Building Data Products: 6 Things You Should Know

What Is a Data Product?

A data product is any tool or application that leverages data to provide insights, enable decision-making, and drive automated actions. In simple terms, it packages raw data into a format that can generate value for users. Whether it's a dashboard that visualizes KPIs, a machine learning model predicting customer churn, or an API delivering real-time data to an external system, the essence of a data product is how it translates data into useful information.

Unlike traditional reporting or analytics, data products are built with scalability, reusability, and continuous delivery in mind. They are often designed as reusable components that can solve a variety of business problems and improve operational efficiency.

Why Data as a Product?

The shift towards treating data as a product stems from the need to make data more valuable and accessible to end-users. In the past, data was often seen as a by-product of business processes, stored in silos, and difficult to access or use effectively. However, the concept of data as a product changes this approach by treating data as a first-class citizen in an organization's strategy.

Here are some key reasons why companies are moving toward this model:

• Enhanced decision-making: When treated as a product, data is tailored to deliver the right information to the right users at the right time, making decision-making faster and more accurate — and removing the need for switching to a separate reporting application
• Business value: The ability to deliver actionable insights directly within the decision-making workflow, empowering companies to drive tangible business outcomes. By integrating data where decisions are made, organizations can enhance performance, foster innovation, and boost customer satisfaction without the need for separate reporting tools.
• Scalability and reusability: By treating data as a product, teams can build scalable, reusable solutions that continuously deliver value over time, rather than bespoke reports or one-off analyses.

Key Characteristics of Data Products

When building data products, certain key characteristics can greatly enhance their value and adaptability. Incorporating the traits below can help distinguish them from other types of data deliverables, ensuring they offer long-term benefits.

• Interoperability: A data product must integrate seamlessly with other systems, tools, and platforms. This interoperability ensures that data can be consumed in a variety of contexts without being tied to a specific application or vendor.
• Composability: The components of a data product (e.g., metrics, visualizations, data transformations) should be modular and reusable across multiple solutions. This ensures efficiency, reduces duplication of efforts, and enhances system stability.
• Discoverability: Users need to easily find and understand what data is available and how to use it. This is crucial in organizations where different teams require access to diverse data sets.
• Scalability: A good data product scales with the growth of data and the increasing complexity of business needs. It should be able to serve a small team today and grow to support the entire enterprise tomorrow.
• User-centric design: Any data-driven deliverable should be built with the end-user in mind, ensuring the user interface and experience meet the needs of different audiences, from executives to frontline operators.

Examples of Data products

Data products come in various forms, depending on their use case and the value they deliver. Here are a few examples of data products in real-world scenarios:

• Customer segmentation tool: This data product could automatically categorize customers based on their behavior and demographics, providing sales and marketing teams with actionable insights on how to tailor their strategies.
• Predictive maintenance system: Used in industries like manufacturing or transportation, this data product leverages sensor data to predict when machinery or equipment might fail, enabling preemptive repairs and reducing downtime.
• Personalized recommendation engine: Commonly used by e-commerce or streaming platforms, this product provides personalized content or product recommendations based on a user’s historical behavior and preferences.
• Executive dashboard: A high-level visual data product designed for C-level executives, providing insights into key business performance metrics such as revenue, customer satisfaction, and operational efficiency.

Building Data Products: A Step-by-Step Approach

Creating a data product is both an art and a science, requiring a thoughtful, structured approach. Here’s a step-by-step guide on how to build one that is successfully adopted:

• Identify the problem: Start by understanding the specific problem you are trying to solve. Talk to stakeholders to gather requirements and ensure the product aligns with business goals.
• Gather and prepare data: Data preparation is critical. Ensure that you have access to clean, structured data that can be used reliably in the end product. Tools like data pipelines and ETL processes can automate much of this work.
• Design the product: This includes deciding how the product will look (if it's a UI) and how it will function behind the scenes. Will you need machine learning models? What kind of visualizations will you use? Keep the end-user in mind.
• Develop the product: Using agile methods, build the product in iterations, making improvements based on user feedback. Use modern development principles such as code-driven design and CI/CD pipelines to ensure efficiency and scalability.
• Test and validate: Before launching, thoroughly test the product to ensure data accuracy, performance, and user satisfaction. This might involve pilot programs or A/B testing with a small group of users.
• Deploy and Maintain: Once tested, deploy the new asset, ensuring you have observability and monitoring in place to track its performance and user engagement over time.

Data Product Strategy

Having a strong data product strategy is essential for any organization looking to leverage data effectively. Here are a few strategic considerations:

• Align with business goals: Every data product should directly support a business goal, such as improving operational efficiency, increasing revenue, or enhancing customer experience. Additionally, build strategies should align with broader data and software development approaches, such as Continuous Integration/Continuous Delivery (CI/CD), automation, and Analytics as Code, ensuring seamless integration into existing workflows.
• Promote cross-department collaboration: Data products often require inputs from different departments (e.g., marketing, sales, IT). Collaboration across teams is essential for success, and aligning development approaches can streamline this cross-functional work, improving agility.
• Foster a data-driven culture: Encourage a company-wide embrace of data-driven decision-making. This ensures that data products are valued, used, and maintained over time. Integrating analytics and automation within workflows further reinforces this culture, making it easier for teams to adopt and trust data-driven insights.
• Focus on reusability and modularity: Ensure that components of your data products can be reused across multiple use cases, driving efficiency and long-term sustainability. Aligning with practices like CI/CD and automation promotes reusability, as it allows for continuous updates and scaling across projects without starting from scratch.

Data Product Recap

Data products are transforming how organizations leverage data to drive business outcomes. By following a structured approach to building data products and focusing on key principles like interoperability, composability, and user-centric design, businesses can unlock the full potential of their data. Whether you’re building simple dashboards or complex machine learning models, treating data as a product ensures that it becomes a critical asset that grows in value over time.

Incorporating the right data product strategy helps businesses create scalable, reusable solutions that adapt to the changing needs of the market, making them more competitive and efficient in their operations.

Building Data Products With GoodData

GoodData is fully equipped to support data product builders with its robust analytics platform. Designed with the principles of data as a product in mind, GoodData provides the tools and infrastructure necessary to create, manage, and scale data products efficiently. With a focus on interoperability and user-centric design, GoodData empowers organizations to unlock insights and drive value from their data seamlessly. Embrace the future of data with GoodData, where your data products can thrive.

Don’t just take our word for it, sign up for a 30-day trial to try it for yourself or book a demo and get a tailored tour of the GoodData platform.