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Understanding Their Value and Functionality
I am passionate about bringing data products to life. Bridging the gap between business and technology to develop data products that makes a different. My Hobbies are: Traveling, Biking and Cooking.
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In today’s data-driven world, the term "data products" is gaining traction as businesses increasingly leverage data to solve real-world problems. Just as crude oil must be refined to deliver its true value, data is an information asset that needs to be processed and analyzed to generate meaningful insights (Palmer, 2006). Whether for internal business use or external monetization, data products are the tools that transform raw data into value-adding solutions. Let’s explore what makes data products essential, how they are categorized, and how they create value.
At its core, a data product is the output of work by data scientists, analysts, and engineers designed delivering value to users through the power of information. Unlike raw data, data products deliver actionable insights or automate solutions to real-world problems. According to Loukides (2011), they can be classified as either:
Overt Data Products: These make data visible to the user, often in the form of reports, dashboards, or APIs.
Covert Data Products: These solve problems without exposing the raw data to the user. For example, an automated drone delivering goods doesn’t show the user the underlying data used to calculate the route—it simply performs the task seamlessly.
As businesses and customers increasingly prioritize simplicity and effectiveness, data products are evolving from overt to covert. The focus is shifting to providing fast, intuitive solutions rather than overwhelming users with raw data.
Data products serve a variety of purposes, from enabling better decision-making to automating complex processes, enhance customer experiences. Their applications extend across industries and customer types, whether the end-user is internal (within the company) or external (another company or individual customers). A well-designed data product can be a competitive differentiator by offering:
Unique Data: Providing proprietary or hard-to-find datasets.
Exceptional User Experiences: Delivering seamless, intuitive solutions that solve specific problems effectively (Weber, 2021).
For example, a business intelligence dashboard might provide executives with key metrics for decision-making, while a machine-learning algorithm might power a recommendation engine for an e-commerce platform.
O’Regan (2018) categorized data products into functional types based on their purpose. Here’s a closer look at the five main categories:
Raw Data (Overt Data Products)
Provides access to cleaned or preprocessed data.
Example: A company offering access to weather datasets for research or development.
Derived Data
Supplies enriched or processed data, such as customer segmentation or predictive scores.
Example: A marketing analytics platform that adds demographic insights to customer data.
Algorithms as a Service
Delivers algorithms for external use, such as APIs for image recognition or text analysis.
Example: A picture similarity search engine used in online retail.
Decision Support
Offers insights and visualizations to guide user decisions.
Example: Analytical dashboards showing real-time performance metrics.
Automated Decision-Making
Executes decisions autonomously without human interaction.
Example: High-frequency trading systems in financial markets.
Data products interact with users and systems in various ways to deliver value. These interactions can occur via:
APIs (Application Programming Interfaces):
APIs enable seamless communication between software components, often within modern microservice architectures. For example, one microservice might supply raw data, which is then processed by a data product and passed on to another system via an API.
Dashboards and Visualizations:
Insights are often presented in visual formats to aid understanding and decision-making, as seen in business intelligence dashboards.
Web Elements:
Data products power interactive web elements, such as recommendation engines on platforms like Netflix.
These interaction methods ensure that data products can deliver insights or actions efficiently to end-users or other systems.
Sources:
Loukides, M. (2011). “The Evolution of Data Products.” O’Reilly Media: https://www.oreilly.com/radar/evolution-of-data-products/
Palmer, M. (2006). Data is the New Oil: https://ana.blogs.com/maestros/2006/11/data_is_the_new.html
Weber, H. (2021). The Basis of Data Product Management by Eric Weber, Head of Data Products Yelp. https://www.youtube.com/watch?v=TNIje1OrnFA
O’Regan, S. (2018). Designing Data Products: https://towardsdatascience.com/designing-data-products-b6b93edf3d23
