Demystifying the decision intelligence product ecosystem 📈

An easy to read introduction to the decision intelligence product ecosystem

Hello friends and welcome to the first article of Signal to Product!
This time, we’ll start from the ground up and clarify the main terminology that defines the decision intelligence product ecosystem.

Introduction - What does the umbrella term “Decision Intelligence Products” mean?

The decision intelligence product ecosystem is vast and contains a few sub-categories. This article aims to help you better understand what is included in this ecosystem and what is not.

Let’s start with a wider definition of the ecosystem as a whole.

Decision Intelligence Products are tools or solutions that span the entire journey from raw data to actionable insights, empowering humans to make informed, strategic decisions. They include three main categories:

• Data products (e.g. APIs, data catalogs, etc.)

• Information products (e.g. dashboards, reports, etc.)

• Insight products (e.g. predictive alerts, and dynamics recommendations)

These products work together to provide clarity, context, and guidance to decision-makers.

In addition to the three categories that can be considered the ecosystem's outputs, we can mention one more part of the ecosystem -  developer tools (e.g., Tableau, Python, Power BI, etc.). We can consider them the enablers and infrastructure of decision intelligence products, the tools needed to develop them.

What’s not included in the decision intelligence product ecosystem?

Data products can often be fed into autonomous machines that make decisions independently, whether based on hard-coded rules or AI algorithms. Waymo‘s self-driving taxis are an example of such machines, but they are not included in decision intelligence products. 

I draw the boundaries of the decision intelligence product ecosystem around solutions that help humans make decisions. Any solution geared towards machines making autonomous decisions without a human in the loop is out of this scope.

Also, decision intelligence products are geared towards gathering information for the sake of making decisions (aka “epistemic actions”) instead of solutions geared towards practical actions. For instance, you are 100% able to check the prices of products in an online clothing store, but I wouldn’t include online stores in this category because their main purpose is to make you buy products.

Take a look at the diagram of the decision intelligence product ecosystem that sums it all up:

The decision intelligence product ecosystem. Image created by the author

Now let’s talk about each product category individually.

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The World Bank Open Data Catalog. Source: The World Bank

Data Products

A data product is designed to provide data as the core output to enable human-led analyses and the creation of digital services. These products are often infrastructure-focused and help technological systems or humans access, manage, or manipulate data.

In short, think about raw data that can be used for many different purposes.
Data products’ main characteristics are:

• Raw or semi-processed data: The product delivers data directly or provides a way to interact with it

• Built for human or non-human data consumers: These consumers could be humans (e.g. data analysts, software application developers, etc.) or systems (like machine learning models)

Industry examples of data products

1. Datasets - Some notable examples in this category include the World Bank Open Data collection, and Google’s collection of computer science open datasets. You can literally take these data sets as is, and perform your own analyses

2. Applications Programming Interfaces (APIs) - Some interesting examples in this category include Finhub.io for real-time stock and crypto prices API, AccuWeather’s weather report API, and Moovit’s public transit API. All of these can be fed into the creation of information products for humans and more complex digital products. If you want to know more about how APIs work, check out this great post from PM Skills Weekly

3. Data pipelines - Processes that collect and transform data (e.g. providing additional input, data enrichment, etc.). An example could be taking a customer’s credit card transaction and assigning a spending category, a service that’s provided to many international banks by companies such as Personetics to enable things such as cross and upselling financial products and services to their customers

Now let’s explore the next level up - Information products

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Real-time flight tracker by Flightradar24

Information Products

An information product is designed to provide easy access to information for humans who need to make decisions in various contexts (e.g., drivers driving from point A to point B, business owners deciding whether to purchase additional stock, etc.). Information products focus on delivering information derived from raw data in a structured, actionable, and often user-friendly format. 

In short, think about the types of screens that show trends and real-time information.

