What Are The Different Forms Of Data Products?
Two Forms of Data Products - And Two Data Product Enablers
This article is the fourth in a series on the Data & AI Product approach and how to craft impactful Data & AI Products. The first article explained what the Data Death Cycle is, the second one focused on defining what is a Data & AI Product, while the third one delved into the reasons why traditional Agile and Product Management approaches fail with Data & AI. This article explains the two main forms of Data Products, along with two key Data Product enablers.
Happy reading, and we welcome your feedback and any other definitions you have identified as valuable in your experiences.
Understanding Data Products can feel daunting due to their complex definitions and diverse interpretations. A quick search for “types of Data Products” reveals a range of perspectives from industry experts.
Data Products are often categorized as “source-aligned,” “consumer-aligned,” or “aggregate.” Source-aligned products contain minimally transformed operational data, consumer-aligned products reflect business insights, and aggregate products combine multiple data sources to track key metrics.
Others define Data Products by complexity and level of automation, such as raw data, derived data, algorithms, decision support, and automated decision-making.
Although these classifications offer value, simplifying and demystifying these ideas can help clarify them.
In a previous article, we defined a Data & AI Product as “a Product primarily powered by Data and/or AI, designed to leverage the potential of Data & AI to solve problems and create value for its target users”.
In this article, we explain two primary categories of Data Products—Data Analytics Products and AI Products—along with two essential enablers: treating data itself as a product and establishing a Data Platform.
The Foundation: What Makes a Data Product?
The heart of any Data Product can be resumed in the following way:
At its core, every Data Product follows a core pattern: it takes data as input, processes it through a transformation/modeling layer, and outputs valuable results. Think of it as a refined recipe—raw ingredients (data) go through specific preparation steps (modeling) to become a final dish (output).
The complexity of each part and their interactions determine the type of Data Product we’re discussing. Data Analytics Products rely on human-defined, deterministic rules, while AI Products operate on algorithm-driven, data-learned rules.
Two Main Forms of Data Products
Rather than diving into numerous technical classifications, we can simplify by thinking of Data Products in two main forms: Data Analytics Products and AI Products.
Data Analytics Products
Our definition: A Data Analytics Product is a software or platform that processes and analyzes data to generate insights, facilitate decision-making, and drive strategic business actions.
The primary purpose of a Data Analytics Product is to empower organizations to make informed decisions by uncovering insights that drive actions like:
Enhancing operational efficiency
Identifying market trends
Optimizing processes
Improving customer satisfaction
Driving revenue growth
Here is what they are composed of:
Data Analytics Products may for example focus on sales performance, operations, market trends, and price analysis. They integrate multiple data sources, applying analytical models to interpret data and reveal insights that evolve alongside organizational needs. Often, they serve as the groundwork for future AI Products by highlighting insights that can inform advanced modeling.
Despite their benefits, Data Analytics Products can suffer from the “Yet Another Dashboard” syndrome, where low user adoption occurs when users find themselves exporting data to Excel rather than using the dashboard features. Common challenges include poor data quality and misaligned KPI definitions, which can erode trust and limit engagement.
Takeaway #1: A dashboard is not a product if it is only restricted to data and KPI publication. A Data Analytics Product aims at uncovering insights that help make better decisions.
AI Products
Our definition: An Artificial Intelligence (AI) Product is a system or application that leverages algorithms and computational models to ease tasks that typically require human intelligence.
The goals of AI Products are to:
Automate complex processes
Provide predictive insights to support decisions
Enable personalization in areas like language processing, computer vision, and pattern recognition
Analyze vast datasets to uncover trends and patterns
Here is what they are they work:
AI Products differ from Data Analytics Products by offering probabilistic rather than deterministic results. They learn from data over time, which allows them to perform tasks like recommendations, personalization, image recognition, and content generation with increasing accuracy.
Depending on the application and the problem to solve, the “training” part of the Machine Learning model described on top the the above schema is optional. In many cases, an AI Product leverages a pre-trained Machine Learning model that can be used without further training.
This is often the case with GenAI applications where training (or fine-tuning) is not needed because the use of foundation models is enough. In this case, the “Data Input” is rather a Prompt, and the Data Output can take many forms.
In every case, this probabilistic approach introduces some uncertainty, meaning AI outputs need continual monitoring and refinement to remain effective.
Takeaway #2: An AI algorithm is not a product. An AI Product encompasses the whole user experience, and is not restricted to the algorithmic part alone.
Essential Enablers: Making Data Products Work
By restricting to only two forms of Data Products, we know some questions may arise: What about Data As A Product? What about Data Platforms?
Here is our take: Data As a Product is a mindset, and a Data Platform is not a Data Product. But treating data as a product and building Data Platforms are key enablers for creating and extracting maximum value from both forms of Data Products.
