What Are Data Products?

What Are Data Products?

As businesses become more data-driven, many are realizing that having vast amounts of raw data is not enough. Organizations need to transform that data into valuable, actionable insights that can drive business decisions, improve processes, and unlock economic value. This is where the concept of data products comes into play. To transform data into actionable insights, companies are adopting the concept of data products—packaged data assets designed to solve specific business problems or provide valuable insights. Unlike raw datasets, which can be fragmented and hard to use, data products are organized, accessible, and ready for immediate application.

The Concept of Data Products

A data product is more than just a collection of data—it’s a tool that turns raw information into business value. These products are designed to deliver insights, improve processes, and generate value across an organization or to external customers. According to the business intelligence community, data products help bridge the gap between raw data and business decision-making by making data more understandable, usable, and impactful.

For example, a data product might take the form of a dynamic dashboard, an API that delivers customer data in real time, or a curated dataset that helps improve operational efficiency. By delivering data in a structured, self-service manner, data products empower users to draw insights quickly, without relying on complex BI acrobatics or coding skills.

Why Data Products Matter

Data products help businesses achieve several critical objectives:

• Accessibility: Data products make information available to both technical and non-technical users through interfaces such as dashboards, APIs, or easy-to-use tools. This democratizes access to data across the organization.
• Value Creation: By transforming raw data into a curated, actionable format, data products allow teams to make informed decisions, improving operational efficiency, customer experience, and overall business outcomes.
• Scalability: Well-designed data products are scalable, allowing businesses to integrate and utilize increasing amounts of data without additional complexity or overhead.

At Gigasheet, we've seen firsthand how data products empower business users to take control of their data and perform the final mile of analysis on their own. In traditional setups, the process of getting insights from data often required heavy involvement from technical teams—data scientists, analysts, or engineers—to clean, prepare, and present data in a usable format. This approach is not only time-consuming but can also create bottlenecks as technical teams balance multiple projects. However, with well-designed data products, the process is streamlined, allowing non-technical users to engage directly with the data.

What’s been most transformative for our users is the self-service aspect. Data products make it possible for business users—whether they’re in sales, marketing, finance, or operations—to explore the data themselves, ask their own questions, and generate insights without needing to write a single line of code. This shift not only speeds up decision-making but also democratizes data access, putting the power to analyze and act on data into the hands of those who understand the business context best. The result? Faster insights, more informed decisions, and a data-driven culture that scales across the organization.

Data Products vs. Raw Data

While raw data can be a valuable asset, it is often difficult to access, analyze, and derive insights from. Data products, on the other hand, are structured to be immediately useful. Some key differences between raw data and data products include:

• Purpose: Data products are designed with a specific business use case in mind, while raw data is often a byproduct of everyday operations.
• Ease of Use: Data products come with built-in tools or interfaces that make them easier to use, even for non-technical users.
• Refinement: Data products undergo processing and curation to ensure accuracy, relevance, and consistency, while raw data may require significant cleaning and analysis.

Examples of Data Products

• Dynamic Dashboards: Visual interfaces that update in real-time, allowing businesses to track KPIs, monitor trends, and make data-driven decisions.
• APIs: Provide external or internal users with programmatic access to data, enabling automated workflows, integrations, and more.
• Data Catalogs: These products centralize and organize data from different sources, making it easy for users to discover and access the information they need.

Data products are typically tailored to solve specific business challenges by transforming raw data into structured, actionable insights. Here are four examples we see at Gigasheet:

Retail Sales and Segment Cohort Analysis:
Retailers, particularly those dealing in high volumes frequently use data products to track customer behavior across various categories and segments. This includes identifying purchasing patterns, analyzing cohort behaviors, and conducting "new-to-segment" analysis, where businesses study customers purchasing from a category for the first time. These insights, often presented via dashboards, help companies optimize product offerings, refine marketing strategies, and increase customer retention.

Market Segmentation Data Feeds:
Companies involved in advertising, market research, or audience analytics create data products that deliver segmented audience data. These products allow users to filter data by demographics, behaviors, or locations to identify trends and opportunities. With tools like Gigasheet, organizations can offer large datasets that clients can explore and extract in a self-service manner, making it easier to customize and act on the information.

Financial Transaction Reporting:
Businesses that manage high volumes of financial transactions, such as e-commerce sales or vendor payments, often rely on data products that provide real-time reporting. These products allow finance teams to monitor key metrics, identify discrepancies, and ensure compliance. By organizing financial data into easily accessible formats, companies can streamline auditing and reporting processes.

B2B Data Products:
B2B data providers often package large datasets, such as firmographic or technographic data, into data products for B2B sales activities. These data products allow clients to explore, filter, and download the data they need for their own business needs, such as lead generation or market analysis. Platforms like Gigasheet are well-suited for delivering these massive datasets in a scalable, self-service format, giving clients control over how they interact with and extract value from the data.

These examples highlight how data products transform raw data into valuable tools that drive insights, decision-making, and business outcomes across industries.

The Data Product Lifecycle

Creating and managing data products requires a holistic approach that covers the entire data lifecycle. This process typically includes these 5 steps.

Data Collection:
The first step in creating a data product is gathering relevant raw data from various sources such as internal systems, third-party vendors, or external APIs. This step ensures that all necessary data points are collected, setting the foundation for building a valuable and actionable data product.

Data Curation:
After collection, the raw data must be cleaned, organized, and structured. This involves filtering out irrelevant data, correcting inconsistencies, and formatting the data into a usable form. Curation is essential for ensuring the quality and reliability of the final data product.

Productization:
The curated data is then transformed into a data product, such as an interactive dashboard, API, or report. This step includes adding features that enable users to explore and interact with the data, ensuring it addresses specific business use cases or challenges.

Distribution:
Once the data product is built, it must be made accessible to its users. Distribution can take place via an API, an online platform, or through direct data feeds. This ensures the data product reaches the intended audience in a way that’s convenient and easy to use.

Continuous Improvement:
After the data product is live, it’s important to monitor its performance and gather feedback. This step involves iterating on the product based on user insights, updating it with new data or features, and ensuring it remains relevant and useful as business needs evolve.

Best Practices for Building Data Products

Building successful data products requires both technical precision and a focus on delivering business value. One of the most important principles is to design with the end user in mind. Every data product should be created to solve a specific business problem, making it essential to understand who will be using the product and what decisions they need to make. By focusing on user needs and business outcomes, data teams can ensure that their products are both relevant and actionable. Additionally, automation is key to streamlining data collection, processing, and delivery. Automating common tasks ensures data products are consistently up-to-date and reliable, reducing manual work and improving efficiency.

Another critical best practice is to ensure scalability from the start. As data volumes grow, the data product should be able to handle the increased load without sacrificing performance or usability. Monitoring and measuring the impact of data products is also essential—tracking metrics and gathering user feedback can help identify areas for improvement. Finally, documentation is often overlooked but crucial; clear and accessible documentation allows users to fully understand how to use the data product, while also ensuring the data’s origins and transformations are transparent. Together, these practices contribute to the creation of effective and scalable data products that drive real business value.

Conclusion: The Future of Data Products

Data products represent a powerful shift in how businesses approach and use data. By treating data as a living product rather than a static asset, organizations can unlock its full potential, delivering greater insights, more efficient processes, and ultimately, better business outcomes. As technology evolves, data products will become an even more essential part of the modern business toolkit, helping organizations stay agile and competitive in an increasingly data-driven world.

For businesses looking to unlock the full value of their data, adopting a data product mindset is a critical first step.