Structuring the execution of data projects — from formalization to delivery — by integrating transformation levers and data acculturation.
Data transformation engagements can take many forms — from drafting RFPs to operational project management, through to supporting teams in building their skills and data literacy.
They typically revolve around one or more of the following themes:
- RFP drafting
- Data project management and delivery
- Team training and data literacy
Illustration / Use Case
RFP Drafting
An industrial company has just finalized its data roadmap, centered around the deployment of a data lake to harmonize practices and lay the groundwork for a “semantic” layer that will facilitate the future deployment of agents.
Before committing to and adding a new element to a project portfolio, it can be valuable to consult several integrators. This typically involves formalizing an RFP, at the heart of which is the technical specification document. We support you in drafting these materials so that the final decision is as informed as possible.
Data Project Management and Delivery
A company has strong data expertise but struggles to establish a global, cross-functional governance framework that ensures the continuity and consistency of data flows between different systems — with each department tending to define its own roadmap.
We support you — independently or in partnership with digital project management specialists — to help you master the interdependencies between projects and establish centralized governance. This enables smooth information flows, reliable usage, and the ability to scale data initiatives across the organization.
Team Training and Data Literacy
In a fast-moving data landscape, it is not always easy to find one’s bearings, and the temptation to chase the “next big thing” is real. Yet, as the saying goes, the grass is often not greener on the other side — it is greener where you water it.
We help you capitalize on your existing resources and upskill teams in various ways. This can take the form of co-building shared data workflows across teams (Data Engineering, Data Analytics, Data Science, etc.), introducing development best practices (versioning, documentation, etc.), or running deep dives on specific topics — whether statistical (underlying assumptions when using a class of models, result interpretation, etc.) or focused on industrializing practices.