Introduction

Today we are talking about an important concept that often creates confusion: “product” in the Data and AI world. It is a term that sounds simple, yet hides a lot of complexity once you dig into it.

Think about the shiny .pbix you built in Power BI Desktop, or the K-NN clustering .ipynb notebook you coded in Jupyter. Impressive work, but honestly? On its own, it is pretty much useless. Like totally useless, why? Because no one is going to hand your laptop to the end user…

This is where the idea of a product comes in. But watch out ⚠️ there are homonyms, false friends, and some traps to avoid. That is exactly what we will try to untangle in this article💡.

The world of homonyms

The tricky part is that the word product is used all the time in many different contexts. Everyone agrees it sounds important, but if you ask two different teams what they mean, you often get two completely different answers ≠.

For some, product means a way of working, of organizing projects. For others, it means a recommendation system or a dashboard that is in production and that business teams can use directly. Both sides are correct, because they are not talking about the same thing.

This is where the confusion starts 😐. Without clarity, teams argue about terms and concepts, when in fact they are using the same word to describe two different realities. So it’s time to break things down into false friends, close friends, and real friends !

False Friend: Product Data/AI

In this sense, product means applying product management practices to Data and AI work. We’re not talking about datasets or dashboards here, but about the way we organize, prioritize, and deliver Data & AI initiatives.

This is where methods like Scrum, SAFe, or Lean come into play. Starting in the 2010s, as Data & AI initiatives became more mainstream, organizations began managing them with the same rigor as traditional software projects (think about a website / a mobile app): clear ownership, a roadmap, a backlog, iterations, and measurable outcomes.

This is also where we move away from the old waterfall mindset. Instead of running projects in a linear V-cycle, we embrace iterative delivery and we have agile ceremonies like sprint planning, sprint retrospectives, PI Planning, etc.

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So to wrap it up, this meaning of product has nothing to do with dashboards or datasets. It’s about a methodology, a way of working to manage Data/AI projects effectively !

Close Friends (or Enablers)

Close Friend #1: Data as a Product

Data as a Product means treating data itself — the tables, the records, the raw content — as something that can be delivered and consumed with the same care as a finished product. Think of a sales transactions table, a customer dataset, or an Excel export: on their own, they are just files. But once managed as products, they become reliable inputs for analysis and modeling.

The key idea is that if data is to be used by Data Analysts, Data Scientists, or ML Engineers, then it needs to be usable: reliable, documented, versioned, discoverable, and supported. Not a spreadsheet buried in someone’s desktop ⚰️, but a certified dataset you can trust and reuse.

In this sense, Data as a Product is an enabler. It gives data practitioners confidence that what they’re working with is consistent and dependable. Without it, every analysis, ML model, or AI system risks being built on fragile foundations. That’s why Data as a Product is one of the core principles of Data Mesh.

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Imagine, in an ideal world (because I haven’t seen it so far 💭), a data marketplace inside the company where certified datasets and feature stores are easy to find and plug directly into your work. That’s the promise of Data as a Product!

Close Friend #2: Data Platform

Another close friend is the Data Platform. It is not a data or AI product in itself, but it is essential as the foundation and the paved roads that allow data and AI Products to exist and scale.

Think of platforms such as Databricks, Snowflake, BigQuery, or Fabric. They provide the environment where architectures like data warehouses, lakehouses, or meshes actually come to life. Without a platform, these architectures remain abstract diagrams on a whiteboard.

A data platform handles the ingestion, storage, transformation, and serving of data throughout the analytics and machine learning lifecycle. The mistake many organizations make is to call the platform itself a product. In reality, its value lies in enabling others to build products on top of it – not in replacing them.

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A Data Platform is not a Data Product. It’s an enabler, the engine room that gives Data & AI Products the foundation to actually run and scale.

Data/AI Products

So, we’ve talked about the fake friends and the close friends… but friends of what, exactly? Well, they’re the friends of the real Data/AI Products. 📍Let’s pin down what that actually means.

Let’s start with the basics: what is a product? At its core, a product is a packaged solution designed to deliver value to a specific user. A Data or AI Product takes this a step further: it’s a solution where data or AI is the main engine of value creation, not just a side effect. In other words, it’s not simply a website or an app that happens to produce/consume data, but something built to turn data into real impact.

It’s also not just a one-off dashboard, a notebook, or a model hidden away on someone’s laptop. A true Data/AI Product is reliable, accessible, and supported — something people can actually adopt and rely on in their everyday work. That’s the difference between a product and a simple output or artifact.

Data & AI Products - DTL

And you know me 😗 when it comes to mind mapping, I like to break things down into categories. So let’s do that here. This isn’t an exact science, and everyone might slice it differently, but I believe the following three categories are the most useful.

