EXPERTISE
Data Analysis
Are you still making decisions without a clear understanding of your data? We transform your raw data into actionable insights so you can manage your business with clarity and confidence.
The finding
Having data isn’t enough. Too often, this data is scattered across disparate tools and siloed IT systems; it’s poorly defined and difficult for business teams to interpret. As a result, decisions are made based on gut instinct, reports multiply without being truly aligned, and data continues to be viewed as a technical issue rather than a business driver.
That is exactly where our Data Analysis Practice comes in.
Our support
Our data analysts integrate into your teams to understand your business challenges before building anything. We work across the entire analytics chain: from defining your key metrics to deploying your dashboards and training your teams. Our data analysts apply statistical methods tailored to each business question, from simple metric tracking to the most exploratory analyses.
What we solve
Your data is available, but no one really knows how to interpret or use it?
We organize your information, metrics, and data; train your teams; and implement tools that make data accessible to everyone, without requiring advanced IT skills.
Are your reports time-consuming, unreliable, or poorly adopted by the business units?
We redesign them to address your actual management challenges, automating what can be automated and simplifying what needs to be simplified.
Does your organization lack data maturity and governance?
We establish the frameworks, processes, and best practices that lay the foundation for a sustainable data culture.
Our expertise
Business Intelligence & Reporting
Data visualization
Data storytelling
Exploratory analysis
Data Governance
Data management
GDPR Compliance
Our professions
Data Analyst
Business Analyst
Data Steward
Data Manager
Our tools
Examples of assignments
As part of the management team of an e-commerce site for electronics and household appliances, a consultant was called in to improve the purchasing and post-purchasing customer experience. Analysis of sales KPIs, NPS, customer service activities and user feedback to define a strategy and roadmap, and proposal of a Discovery methodology.
Working for a customer in the supply chain sector, our team, comprising two Product Managers and a Product Designer, was dedicated to the delivery and discovery of their applications. Our intervention was also aimed at guiding the customer towards a more product-centric approach (frequent delivery, OKR, product vision, design process, post-implementation follow-up).
As part of the Product teams of a player in the sports betting industry, our Product Manager leads a multi-profile team: Tech, QA, Design. He is involved in identifying and prioritizing Product initiatives in the context of the creation of a new Offers platform. He leads the Delivery activity in an environment with strong dependencies between Product squads.
In an agritech startup developing autonomous robots, our Product Manager structured the Product organization to support its ramp-up. The mission began with a co-constructed 3-5-year product vision, which was then translated with the teams. A diagnosis of 20 key themes enabled us to measure product maturity and prioritize 33 concrete recommendations. The intervention laid down the fundamentals: clarification of roles, adoption of OKRs, Definition of Done, framing of tests, structuring of rituals. The result: faster decisions, better coordination, smoother development cycles and enhanced performance.
As part of the digital department of France's leading radio group, our Product Management consultant oversaw the design, implementation and roll-out of a new program schedule management tool for all the group's channels. The mission began with an in-depth Discovery phase (user interviews, benchmarking of other media practices, "make or buy" arbitration), and continued with the definition of a clear Product vision, the elaboration of a structured roadmap, the delivery of an operational MVP, and then the progressive roll-out to each channel.
Within the digital department of France's leading radio group, a consultant led the design, implementation, and rollout of a new program schedule management tool for all of the group's channels. The project began with an in-depth discovery phase (user interviews, benchmarking of other media practices, "make or buy" decision-making), followed by the definition of a clear product vision, the development of a structured roadmap, the delivery of an operational MVP, and then gradual deployment across each channel.
How we work
We always start by gaining a deep understanding of your business challenges before making any recommendations. No off-the-shelf dashboards: every analytics solution is tailored to your metrics, your users, and your product goals.
The choice of methods follows the same logic: analyzing a cross-tabulation of thousands of statistics and dozens of variables does not require the same techniques as weekly performance tracking. Depending on the situation, we use descriptive statistics, segmentation, or factor analysis (principal component analysis, correspondence analysis) to identify the information that truly shapes the big picture.
Our Data Analysts work closely with all of your teams—including Product teams, Product Managers, Developers, Data Engineers, and Data Scientists—to ensure that analysis is never just a technical exercise, but rather a powerful tool for driving your products and business decisions.
Do you have a project in data analysis?
APPROACH 1
Staffing
A data analyst consultant who works alongside your data teams to provide targeted expertise on your reporting, analysis, and management challenges. Whether you’re looking to bring specialized expertise in-house or to establish a key data analyst role within your company, we can help you scale your operations.
APPROACH 2
Consulting & Auditing
Support from one or more data analysts on a strategic or operational issue: structuring your data governance, overhauling your reporting processes, and implementing your key performance indicators.
APPROACH 3
Customized workshop / training
A customized workshop lasting from half a day to three days to define your data strategy, train your teams on the tools, or foster a data-driven culture across your business units. Each training session is built around your own datasets, so that the skills you learn can be immediately applied in real-world work situations.
They give us trust
Turning data into a decision-making tool
In an environment where data is growing exponentially, true value lies not in the volume of data but in the ability to use it to make informed decisions. Analysis means understanding what is happening, why it is happening, and what to do next.