Information products’ main characteristics are:

• Derived from raw data: An information product is built on top of raw or processed data but focuses on interpretation

• The purpose is decision-making: Information products are aimed at providing humans with information they can act on

Industry examples of information products

1. Interactive dashboards - There are many examples, and the ones that probably come to mind for many of us are Google Analytics, and company KPI dashboards for different members of the C-level managers that involve charts and graphs. The dashboard is a design element that’s used in many instances and we’ll touch upon it in future articles

2. Interactive maps help us make decisions based on spatial information. Many of us can think of Google and Apple maps, Waze, and more specialized maps such as Flighradar24 for airplane movements and MarineTraffic for ship movements

3. Static reports—These can be ad-hoc or periodic. For instance, you might receive a weekly exercise report from Fitbit or Google Fit or an ad hoc report about consumers or companies in country X generated by companies such as Mintel, Gartner, etc.

4. Content databases and information repositories - They could be internal or external to an organization. Think of a public library’s database, or an internal marketing content library that’s owned by a company and sits on platforms such as Bloomfire.

5. Market intelligence platforms - Interactive platforms that sometimes combine some of the elements mentioned before to give you a comprehensive solution to learn about a specific industry. Examples can be Fathom4sight, iSky Research, 11:FS Pulse in the fintech space, and Quantumrun for trends across various industries.

Let’s explore the next level up - Insight products.

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Climate risks by different categories for real-estate properties. Source: Zillow

Insight Products

Insight products are the final step in the decision intelligence value chain. They focus on providing specific, actionable guidance rather than leaving the user to interpret information. They tend to be embedded within a larger information product (or just a digital product) and trigger insights and alerts dynamically.

In short, think of the alert you get on Google Maps when you’re already on your way and it wants to suggest a short driving route for you.

Insight Products’ main characteristics are:

• Directly Actionable: They don’t just explain what’s happening or what has already happened but suggest what to do next

• Often Predictive or Prescriptive: Insights are frequently derived from advanced analytics or machine learning models

• Contextually tailored to Users’ Needs: Insights are highly contextual and often designed for a specific decision-maker or operational process and are triggered dynamically

Industry examples of insight products

1. Dynamic route suggestions on GPS applications such as Waze, and Google Maps

2. Location intelligence - Companies such as Local Logic and First Street offer these insights as a service to real-estate companies and embed them on websites such as Zillow (see the photo above)

3. Personal financial management - A larger group of banks offers dynamic insights about their spending habits and more to their clients. A few examples from the Canadian market where I’m based are RBC’s NOMI and Desjardins’ Alvie

4. AI-generated highlighted summaries - In the age of generative AI, some solutions involve AI-generated summaries that give you the main insights from various sources. Examples can be Google Gemini search highlights, or Dscout’s AI function that helps user researchers summarize insights fast

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The decision intelligence value chain

Another way to think about the three types of decision intelligence products is to view them as a value chain. Let’s break it down for you:

• Data Products: Provide the raw materials (data infrastructure, APIs, pipelines) and enable the creation of products in the next two levels - Information and insights products

• Information Products: Take raw data and transform it so you can get the full context and see trends and patterns (e.g. dashboards, interactive maps)

• Insight Products: Deliver actionable guidance and recommendations (e.g. predictive models, hard-coded and AI-based recommendations)

Take a look at this graph that explains how these products differ in terms of value and complexity:

The decision intelligence value chain. Image created by the author

• Value - How much value a user can derive from each type of product. It’s easier to take action on a recommendation generated by an insight product as opposed to using a large dataset produced by a government that requires a lot of processing

• Complexity - The level of complexity needed to develop a product in each category. It’s easy to release a dataset, but much more complicated to develop a dashboard with data visualizations, and it’s even much harder to develop machine learning models and insight products.

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Conclusion: Final Words

If you’ve made it to the end then I hope I gave you some food for thought about the different ways you can look at the decision intelligence produce ecosystem. I know it’s a lot of information and it might take a bit of time to digest.

Those of us who work on any of the three types of products can better understand in what category their product falls. It’s important to mention that in some cases, a product can play in more than one category as some companies offer an information or insight product, as well as an API that developers can use to develop even more applications and digital products.

In the next article, I’ll explore some of the additional characteristics that define decision intelligence products. This will help you better understand their different forms and shapes.

In the meantime, I’d love to hear your thoughts and comments on this article.

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©Yaron Cohen - 2025