EXPERTISE
Product Data/IA
United by the concept of “Data as a Product,” we are involved from the very beginning of a data platform’s development and throughout its entire lifecycle. Driven by a shared passion for innovation and excellence, we apply our expertise to help our clients and businesses unlock the value of their data and tackle the data challenges of tomorrow.
The finding
Product teams still rely heavily on intuition when making decisions. Not because of a lack of data, but because the data that does exist doesn’t answer the questions they’re asking: products were never set up to track this information, the events that were collected were gathered without a specific purpose, and no one has a clearly defined role in finding solutions to this issue. As a result, product data remains the domain of technical specialists, disconnected from the roadmap decisions it should be informing.
That’s exactly where our Product Data Practice comes in.
Our support
Our Product Data and AI experts help you foster a data-driven culture within your company. They transform the way data is used, ensure data quality and the robustness of solutions, so that data becomes a strategic asset and a true source of value.
We view AI as a catalyst for digital performance, and we use it as an agile, modern solution that accelerates, enhances reliability, and enriches the development of data products.
We establish management rules that prevent the most common pitfall: events added over the course of sprints by different developers, without a common set of guidelines, until no one knows what a metric actually measures. This rigor is what allows business teams to rely on the data without questioning it every time they use it.
Our professions
Data Engineer
Data Analyst
Data Scientist
Data Manager
Data Steward
CPD Data
Business Analyst
Our expertise
Data Engineering
Data Governance
Data Platform
Data Product
Data Management
Data Storytelling
Data Analysis
IA
Machine Learning
Examples of assignments
A customer in the aerospace industry brought us on board to build a data platform and ensure migration to the AWS cloud. We set up a robust, accessible data service, and carried out a smooth migration thanks to technology choices adapted to their needs.
Within the Software department of our client, a French car manufacturer, we were integrated into an R&D project to support the implementation of innovative development methods using generative AI. Our involvement focused mainly on setting up an end-to-end pipeline for the solution, providing day-to-day support as an AI expert, and drafting scientific documentation to facilitate the preparation of the project's CIR file.
Our banking client wanted us to strengthen their IT support team for 2 years, with the aim of improving their BI reporting productivity for business management. The challenge was to optimize and implement the technical processes needed to control the entire data chain, from collection to restitution using Power BI. We contributed to a technological watch on the tools and sources available, in order to propose new requirements.
In a context of cross-functional collaboration in the field of retail and mass distribution with the Data professions: Data Analyst, Web Analyst, Data Managers. The main objective of this 14-month assignment was to identify needs, transform them into tasks and integrate them according to their importance to the roadmap and their impact on existing systems. Our intervention also aimed to ensure RGPD compliance for our client's various Data projects.
On a major aquaculture fluid mechanics simulation project, our customer requested our expertise in quality analyses to guide the project management. Our intervention led us to carry out analyses and predictions for several R&D projects under NDA.
Do you have a Product Data/IA project?
APPROACH 1
Staffing
Support from one of our Product Data experts, integrated into your Product teams, to provide targeted expertise and help you achieve your goals.
APPROACH 2
Consulting
Support from one or more product experts on a strategic or operational product issue.
APPROACH 3
Customized workshop / training
A customized workshop lasting from half a day to 3 days, led by a data manager, to solve a product-related problem, generate ideas, or raise awareness among your teams on a specific topic.
Our Practice Leader
Mylène
Toulouse
Toulouse
“At 5 Degrees, our mission or career wishes are heard and we are helped to achieve them.”
Mylène, Practice Leader Product Data
Giving meaning to data to drive product value
Today, data is an essential driver of decision-making and innovation. At 5 Degrés, we help your company transform its product data into a strategic management tool.
Our approach is based on a simple principle: data should support decision-making, not the other way around. We help our clients manage and implement data strategies that support their product vision and promote informed decision-making at every level.
Building a data architecture that serves the product
The primary challenge with product data is structural: collecting the right information, in the right place, at the right time. Our teams help you define a clear and interoperable data architecture:
Product instrumentation
Tracking key events
Processing pipeline
Custom dashboards
Every piece of data we collect serves a measurable purpose: to improve the product and maximize its value. We prioritize tools that are simple, reliable, and compatible with your technical ecosystems to ensure the long-term viability of your data and analyses.
From raw data to product insights
Collecting data isn't enough—you also need to understand it. Our experts in data analysis and data visualization translate metrics into concrete action steps. We help your teams interpret key signals to prioritize improvements, guide the product data roadmap, and enhance the overall management of your project.
Combining design, data, and strategy
Product data doesn’t stand alone—it’s part of a broader strategy. By combining insights from design, development, and data, we help teams identify growth opportunities, manage tests of new hypotheses, and validate sound intuitions using data. In this way, data becomes a tool for collaboration rather than an isolated product silo.
