Strategy

Make your data reliable so you can make risk-free decisions.

Your data exists. But is it reliable, accessible and organized to feed your strategic decisions? We build the Data and AI strategy that turns your data into a well-managed decision-making asset.

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+150 companies supported
— 5/5 on Google

benefits / impact

What it delivers

Concrete results for your organization, measurable from the very first weeks of the engagement.

01

A clear, prioritized Data roadmap

You know exactly which initiatives to launch, in what order and with what expected return. No more scattered Data initiatives without an overall vision.

02

Alignment between business and tech

Data strategy is no longer just an IT topic. Business leaders understand what data brings them and take an active part in decision-making.

03

A solid foundation for AI

There is no high-performing AI model without quality data. Data strategy lays the foundations on which your future AI projects will rest.

04

Lower costs

Duplicated, inconsistent or inaccessible data is costly in time, errors and missed opportunities. We eliminate these losses at the source.

05

10x

Decisions based on reliable data

No more debates over the accuracy of figures in executive committee meetings. Your data is consolidated, qualified and authoritative.

Chapters

Our expertise

The skills and know-how we bring to deliver results in this area of expertise.

Data maturity audit and assessment

Assessment of your data assets: quality, accessibility, existing governance and gaps to close.

Enterprise Data strategy definition

With the adoption of the AI Act and stricter regulatory requirements, governance is no longer optional. It enables organizations to limit risks, ensure compliance and turn this requirement into a competitive advantage.

Data Quality Management

We carry out an in-depth assessment of processes, data and business objectives in order to build a realistic, prioritized roadmap together.

Target Data architecture

Governance of data, models, risks, ethics and regulatory compliance, as well as performance management.

AI use case scoping

We use automated evaluations, monitoring dashboards and regular audits.

Support for business leadership teams

Yes. It lays the strategic groundwork before launching projects, which avoids 70% of common mistakes.

Tools / Partnerships

How we put this expertise into practice

We combine proven methodologies with market-leading technologies while remaining independent in our recommendations. Our choice of tools is driven by your context, not by commercial agreements.

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Amazon Web Services

Google Cloud

Azure

Apache Airflow

Power BI

Amazon Web Services

Google Cloud

Azure

Apache Airflow

Power BI

Amazon Web Services

Google Cloud

Azure

Apache Airflow

Project types

What we deliver

Operational deliverables, not recommendations. Here are the engagement formats we deploy in this area of expertise.

01

360° Data Assessment

A complete review of your data assets: source mapping, quality assessment, identification of quick wins and structural recommendations.

02

Data & AI Roadmap

A 12- to 24-month strategic plan linking priority Data initiatives with the identified AI use cases. Each initiative is budgeted and tied to KPIs.

03

Data Quality Program

Implementation of quality rules, control processes and monitoring tools to continuously guarantee the reliability of your critical data.

04

AI pre-project scoping

Feasibility study on a targeted AI use case: assessment of available data, choice of approach, estimated gains and delivery schedule.

Business Cases

They structured their Data strategy with us

We don't deliver POCs. We deliver systems that run, with a measurable impact on our clients' business.

See all our business cases

Supply Chain Optimization Application

A pharmaceutical distribution player had to rethink its entire supply chain: pharmacy assortment, inventory management and delivery channels. Operational research alone was no longer enough. We built the application that transformed its operations.

Social media trend detection

In a market where consumer behaviors change faster than decision cycles, a customer needed to anticipate trends instead of experiencing them. We built the platform that turns social media noise into actionable signals.

Yield of agricultural fields

The climate subsidiary of a French insurance leader needed to predict field yields across Germany to price a new drought insurance offer. Internal data was not enough. We built the predictive models that made the product marketable.

Customer needs analysis platform

A large French group needed to understand the current and future needs of its customers by aggregating a massive volume of consumer data. After a POC of 8 weeks, we industrialized a complete platform deployed on all French entities and is now being extended at the group level.

Skin scoring and analysis

A major player in luxury cosmetics had developed a skin scoring algorithm. Problem: no one could verify what the AI was based on to make its diagnosis. We built the visualization system that makes predictions transparent and deployable in stores and on mobile.

Beaconing detection

A large group's security team needed to identify beaconing signals, regular network traffic sent by potentially compromised machines to servers controlled by attackers. The volume of logs made manual detection impossible. We built the anomaly detection system capable of processing this data on a large scale.

FAQS

Your questions, our answers

All the answers to understand our approach, how we work and what you can expect from working with us.

What is a Data and AI strategy?

A Data and AI strategy is a framework that defines how your data becomes a reliable asset for decision-making and how AI fits into your business priorities. Diametral provides a complete assessment, a budgeted 12- to 24-month roadmap, quality programs and use case frameworks ranked by expected impact.

Why build a Data and AI roadmap?

A Data and AI roadmap prevents initiatives from being scattered and aligns business, IT and leadership teams around measurable results. Without a roadmap, AI projects multiply in silos, budgets dwindle and executive committees spend their time debating the reliability of figures instead of making decisions.

How long does it take to put a Data strategy in place?

A Diametral Data strategy is developed in 4 to 10 weeks, depending on the size of the group and its current maturity level. The deliverable includes a maturity assessment, a map of critical data, a costed roadmap and a governance plan ready to be presented to the executive committee.

How does Diametral ensure data reliability?

Diametral ensures data reliability through a continuous quality program: automated validation rules, anomaly monitoring, a centralized data catalog and governance of master data. This approach puts an end to recurring debates about the accuracy of figures in executive committees and secures every AI use case that relies on this data.

What is the difference between a Data strategy and an AI strategy?

A Data strategy organizes the collection, quality and accessibility of your data, while an AI strategy defines how that data creates value through predictive or generative models. Diametral addresses both in parallel, because an AI strategy without a Data strategy produces unreliable models, and a Data strategy without use cases remains purely theoretical.

Vue aérienne d'un marais avec de petits cours d'eau sinueux traversant des zones de végétation brune et des berges sableuses.

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Is your data ready for AI?

A 30-minute conversation with one of our experts to assess your Data maturity and identify the first actions.

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