Enterprise Data
Transformation

Yes — that's the core of the framework. Someone learning to read a dashboard and question a number is on a different path from an engineer building pipelines, and each path tunes further per business unit so people learn only what fits their work.

Every participant is assessed before and after for a real uplift score, not a completion tick. Capstones run against your own data and a genuine business question, so leaders can see the analysis land and justify the spend.

Orbit is the platform we run the programme on — paths, cohorts, assessments and progress in one place — sitting alongside your current systems and giving L&D and leadership a live view. Direct LMS/HRIS integration is on the roadmap.

Yes. Delivery is virtual, hybrid or in person, with cohorts scheduled across cities and time zones. Distributed data teams see one connected programme, tracked centrally.

Yes. Alongside our practitioner faculty, your own data leaders and SMEs can be onboarded to deliver sessions — so your platforms, tooling and data context show up in the learning.

Leaders get a live view of progress, completion and uplift by path, cohort and business unit through Orbit — so you can see which teams have become genuinely data-driven, without chasing reports.

A structured path that builds data skills across every role, not just the data team. Business users learn to read and question data, analysts and engineers learn to build with it, and leaders learn to govern it — each on a path that fits their work. The aim is a data-driven organisation, not a stack of dashboards nobody trusts.

It teaches non-specialists to find, read and act on data without waiting on an analyst. Not SQL for everyone — just enough fluency to ask the right question, challenge a number, and decide with evidence instead of instinct.

Role by role. A finance lead and a data engineer need very different things, so a blanket course wastes both. We assess where each team is, teach against their real data, and measure the lift — so literacy sticks instead of fading after the session.

Enough to be useful without a specialist: reading a dashboard critically, spotting a misleading chart, framing a question data can answer, and knowing when to pull in the data team. Deep modelling stays with the specialists — everyone else needs confident fluency.