Strategy
Make your data a structural competitive advantage.
Organizations that dominate their market don't suffer from their data, they control it. Together with you, we design the AI & Data strategy that transforms your information assets into a lever for decision, performance and sustainable differentiation.
+150 companies supported
— 5/5 Google reviews
Benefits/Impacts
What it brings
Concrete results for your organization, measurable from the first weeks of intervention.
01
A clear and prioritized Native AI roadmap
You know exactly which projects to launch, in what order and with what expected feedback. No more scattered AI & Data initiatives without an overview.
02
An alignment between jobs and tech
There are no longer siloed strategies, but a single common strategy, defined to meet business challenges thanks to adapted and efficient AI & Data engines.
03
A solid foundation for AI
No successful Native AI organization without a clear vision supported by a solid architecture and integrated foundations. The strategy sets out the organizational, cultural, and technical prerequisites for success.
04
A reduction in costs
Duplicate, inconsistent, or inaccessible data is costly in terms of time, errors, and missed opportunities. We eliminate these losses at the source.
05
10x
Decisions based on reliable data
No more debates on the veracity of the figures in the management committee. Your data is consolidated, qualified, and authoritative.
Chapters
Our expertise
The skills and know-how that we mobilize to deliver results on this expertise.
Tools/Partnership
How do we implement this expertise
We combine proven methodologies with market-leading technologies, remaining independent in our recommendations. Our choice of tools is dictated by your context, not by trade agreements.
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
Type of projects
What we deliver
Operational deliverables, not recommendations. Here are the mission formats that we deploy on this expertise.
01
Diagnostic Data 360°
A complete overview of your data assets: source mapping, quality assessment, identification of quick wins and structuring recommendations.
02
Roadmap Data & AI
A strategic plan over 12 to 24 months, combining priority Data projects with identified AI use cases. Each initiative is budgeted and associated with KPIs.
03
Data Quality Program
Implementation of quality rules, control processes and monitoring tools to guarantee the reliability of your critical data on an ongoing basis.
04
AI pre-project framework
Feasibility study on a targeted AI use case: evaluation of available data, choice of approach, estimation of gains and implementation schedule.
Business Cases
They structured their
Data strategy with us
We do not deliver POCs. We deliver systems that work, with a measurable impact on the business of our customers.
Your questions, our answers
All the answers to understand our approach, how we work and what you can expect from our collaboration.
What is a Data and AI strategy?
A Data and AI strategy is a framework that defines how your data becomes a reliable decision-making asset and how AI fits into your business priorities. Diametral delivers a 360° diagnosis, a 12 to 24 month budgeted roadmap, quality programs and use case frameworks arbitrated by expected impact.
Why build a Data & AI roadmap?
A Data & AI roadmap avoids the dispersion of initiatives and aligns business, IT and management teams with measurable results. Without a roadmap, AI projects multiply in silos, budgets erode, and executive committees spend their time debating the reliability of figures instead of deciding.
How long does a data strategy take?
A Data Diamétral strategy is built in 4 to 10 weeks depending on the size of the group and the existing maturity. The deliverable includes a maturity diagnosis, a mapping of critical data, a numerical roadmap and a governance plan ready to be presented to the executive committee.
How does Diametral make data reliable?
Diametral makes data reliable through a continuous quality program: automated validation rules, anomaly monitoring, centralized data catalog and governance of master repositories. This approach eliminates the recurring debates about the accuracy of COMEX figures and secures each AI use case built on this data.
What is the difference between Data Strategy and AI Strategy?
Data Strategy organizes the collection, quality, and access to your data, while AI Strategy defines how that data creates value through predictive or generative models. Diametral deals with both in parallel because an AI Strategy without Data Strategy produces unreliable models, and a Data Strategy without a use case remains theoretical.
contact
Is your data ready for AI?
A 30-minute exchange with one of our experts to assess your Data maturity and identify the first actions.