Data products

What is a data product?

Data products are data assets that are reusable, self-contained, user-centric solutions that bundle some data of interest with the tools necessary for its target consumers to make it readily usable. These data product bundles can be internal tools used by data analysts or customer-facing applications, all of which leverage data to deliver insights, improve user experiences, or automate tasks. And whether they are internal or consumer data products, they should empower their users to make data-driven decisions. 

Unlike traditional data assets, data products go beyond simply delivering raw data access. A data product takes curated datasets, integrates them with relevant tools and functionalities, and presents them in a user-friendly manner. The result: the data product user is able to solve a specific business problem or address a user need. This packaged approach—combining data with the means to use, analyze and present it—makes a data product reusable and even sellable. 

Here are some key characteristics of data products:

• Focus on user needs: Data products are designed with the specific needs of their target users in mind. They prioritize user experience (UX) by presenting data in a clear, concise, and actionable format.
• Data integration: Data products often combine data from multiple sources to provide a holistic view. The combination process can involve integrating data from internal databases, enterprise applications—customer relationship management, marketing automation, or any other corporate domain system—and external data sources like consumer demographics, social media feeds, or weather.
• Actionable insights: Data products should do more than present raw data. They should help users understand the data by providing features like data visualization tools, machine learning algorithms for generating insights, and automated reports highlighting key trends.
• Measurable impact: Data products should deliver a clear and quantifiable benefit to the business, improving key business metrics like revenue, customer satisfaction (CSAT), or operational costs.

Is a data product  the same as “data as a product”?

Yes and no. There can be some confusion between the terms “data as a product” and “data product.” While very much related, here’s how the two differ:

• Data as a product is a broader concept that refers to treating data as a valuable asset and applying product management principles to its governance, access, and usage. It involves ensuring data quality, security, and discoverability so that it can be effectively leveraged across the organization.
• Data product refers to a specific implementation of the “data as a product” concept. It’s a self-contained solution that uses data (and relevant tools) to address a particular need.

Essentially, “data as a product” is the philosophy, while a data product is the tangible outcome.

How are data products different from traditional software products?

While both traditional software products and data products aim to solve user problems, they approach it differently:

• Traditional software products: These focus on delivering a specific set of features and functionalities. The user experience often is concerned only in an much as it allows users to complete certain tasks or achieve particular outcomes. (In other words, how easily (and how well) the user can actually use the software is not the largest consideration. 
• Data products: These leverage data to empower users in specific ways. They may offer fewer built-in capabilities (numerically speaking) than a massive CRM, billing or other large enterprise system, but the priorities center presenting data in a way that users can analyze and act upon for a particular purpose.

Here’s a rather flavorful analogy. Look at a traditional software product as a pre-made meal. It provides all the necessary ingredients (features) and instructions (user interface) for users to complete a task (eating). A data product, on the other hand, would be like a well-stocked pantry. It provides users with various ingredients (data) and tools (analytics) to cook a meal (insights) tailored to their specific taste at the moment (needs).

What is data-driven product development, and how does it differ from traditional product development?

Data-driven product development (DDPD) is a development methodology that relies heavily on data to inform product decisions throughout the entire lifecycle. Here’s how it differs from traditional product development:

• Traditional product development: This approach often relies on intuition, market trends, and customer feedback to guide product decisions. While valuable, these methods can be subjective and prone to bias. Product managers may prioritize features based on their own assumptions about what users need, or they may be swayed by the loudest voices in the customer feedback pool.
• Data-driven product development: This approach leverages quantitative data from various sources to inform product decisions. This data can include user behavior analytics, A/B testing results, customer surveys, and market research reports. By analyzing data, product teams can gain deeper insights into user needs, identify pain points, and validate (or invalidate) product hypotheses.

DDPD is an iterative process. Teams continuously gather data, analyze it, and use those insights to refine their product roadmap and prioritize features. This results in a more user-centric and data-backed approach to product development.

Here are the benefits of data-driven product development:

• Improved product-market fit: By understanding user behavior and needs through data, products are more likely to resonate with the target market and achieve greater digital adoption.
• Reduced development risk: Data-driven validation of product ideas helps reduce the chance of investing time and resources into features users don’t actually want or need.
• Increased efficiency: By prioritizing features based on data-driven insights, teams can allocate resources more effectively and focus on development efforts that will deliver the most value.
• Faster product iteration: Data allows for continuous measurement and improvement. Teams can identify areas for improvement quickly and make data-driven adjustments to the product, leading to shorter product iteration cycles.
• Improved decision-making: Data removes subjectivity from the product development process. By basing decisions on real user data, teams can develop products that better meet user needs, ultimately leading to product success.

