215
Employees worldwide
56+
years in operation
50 B+
Transactional rows
A leading US sports retailer was running its entire data operation on SAS: nightly batch jobs, manual ad-hoc extracts, and siloed sales, stock, and promotional data with no unified view across the business. Pricing decisions were made without visibility into how discounts affected demand or margin. Through its Data and Cloud Modernization Services and Solutions, Inferenz migrated 50 billion transaction rows from SAS to Snowflake, built a unified reporting layer syncing Hyperion to Tableau, and delivered a price elasticity modelling engine, all in 100 days. Dashboards now refresh hourly, manual data pulls are near-eliminated, and pricing teams have elasticity signals before campaigns launch.
Leadership needed a single source of truth and faster access to pricing intelligence. The existing SAS infrastructure was creating compounding bottlenecks across reporting, analytics, and commercial decision-making.
Sales, stock, and promotional files lived in separate systems with no shared data model. Every report required manually pulling and reconciling data across sources — a process that was slow, error-prone, and entirely dependent on individual analyst effort.
Teams queued SAS jobs overnight and still relied on third-party extracts for any ad-hoc question. There was no way to answer an unplanned business question without waiting for the next batch cycle or filing a request to the data team.
The business had no analytical view of how discounts and promotional pricing affected demand or margin. Pricing decisions were made on instinct and historical precedent — with no model to test scenarios or quantify the trade-off between volume and profitability before a campaign went live.
Fifty billion transaction rows sat in a SAS environment that was not built for the query volumes or analyst self-service the business now needed. Scaling the infrastructure further meant more cost, more batch dependency, and no path to real-time analytics.
Inferenz built a cloud-native data warehouse and price elasticity engine from the ground up, migrating 50 billion SAS transaction rows into Snowflake and delivering a production-ready analytics platform in 100 days.
Python and Apache Airflow pipelines replaced the SAS batch architecture, landing the full transaction history into Snowflake and enabling near-real-time query access for the first time. The ingestion framework was built for reuse while new data sets plug into the same pipeline without code changes, removing the custom-build overhead that had previously made every new integration a standalone project.
Hyperion financial feeds were connected directly to Tableau, creating a single governed reporting layer where every team reviews the same numbers from the same source. The manual reconciliation process that had previously preceded every leadership review was eliminated.
A price elasticity modelling engine was built on Python to give the commercial team analytical visibility into pricing decisions before they are made. The models simulate the impact of price moves across product categories, quantify margin risk, and surface the discount thresholds at which demand uplift no longer justifies the margin cost — giving pricing teams evidence-based guidance ahead of every campaign launch.
The migration was structured to deliver working capability in phases throughout the 100-day timeline rather than as a single go-live event, ensuring the business had access to improving data throughout the engagement rather than waiting for a full cutover.
Sales & revenue dashboards
Hourly Snowflake loads replaced overnight SAS batches, giving commercial and finance teams current data at the start of every working day.
Manual data pulls
A single Snowflake view eliminated the ad-hoc extract process that had previously required analyst effort for every unplanned business question.
Promo-margin accuracy
Price elasticity models surface demand and margin trade-offs before campaigns launch, replacing instinct-led pricing with evidence-based decisions.
From SAS to production
50 billion transaction rows migrated and a full analytics platform delivered; warehouse, reporting layer, and pricing engine, within the engagement timeline.
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