How Cepheus helped a century-old mining and metals company retire complex, undocumented SAS workflows — with zero loss of accuracy — through automated reconciliation and expert-led code translation.
The Client
A global mining and metals company with tens of thousands of employees worldwide and operations spanning multiple decades of analytics history. Like many century-old industrial enterprises, its core forecasting and impact-analysis workflows were built on SAS — some over 30 years old, with the original architects no longer available to explain the logic.
The Challenge
- Aging, undocumented complexity. Statistical workflows like Before/After Control Impact analysis and survival forecasting had been layered over decades, with no institutional memory of the original design decisions.
- Rising cost, shrinking flexibility. Continued SAS licensing was expensive and limited the company's ability to modernize its broader data stack.
- A scale problem, not just a technical one. Over 1,000 critical tables, some exceeding 4 million rows, needed to be verified — well beyond what manual review or spreadsheet-based checks could handle.
- Midway through the initiative, the company made a company-wide decision to pivot its target platform — a shift that would have derailed most migration vendors.
The Cepheus Approach
Cepheus didn't just translate code — we rebuilt trust in the outputs.
- Automated reconciliation to verify tables, macro variables, and logs at scale
- Expert-led conversion for the most complex legacy constructs, with full human oversight
- Structured mapping of cross-workflow dependencies to resolve hidden execution-order issues
- Query re-engineering to eliminate join and structural issues introduced by the platform shift
- Training and enablement so internal technical teams could fully own the new PySpark and Azure Databricks workflows going forward, not just inherit them
Solving What Others Couldn't
Major categories of failure that typically stall — or quietly break — SAS migrations:
- Floating-point date-time joins from decades-old SAS logic (seconds since epoch)
- Loss of PROC SQL's automatic crossjoin protection when moving to PySpark SQL
- Nested SQL subqueries failing to execute correctly in the Azure Synapse POC environment
- Massive PROC Summary models (200+ columns, custom GLMs)
- Dynamic date-time logic causing outputs to shift with every run
- Tables updated mid-workflow and reused downstream, with no mapped dependencies
- Large workflows (10,000+ lines) silently producing "correct-looking" outputs from empty datasets
- Tables exceeding 4 million rows — beyond the limits of Excel-based verification
- Repeated macro definitions with unclear versioning across workflows
Beyond these nine, Cepheus resolved every complexity across the entire workflow estate — with 100% reconciled outputs.
Mid-Project Pivot
Partway through, the client made a company-wide decision to abandon its original target platform in favor of Azure Databricks. Rather than a setback, this became a proof point: Cepheus's methodology was portable enough to re-target the migration without losing prior work or timeline integrity.
The Impact
- 30% reduction in operational costs from retired licensing
- 50% faster migration cycles through automation and minimized UAT
- 100% auditability with repeatable, automated output verification
- Stronger internal adoption of the new workflows by technical teams
- A future-ready analytics architecture, free of legacy constraints
Why Cepheus
- True code translation — not a rewrite from scratch
- Deep expertise in resolving cross-workflow execution dependencies
- Construct-level complexity scoping for accurate estimation upfront
- Commitment to full-estate migration with minimal disruption to business-as-usual
- Stack flexibility beyond this engagement — the same methodology extends to SAS-to-R/Python migrations, relevant for regulated industries such as Pharma and Life Sciences where R remains the standard for statistical validation
Still Running Critical Decisions on Legacy SAS?
Whether your target stack is Databricks, PySpark, or R/Python, if your workflows have outlived the people who built them, we can help you migrate with confidence — avoiding guesswork.
Talk to Cepheus