Empower Your Teams
Our goal is not to remain indispensable. Every project includes a skills-transfer component: documenting instrumentation choices, training product teams to interpret data, and helping them become self-sufficient in using the deployed tools.
Training based on your own datasets instills lasting habits and helps your teams find the right solutions—where a textbook example remains purely theoretical. The challenge goes beyond the technical: it’s about giving design and development teams a common language around data, so they can understand how your products work and make decisions together rather than having them dictated by a single function.
FAQ - Product Data & AI
The “Data as Product” approach involves treating a dataset as a product in its own right: identified users, a documented scope, an expected level of quality, a designated owner, and a defined lifecycle. It puts an end to one-off data deliverables that no one maintains six months after their creation. In practical terms, this changes day-to-day management: each dataset has an owner who is accountable for its reliability, accessible documentation, and governance rules that endure even after the person who created it leaves.
The Product Data Manager (or PDM) serves as the link between the product and data teams. Their role is not to build data pipelines or produce analyses, but to define what data the product needs to collect, why, and how it will be used. It’s a role that involves translation: they translate the questions posed by the product team into data requirements and convey what the data reveals in business terms. The required skills combine product knowledge, sufficient technical expertise to communicate with developers, and genuine proficiency with day-to-day Product Data Management.
Instrumenting a product involves embedding data collection points into the code that will reveal what users are actually doing with it. This decision must be made before development begins, not afterward: adding events to a product that has already been released is always more expensive than planning for them from the design phase. The process involves defining a tagging plan (which events, which associated properties, and what naming conventions to use) and then having the developers implement it. The product’s technical constraints then determine the data collection solution that is ultimately chosen.
The most common instinct is to track everything, which results in volumes of unusable data. It’s better to start with the questions the team is asking: Which steps in the journey are causing problems? Which features are actually being used? Where are users dropping off? Three categories of events cover the majority of digital product needs: steps in the onboarding journey, value-driven actions that indicate actual usage, and friction points such as errors or drop-offs. Every event collected must be linkable to a decision.
A single metric isn’t enough. The gross adoption rate—how many users tried the feature—says little without its counterpart: how many returned to use it. You need to analyze the reach, frequency, and depth of usage, then compare these metrics to a reference population. A useful dashboard also displays the time between release and first use, which reveals discoverability issues. And the measurement should be based on a time frame consistent with the product’s natural usage cycle, not a default calendar month.
Data doesn’t make decisions for the product team—it reduces uncertainty. It comes into play at three stages: early on, to objectively assess a problem and prioritize topics; during the design phase, to define the hypotheses to be tested; and after delivery, to verify that the expected outcome has occurred. The pitfall is consulting the data only at the last minute, to justify a decision that has already been made. A product data roadmap that is truly data-driven requires that success metrics be defined before each project is developed—not after.
Usage data tells us what users do; user research tells us why. Taken in isolation, both can be misleading: a high abandonment rate on a form doesn’t indicate whether the problem stems from its length, a field that’s difficult to understand, or an inopportune timing. The most effective approach is to use quantitative data to pinpoint the problem, then qualitative data to explain it. The reverse also works: an issue raised by a few users in interviews can then be verified across the entire user base.
Start with a business question, never with a tool. Identify a decision that was truly made without sufficient information, determine what data would shed light on it, verify that the data is collected and reliable, and then produce the corresponding analysis. This first complete cycle contributes more to a company’s cultural transformation than a platform designed for uses that are still hypothetical. Training for business teams comes next, and it’s best to base it on your own datasets rather than textbook examples: the skills acquired can be put to immediate use.
Expect it to take a few weeks to produce your first usable deliverable—a validated metric, an approved dashboard, or a validated prototype—provided the data exists and is accessible. Timelines lengthen significantly when data must first be consolidated across heterogeneous systems, or when its quality must be verified before any analytical use. In fact, this is what most companies find: the amount of preparatory work often exceeds the time spent on the analysis itself.
Between data teams and product teams, there is often a missing link: someone who translates business questions into data requirements and analysis results into product decisions. That is the role of the Product Data Manager, or PDM.
His job is neither to build pipelines nor to produce analyses, but to decide what the product should measure and why. The required skills combine product knowledge, sufficient technical expertise to communicate with developers, and proficiency in day-to-day data governance.
Not all companies need a dedicated position. In many companies, this role is filled by a product manager who is developing data skills, or by a data analyst who works closely with the product teams. What matters is that someone explicitly takes on this responsibility.
Latest news in Product Data/IA
Expert articles, interviews, experience feedback and recaps of our exciting in-house events and projects.
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