At 5 Degrés, we help our clients and businesses turn raw data into actionable insights—the kind that guide a roadmap, justify an investment, and improve the user experience. From scale-ups to companies with thousands of employees, our approach remains the same: we start with the decision that needs to be made, never with the tool to be deployed.
An approach focused on your business challenges
Our analysts don't create dashboards just for the sake of creating dashboards filled with statistics and information. They start by understanding your business objectives so they can build analyses that truly address them.
Understanding your business and organizational contexts
Impact- and decision-oriented approach
Close collaboration with your data, product, and business teams
Ongoing monitoring of analytical tools and practices
Goal: to provide reliable, clear, and actionable insights. Each metric is chosen for the decisions it enables, not for the data it displays.
From reporting to governance
Effective reporting relies on high-quality, well-defined data that is shared by everyone. We support our clients and all businesses in implementing their data governance: defining data standards, managing access, documenting metrics, and ensuring GDPR compliance.
This rigor is what transforms a reporting tool into a true management tool for the company—one that is adopted and maintained over the long term by business teams. In this way, we strengthen internal capabilities through ongoing training and the adoption of new collaborative tools.
The storytelling as a driver of adoption
Data only has an impact if it is understood and put to use. Our data storytelling experts translate complex analyses into clear narratives that are accessible to everyone across the organization, from operational teams to executive committees. The information is prioritized based on the decisions it enables, not on the technical complexity involved in producing it.
Because an informed decision starts with clear communication.
The Data for Product and Business Units
Our data analysts work closely with all relevant stakeholders—data engineers, data scientists, product managers, developers, and business teams—using data as a common language to advance a shared vision.
This cross-functional approach speeds up projects, aligns teams, and ensures that the analyses produced meet the company’s actual needs. The result: more autonomous teams, faster decisions, and higher-performing products.
FAQ - Data Analysis
A report presents figures. A management tool informs a decision: its metrics are chosen for the insights they provide, their definitions are documented and shared, and they are consulted at a specific point in the decision-making cycle. A report can be perfectly accurate and still fail to trigger any action—which is the most common scenario.
Three causes consistently arise: the metrics do not align with the decisions teams actually make; their definitions are not shared, which fuels disagreements over the numbers; or interpreting the data requires too much effort. A dashboard that isn’t adopted is almost always a problem of context, not visualization.
Start with decisions, not the available data. For each potential indicator: What decision does a change in it trigger? Who is responsible for it? How often is it reviewed? Those that don’t pass this test make the report harder to read without adding any value. It’s better to have five indicators that are actually tracked than forty that are simply listed.
This is the practice of translating an analysis into a narrative that is understandable to the intended audience: context, findings, cause, consequence, and recommended action. It is key to ensuring acceptance, because an accurate but unreadable analysis will not lead to any decision. The level of detail is tailored to the audience, ranging from an operational team to an executive committee.
It encompasses the rules that ensure data remains reliable and usable over time: shared data repositories, indicator documentation, access management, quality controls, and GDPR compliance. The most cost-effective starting point is the indicator dictionary: establishing a single, formal definition for each metric eliminates most disagreements over the numbers.
We remain neutral on this issue. The decision should be based on your existing ecosystem, the internal expertise available to maintain the dashboards, and the level of autonomy desired for the business units. A tool that seems perfectly suited on paper but that no one knows how to evolve quickly becomes a roadblock.
The data analyst starts with the data: they collect it, clean it, model it, and extract actionable trends from it. The business analyst starts with the business process: they formalize needs, define business rules, and translate operational challenges into requirements. The two converge on interpretation, but they do not use the same tools or resources. In a small team, a single role often covers both areas. Specialization becomes useful when data volumes increase or the processes to be defined become more complex.
Business Intelligence describes what has happened: it consolidates historical data into dashboards that track activity and trends. Data analysis goes a step further by examining why these events occurred and what conclusions can be drawn from them, through ad hoc and exploratory analyses. The two are complementary: BI provides regular monitoring, while analysis informs specific decisions. The line between the two has become blurred with the advent of big data platforms, which now make it possible to cross-reference large volumes of data without dedicated infrastructure.
Exploratory analysis seeks to uncover patterns in the data without any preconceived assumptions: unexpected correlations, homogeneous groups, and truly discriminating variables. It often precedes the development of a metric, to verify that it actually measures what we think it does. Depending on the situation, it may involve descriptive statistics, segmentation, or factor analysis. This is the appropriate approach when a dataset contains many variables and no one knows where to start. It requires more time and resources than traditional indicator monitoring, but it is the method that reveals trends that a dashboard does not show.
By focusing on three key areas. First, definitions: when everyone understands an indicator in the same way, there’s no debate over the numbers. Next, presentation: a clear dashboard is better than direct access to a database, even a well-documented one. Finally, support: training built around your own datasets fosters lasting autonomy. Optimizing a tool can never compensate for insufficient guidance. A common mistake is to open up broad access in the hope that usage will follow, when in fact adoption stems from clarity, not from the volume of data made available.
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