The core components of a data product roadmap

A data product roadmap is a strategic plan that outlines the development path of a data product. It leverages data to prioritize features, define timelines, and ensure the product aligns with business objectives and user needs.

Here are six essential components of a data product roadmap:

1. Product vision and goals: A clear and concise statement outlining the long-term vision for the data product and the specific goals it aims to achieve. This vision should be ambitious yet achievable and connect to the overall business strategy.
2. User personas: Defining target user personas is crucial for understanding who will use the data product and their needs. User personas should be based on real user research, including interviews, surveys, and user behavior analytics.
3. Market analysis: Understanding current market trends, consumer preferences, and the overall competitive landscape helps position the data product for success. Gaining such insight involves analyzing existing solutions, identifying potential gaps in the market, and staying abreast of emerging technologies and industry trends.
4. Feature prioritization: Data from various sources is used to prioritize features for development. A common framework for feature prioritization is the Value vs. Effort framework. Ideally, product teams should prioritize features that offer high value to users with minimal development effort (“quick wins”).Here are some examples of data points used for prioritization:
   • User research: User interviews, surveys, and usability testing can reveal user pain points and desired functionalities.
   • User behavior analytics: Data from website or application analytics can show details of user interaction with the current product and identify areas for improvement.
   • A/B testing: Testing different variations of features can reveal which ones resonate most with users.
   • Market research: Understanding industry trends and competitor offerings helps identify features that could give the data product a competitive edge
5. Timeline and milestones: The roadmap should establish a realistic timeline for developing and launching features. By establishing smaller milestones throughout the roadmap, teams can precisely monitor progress and better guarantee the project’s timely completion.
6. Metrics and success criteria: Data product roadmaps should define measurable success criteria to determine if the product is achieving its goals. These metrics can include:
   • User adoption rate: The number of users actively using the data product.
   • Feature engagement rate: The portion of users interacting with specific features within the data product. 
   • Customer satisfaction score (CSAT): A metric that gauges user satisfaction with the data product. 
   • Business KPIs: Key performance indicators aligned with the product’s goals, such as increased revenue, improved customer churn rate, or higher conversion rates.

By tracking these metrics over time, product teams can assess the effectiveness of the data product and make adjustments to the roadmap as needed.

Benefits of using a data product roadmap

Data product roadmaps offer several key advantages:

• Improved product-market fit: By basing feature development decisions on data, data product roadmaps help ensure the product addresses current user needs and solves relevant problems. This leads to a higher chance of product adoption and user satisfaction.
• Data-driven decision-making: Data roadmaps promote a culture of data-driven decision-making within product development teams. By basing decisions on quantifiable data, teams can avoid subjectivity and bias, leading to more effective product development strategies.
• Resource optimization: Data helps product managers prioritize features based on the potential impact of those features. This allows teams to allocate resources efficiently and focus on features that deliver the highest value to users and the business.
• Alignment and transparency: Data product roadmaps foster communication and alignment across teams. By providing a clear vision and roadmap, stakeholders understand product development priorities and can offer valuable feedback.
• Improved agility: Data roadmaps are flexible documents that product managers can adjust as new data emerges. This flexibility empowers teams to be agile in responding to evolving user needs and dynamic market conditions. Change management processes can be implemented to ensure a smooth transition when adapting the roadmap.

What kind of data should I consider when building a data product roadmap?

Building a data product roadmap requires a comprehensive view of the user journey and the business landscape. Here’s a breakdown of the different types of data to consider:

User research data

• User interviews and surveys: These qualitative research methods provide insights into user needs, pain points, and desired functionalities.
• User behavior analytics: Data collected through website or application analytics tools can reveal user behavior patterns, identify areas of friction, and highlight underutilized features.
• Usability testing and visual data: Observing users interact with the data product via session replay and playback (part of the Pendo digital adoption platform) can pinpoint specific usability issues and areas for improvement.

Quantitative data

• Website and application analytics: Web analytics data provides insights into user acquisition, engagement, and conversion rates.
• Customer Relationship Management (CRM) data: CRM systems contain customer demographics, purchase history, support interactions, and other customer data. This data can help identify user segments and tailor the data product accordingly.
• Marketing automation data: Data from marketing automation platforms can reveal user behavior within marketing campaigns and identify potential leads interested in the data product’s value proposition.

Market research data

• Industry reports and analyst insights: These resources cover how digital transformation is shaping specific industries and provide valuable information like competitor analysis, market trends, and emerging technologies that could impact the data product.
• Customer benchmarking data: Benchmarking against industry standards or competitor performance metrics can help identify areas for improvement and set realistic goals for the data product.

