Strategy
Ensure the reliability of your data to make risk-free decisions.
Your data exists. But is it reliable, accessible, and organized in a way that supports your strategic decisions? We develop data and AI strategies that transform your data into a controlled decision-making asset.
+150 companies supported — 4.9/5 Google Reviews
Benefits / Impacts
What This Delivers
Concrete results for your organization, measurable within the first few weeks of engagement.
01
A Clear and Prioritized Data Roadmap
You know exactly which initiatives to launch, in what order, and with what expected return. Gone are the days of scattered data initiatives without a clear overall vision.
02
Alignment Between Business and Tech
Data strategy is no longer solely an IT concern. Business units understand the value data brings and actively participate in strategic decisions.
03
A Solid Foundation for AI
No high-performing AI model can exist without quality data. Data strategy lays the foundation for your future AI projects.
04
Cost Reduction
Duplicated, inconsistent, or inaccessible data costs you time, leads to errors, and results in missed opportunities. We eliminate these losses at the source.
05
10x
Decisions Based on Reliable Data
No more debates about the accuracy of figures in management meetings. Your data is consolidated, qualified, and authoritative.
Chapters
Our Expertise
The skills and expertise we leverage to deliver results in this area.
Tools / Partnerships
How we apply this expertise
We combine proven methodologies with market-leading technologies, while remaining independent in our recommendations. Our choice of tools is dictated by your context, not by commercial 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
Project types
What we deliver
Actionable deliverables, not just recommendations. Here are the types of engagements we offer for this expertise.
01
360° Data Diagnosis
A comprehensive assessment of your data assets: source mapping, quality evaluation, identification of quick wins, and strategic recommendations.
02
Data & AI Roadmap
A 12 to 24-month strategic plan, connecting priority Data initiatives 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 continuously ensure the reliability of your critical data.
04
AI Pre-Project Scoping
Feasibility study for a targeted AI use case: assessment of available data, approach selection, estimated benefits, and implementation planning.
Business Cases
They structured their Data strategy with us
We don't just deliver POCs. We deliver operational systems with a measurable impact on our clients' business.
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 develop a Data and AI roadmap?
A data and AI roadmap helps avoid scattered initiatives and aligns business, IT, and leadership teams on measurable results. Without a roadmap, AI projects proliferate 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 implement a data strategy?
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 ensure data reliability?
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 a data strategy and an AI strategy?
Data strategy organizes the collection, quality, and access to your data, while AI strategy defines how this data creates value through predictive or generative models. Diametral addresses these two aspects in parallel, because an AI strategy without a data strategy produces unreliable models, and a data strategy without use cases remains purely theoretical.
contact
Is your data ready for AI?
A 30-minute discussion with one of our experts to assess your Data maturity and identify initial actions.