What’s a data product? And how do data products revolutionize product development?

Data is the backbone of successful product development. It enables you to gather insights about your users’ behavior, helps you better understand their needs and pain points, and equips you with the know-how to build innovative, relevant products in today’s overcrowded market. 

But data is also key to powering your products. From algorithm-driven recommendation systems to personalized content delivery, the data within your tools and applications is critical to helping you offer unmatched value to your end user.

This article will dive deep into the world of data products, paired with insights from Eric Weber, former Senior Director of Data Science at StichFix.

We’ll explore: 

• What is a data product? And who is a data product owner? 

• What are the benefits of using a data product in your product development process?

• What is the role of AI in product innovation?

What is a data product? And who is a data product owner?

A data product is a fusion of data science and product development that uses data insights to create and deliver tangible value to end users. 

According to Eric, “A data product is where data adds key differentiator capability from which the target customer benefits.” 

To understand if a product within your organization is a data product or not, Eric suggests you start with 2 critical questions: what makes this product a differentiator, and for whom? 

To address these questions, you can start by asking:

• Is data the key differentiator in this product? 

• Who benefits from this data product, both internally and externally? 

• How does it create tangible value for your end users?

It also helps to compare your potential data product to established examples of data products. 

So what does a data product actually look like? 

What are some examples of data products?

What do Netflix, Uber, Fitbit, LinkedIn, Siri, Google Maps, Microsoft Power BI, and ChatGPT have in common?

You guessed it: they’re all data products (or enabled by data products). As you can see, data products are diverse and range significantly from one another. 

We can group data products according to their functionality. Here are some of the key categories:

• Recommendation engines use data about users and users’ behavior to provide insights and make recommendations to those users 

• Real-time dashboards help users explore and make sense of large datasets with interactive visualizations like charts, word clouds, heatmaps, and maps. (Some examples from Contentsquare’s experience intelligence platform include Journey Analysis and Heatmaps.) 

• Predictive analytics tools take historical data (for example, regarding a customer’s behavior) and analyze it to predict future events, such as when said customer is likely to churn   

• Data APIs facilitate seamless data exchange between different applications and systems—enabling data products to be used across various platforms and apps

• Anomaly detectors detect and flag abnormal patterns in data, helping find technical failures, security threats, and identity fraud

• Conversational intelligence systems like Siri, Alexa, and ChatGPT use data to respond to our questions and statements in human-like language

“We used ChatGPT to write our product descriptions,” says Eric. “We have tens of thousands of differentiated products, and it simply wasn’t scalable for us to write our own product descriptions.”

Other data product use cases include

• A homegrown experimentation platform that enables you to do experimentation at a high level of complexity 

• A Client Time Series Model, which drives all of the recommendations made within the product 

• Numerous executive dashboards that help with understanding the functioning and health of a business 

And this list is just scratching the surface. “These are just a few data products, amongst many,” says Eric. 

How are data products managed?

This is a great question—because currently many aren’t.

The concept of data products is relatively new, so many data products are currently unowned within an organization. This presents an opportunity for a new kind of role: a data product manager.

A data product manager solely focuses on products that are centered around data-driven capabilities and insights. Their main responsibility is to identify and develop data-driven products that add measurable value and serve as key differentiators for the target customers, whether internal or external.

The benefits of data products for your product development process 

Data products offer many organizational benefits. Here are a few of them.

1. Enables personalization at scale: by harnessing customer data and behavioral insights, your company can create tailored offerings, recommendations, and campaigns that resonate with different users. This might be suggesting personalized product recommendations, curating content based on user interests, or delivering targeted marketing messages.

2. Streamlines stages of your product development process: from ideation to launch, data products enable product managers (PMs) and teams to make informed decisions based on real-time market trends, user feedback, and performance metrics. This iterative and data-driven approach accelerates the product development process and increases your chances of delivering successful products that align with customer needs.

3. Encourages iterative prototyping: by leveraging data-driven insights, data product teams can rapidly prototype and test new ideas, features, and concepts. This iterative approach allows you to gather real-time user feedback, identify improvement areas, and fine-tune your products for optimal performance. 

4. Drives data-backed decision-making: data products provide comprehensive data analytics, market trends, customer behavior patterns, and other essential metrics that inform strategic planning and resource allocation. Leveraging data in your decision-making processes helps you minimize uncertainties and increase the likelihood of successful outcomes.

The role of AI in product innovation

Today’s companies are exploring how AI can help them innovate and develop products at a faster rate, especially around large language model (LLM) tools like ChatGPT. 

Eric believes AI will continue to transform the way product teams think and work.

“AI creates this ability for more people in a company to act as product builders,” says Eric. “I think one of the biggest opportunities is to say, okay, what is my idea, and can I take this idea to something functional?"

With the help of AI, the line between the traditional role of specialized engineers and data scientists has become blurry with general product managers. 

Now, elements like the framework, code base, front-end design, and the rough draft phase of product development are more accessible. This enables a broader range of employees to participate more actively in the ideation and prototyping stages.

AI also empowers product managers to make data-backed decisions more granularly than before. Rather than relying solely on high-level macro insights, PMs can zoom in and evaluate the smallest pieces of the user experience. 

That’s what platforms like Contentsquare enable, providing a layer of AI-powered behavior analytics to help you gain a 360-degree view of your users’ behavior. 

Contentsquare’s AI, Sense, makes it easy for your teams to get insights into how people use your product across multiple sessions—and how they feel about it. 

For example, you get an in-depth analysis of any aspect of the user experience simply by asking Chat with Sense questions like ‘How many mobile users used X feature this week?’

The granular understanding of customer behavior and engagement Contentsquare gives product managers means they can easily and rapidly identify opportunities for improvement and align product features with customer needs. 

"It can be very tempting when you're building a model just to toss everything in,” says Eric. “Starting from the ground up and being able to justify why you're adding each aspect of data into the product becomes critical."

The future of data products

Looking ahead, product managers should assess how AI can improve their current product experience, focusing on small pieces of the customer journey to determine how AI can alter and improve these aspects. 

AI may also challenge traditional product definitions, which Eric says may lead to integrating AI capabilities into other product offerings or even creating entirely new product categories. 

"What does AI eliminate the need for? What does it create the need for?” he says. “I think pushing on that boundary is super fascinating.”

As the landscape evolves, data product managers play a crucial role in identifying and developing data-driven products that add value to target customers. They create differentiation through data-driven insights and ensure products deliver measurable value to internal and external users.

For organizations, data products help to personalize offerings at scale, streamline the product development process, encourage iterative prototyping, and drive data-backed decision-making. 

And with AI as a driving force, product teams will continue to break down barriers between traditional roles, allowing more teams to contribute to data product ideation and prototyping.