SAS to R Migration & Business Validation
Audit, conversion, non-regression testing and industrialisation for organisations modernising legacy analytical workflows.
A successful SAS-to-R migration is not a line-by-line translation exercise.
Legacy SAS code often contains years of business rules, exceptions and hidden dependencies. The priority is to preserve validated business outcomes while improving the architecture, maintainability and economics of the target system.
A defined scope. Tangible deliverables.
SAS estate inventory and dependency mapping
Migration prioritisation and roadmap
Conversion or redesign of SAS logic in R
Non-regression test framework
R packaging, documentation and deployment
Team training and migration handover
Make the technical decisions explicit.
- Map business outputs and dependencies first
- Prioritise migration waves by value and risk
- Avoid reproducing unnecessary legacy complexity
- Compare SAS and R outputs systematically
- Industrialise the target R environment as part of the migration
Outcomes to define and assess together
- Preserved business logic with stronger maintainability
- Reduced dependency on legacy SAS workflows
- A modern R foundation your team can own
A few useful starting points.
Can you work on an existing project?
Yes. We begin by reviewing its purpose, dependencies, documentation and current constraints. This assessment determines what can be retained and what needs to change.
What should we prepare for the first discussion?
The expected outcome, current tools, main difficulties and any deadline. An architecture sketch or a description of the workflow is enough to start; sensitive data is not needed at this stage.
How are scope, timing and price set?
After reviewing the context, we define the intervention, deliverables, dependencies and acceptance criteria. Timing and pricing follow from this scope and are confirmed in the proposal.
Migrating SAS reporting in controlled batches
A recurring report combines imports, joins, calculations and exports. Use a pilot to compare date conventions, missing values and aggregations. Explain differences and obtain validation before expanding the scope.
R workflows, a reference dataset and a SAS/R differences register.
Illustrative scenario, not a claim about a completed client engagement. Final deliverables depend on the agreed scope.
A useful first conversation.
Which programmes have priority? Are validated outputs available? What tolerances are acceptable?
Share a description of the workflow and its constraints first. Confidential files, personal data and system access can be discussed within an appropriate framework later.