By combining qualitative, quantitative, and visual data from various sources, product teams can build a data product roadmap grounded in user needs, aligned with business objectives, and responsive to market dynamics. A well-functioning data ecosystem ensures that the data used to inform the roadmap is accurate, reliable, and accessible to those who need it.

How can I incorporate user feedback into my data product roadmap?

User feedback is a crucial component of any data product roadmap. Here are some ways to incorporate it:

• User interviews and surveys: Conduct regular user interviews and surveys to gather feedback on user experience, feature suggestions, and pain points.
• In-app feedback mechanisms: Implement features like surveys, polls, and chatbots within the data product itself to allow users to provide real-time feedback.
• Session recording and playback: Utilize session recording and playback tools like those offered by Pendo to observe how users interact with the data product. By visually identifying areas of confusion or friction in the user journey, you can gain valuable insights to inform your data product roadmap and prioritize improvements.
• User communities and forums: Foster online communities or forums where users can discuss the data product, share ideas, and report issues.
• Beta testing programs: Run beta testing programs to get early user feedback on new features before a wider release.

By actively soliciting and incorporating user feedback, product teams can ensure the data product roadmap remains user-centric and addresses evolving user needs.

How does Pendo help me with user research for data product development?

Pendo provides a digital product experience platform (DPXP) with a robust suite of tools for user research and data collection, making it an ideal partner for data product development teams. Here’s how Pendo helps:

• User session recordings: Pendo captures user sessions, allowing product teams to observe how users interact with the data product and identify areas of friction or confusion.
• User behavior analytics: Pendo provides in-depth analytics on user behavior within the data product. This data can reveal which features are most used, how users navigate the interface, and where they drop off in the user journey.
• Heatmaps and clickstream data: Heatmaps visualize user clicks and interactions on the data product interface, providing insights into areas of user focus and potential pain points. Clickstream data reveals the sequence of actions users take within the product, helping to identify user flows and optimize the user experience.
• Feedback tools: Pendo integrates with various feedback mechanisms like surveys, polls, and in-app chat, allowing users to provide real-time feedback directly within the data product.

By leveraging Pendo’s user research capabilities, data product teams can gather rich qualitative, quantitative, and visual data to inform their roadmap decisions and ensure the data product delivers a user-centric experience.

How can I leverage Pendo features to collect data for my data product roadmap?

Pendo offers a variety of features that allow you to collect valuable data for your data product roadmap:

• User segmentation: Pendo allows you to segment users on criteria such as demographics, behavior patterns, feature usage, and more. This segmentation helps tailor the data product experience and roadmap to specific user needs.
• Funnel analysis: Pendo’s funnel analysis tools help track user progress through critical journeys within the data product, such as onboarding or data analysis workflows. By identifying drop-off points within these funnels, you can prioritize features that address these bottlenecks and improve user completion rates.
• A/B testing: Pendo facilitates A/B testing of different user interface variations or feature functionalities. These tests allow you to validate data-driven hypotheses and determine which versions resonate most with users, informing future roadmap decisions.
• Retention analysis: Pendo’s tools help track how many users return to your data product over time. You can use this data to identify features that drive user engagement and prioritize initiatives that improve user retention.

Using these Pendo features, data product teams can continuously gather actionable user data to refine their roadmap, optimize the user experience, and ensure the data product delivers long-term value.

Why is Pendo the ideal solution?

Pendo empowers data product development teams in several ways:

• Data-driven decision-making: Pendo provides the data and insights to make informed decisions about feature prioritization, user experience optimization, and overall product strategy.
• User-centric development: Pendo’s user research tools ensure the data product roadmap is grounded in user needs and pain points, leading to a more user-centric and successful product.
• Improved product-market fit: By understanding user behavior and market trends through Pendo’s data collection capabilities, data product teams can develop products that address real market needs and achieve a solid product-market fit.
• Increased agility: Pendo’s user feedback mechanisms and A/B testing tools enable rapid iteration and adaptation so your product teams can respond quickly to changing user needs and market dynamics.
• Enhanced collaboration: Pendo fosters collaboration between product managers, designers, and engineers by providing a central platform for user data and insights. This collaboration leads to a more streamlined and efficient product development process.

In conclusion, Pendo is a valuable partner for data product development teams. By leveraging Pendo’s comprehensive user research and data collection capabilities, teams can build user-centric data products, solve real business problems, and achieve long-term success in the market.

Where can I learn more about developing products with Pendo?

For those looking to dig deeper, explore Pendo Data Sync or learn how you can use Data Sync.