#1 Analytics / BI

This is the first big family: everything related to analytics and Business Intelligence. The objective here is simple, to look back and make sense of what has already happened. It covers the descriptive side (What happened?) and sometimes the diagnostic side (Why did it happen?). In short, the goal is to help teams describe and understand the past and present.

Here is a non-exhaustive list of analytics products: 

  1. Dashboards, like Tableau or Power BI, the kind of sales board a manager opens every morning to check the pulse of the business.
  2. Monitoring tools, such as Grafana, are live web applications where metrics, logs, and alerts refresh constantly to show if a pipeline is failing or a system is slowing down.
  3. Reporting & exploration, closer to self-service BI, where a cleaned and consolidated dataset is made available for download or direct query so that teams can explore it on their own.

We can split these products into two main flavors:

  • Strategic analytics: long-term, high-level insights that guide leadership and management. Think about a consolidated financial reporting suite for quarterly reviews, or a marketing performance portal comparing campaigns over time. 👉 The success metric here is simple: adoption at the top. Are leaders actually making decisions with it?
  • Operational analytics: real-time or near-real-time insights embedded directly in day-to-day workflows. For instance, a supply chain dashboard that alerts when inventory drops below threshold. These products can rely on a data platform or an operational data store.
    👉 Here, the key metric is actionability. Are frontline teams actually using it to act immediately and improve operations?
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This split is not exclusive. You can have hybrid products, like a customer support dashboard that gives executives an overview of ticket volumes and resolution times, while letting operational teams see which cases are still open and who is handling them.

#2 Predictive / Prescriptive

The second family is predictive and prescriptive products, this is where we go beyond describing the past and start anticipating the future, or even recommending what to do next. In other words, anticipate and optimize.

Here we find products powered by traditional data science, applying machine learning and deep learning to both structured and unstructured data. This translates into techniques such as regression, clustering, and classification. It also covers NLP for text processing, computer vision, and speech recognition. Examples include recommendation engines, churn prediction, sales forecasting, autonomous driving, sentiment analysis, etc. Their shared promise is simple: leverage past and present data to predict outcomes or recommend the best actions.

The success metrics here are accuracy, trust, and impact. Accuracy because users must believe in the predictions, trust because the outputs need to be reliable enough for planning, and impact because the model should ultimately deliver real value: reducing fraud, improving retention, optimizing resources, or detecting a tumor in an MRI scan.

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And of course, the line between predicting and prescribing is blurry. A churn model that flags customers at risk is predictive, but if it also suggests the best retention action, it becomes prescriptive. Many products in this family combine both.

#3 Generative / Agentic

The third family is generative and agentic products. Here we move beyond predicting or optimizing, and step into creating, interacting, and acting. The goal is simple: create, empower, and augment users.

This is the world of Generative AI, powered by large language models (LLMs), GANs, and diffusion models. These products don’t just analyze or forecast, they produce new things: text, code, images, videos… and, when combined with an agentic layer, they can even reason and perform actions.

In practice, this includes conversational interfaces (chatbots, assistants, copilots), creative engines (content generation tools for text, visuals, or code), and increasingly agentic systems that chain reasoning with execution. Unlike predictive ML that often works in the background, generative and agentic products sit front and center: they interact in natural language, deliver creative outputs, and sometimes take initiative to execute tasks on behalf of the user.

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The real promise here is augmentation: helping humans do more, faster, and better. If analytics products describe, and predictive products anticipate, generative products give users a new superpower 🦸 !

Product vs Artifact

Is the article finished? Hell no 🔥! We laid out categories and examples for data and AI products. However, there is a crucial point to make: a dashboard is not automatically a product. It can become one if it meets a few hard requirements. For example, a dashboard or a model that lives on a developer’s laptop and works one out of five times is an artifact. Let’s define what that means:

  • Artifact: a technical output, like a dashboard, a notebook, or a model demo. It is useful as a step in the process, but not something real users can rely on.
  • Product: a solution that is reliable, supported, and built to last, with users, clear value, and a managed lifecycle.

So after all of that, what actually makes something a product? Here are a few key ingredients, provided in this chart.

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If these elements are missing, you have just an artifact, useful for a demo, maybe, but doomed to be forgotten. So make sure you stick to the product mindset, so you build something people can trust, adopt, and build on 🚀 !

Conclusion

So now that you can tell the difference between there / their / they’re, I’m gonna say BRAVO 🎉!

You’ve also leveled up by learning to tell apart the different kinds of Data and AI Products, along with their false friends and close friends.

If you want to go further and explore the different platforms that make all this possible, 👉 click here.

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