Document the product

Record the product’s functionality, architecture, and data sources.

8

Implement monitoring

Track performance and usage of the data product.

9

Develop initial version

Deliver a functional prototype and continuously iterate.

Unlock the value of your data

Schedule a Data Maturity Accelerator to identify opportunities and start transforming your data into actionable products.