Let’s explain all that.
Treating Data As A Product: A Key Enabler to Value Creation
Our definition: Treating Data as a Product means approaching the management of an organization’s analytical data with the same level of care, strategic planning, and focus on user satisfaction as one would for any other product.
In today’s data-driven landscape, treating data as an afterthought is a missed opportunity. Inspired by Zhamak Dehghani's Data Mesh, this approach involves handling analytical data with care, clear planning, and a strong focus on user experience, much like any other product.
Viewing Data as a Product means treating it like a microservice governed by a Data Contract that defines:
Data structure
Quality standards
Terms of use
Clear data-sharing agreements
This approach fosters trust, ensuring that changes in data schemas don’t disrupt downstream processes. It also encourages data producers to recognize and manage the inherent value in their data.
Takeaway #3: Data As A Product is a mindset, a needed cultural shift between Data Producers and Data Consumers. Treating Data as a Product is a key enabler to building robust Data Products.
The Data Platform as a Data Product Enabler
Our definition: A Data Platform is a Product that enables the collection, storage, management, processing, and analysis of vast amounts of data from multiple sources.
Data & AI teams face pressure to deliver more value quickly, showcase measurable ROI, and improve quality without increasing resources. Developing a Data Platform offers a robust solution for managing the growing volume of Data & AI initiatives.
A Data Platform provides a unified, secure, and scalable environment for data management, analytics, and modeling. Despite these advantages, Data Platforms can sometimes be perceived as restrictive or overly complex, leading to low adoption or “shadow platforms” as users seek workarounds.
To unlock a Data Platform’s potential, it must be approached as a Product, prioritizing end-user satisfaction. This involves focusing on features that align with user needs and organizational goals. A successful platform integrates four essential capabilities—Ingestion, Storage, Transformation, and Serving—reflecting the Data and Machine Learning lifecycle to accelerate Data Product development.
Building a product-oriented Data Platform requires technical expertise, organizational alignment, and attention to user experience. With a structured operating model, governance, and ongoing adaptation to user needs, a well-designed Data Platform fosters innovation, growth, and competitiveness, establishing the foundation for crafting scalable, impactful Data Products.
Takeaway #4: A Data Platform is not a Data Product, but is a Data Product enabler and should be implemented with a Product Mindset.
How These Elements Work Together
Data Products are dynamic and often evolve together; a Data Analytics Product may eventually adopt AI features, and an AI Product may originate as a straightforward analytics tool. This evolution is natural and integral to the lifecycle of Data Products.
As organizations increasingly adopt data-driven strategies, understanding these distinct types of Data Products becomes more relevant. A successful data strategy requires recognizing how these elements interact:
The Data Platform provides the essential infrastructure
Data As A Product principles ensure quality and reliability
Data Analytics Products offer actionable insights
AI Products add intelligence and automation
However, it’s essential to remember that Data Product forms exist on a continuum; they aren’t binary classifications or simple boxes to check.
The aim of this article was to offer a simplified and actionable understanding of the different forms of Data Products. That being said, we encourage you to avoid falling into dogmatic discussions about what is and is not a Data Product. The mindset on how to craft impactful Products that are primarily powered by Data & AI is way more important than ambiguous categorizations that do not help to solve real problems.
We hope to bring another vision of the world, focusing on the people, the mindset and the collaboration rather than on technology or exact matching into one definition or another.
What do you think? Have you witnessed other valuable Data Product categorizations?
Stay tuned for the next articles to learn how to escape the Data Death Cycle and Craft Impactful Data & AI Products! In the next article, we will talk about the controversial role of the Data/AI Product Manager.
Hello, as a PM on a Data Product I find this series really interesting, however I find the framing of all data Products only as Analytics or AI really restrictive. In the Product I work on, there's both Analytics and AI however these are more sideline use cases of the Data. In my case, it would be more of a "Data Engineering Product", and I'm not sure how to approach your articles in this case.
hi, I think this is a useful article but I slightly disagree with the framing. Making Data Analytics and AI the 2 types frames this as aspects of technology. I tend to use a framing around operational vs analytical data planes. The reason I do that is that the operational data plane is all about "doing transactional stuff" (like sell a product, support a customer, manufacture something) so a "data product" in this framing is one that helps operational stuff. For example the cross-selling chatbot on your company website might rely on a data product that matches customer details to a cross-selling propensity list.
Whereas the creation of the cross-selling propensity list is in the realms of a data product in the analytics realm (where analytics can vary from a simple regression formula to a more nuanced ML model). I tend to think of this as a data analytics product, and there is obviously a spectrum of data products from the operational to the analytical (and AI if you want to separate that).