Data Warehousing & Modeling
Cloud and on-premises data warehouses and dimensional models engineered for fast forecasting, S&OP, and executive reporting.
Life Sciences
Life science commercial and supply data spans patient cohorts, specialty pharmacies, wholesalers, payers, and gross-to-net contracts. We design cloud and on-premises warehouses that unify these sources into governed schemas built for patient-based forecasting and long-range planning.
What we factor in
- NDC, strength, and pack hierarchies
- Specialty pharmacy and 852/867 tables
- Anonymized patient-level data (APLD)
- Payer, formulary, and 340B dimensions
- Gross-to-net contract ledgers
- Cloud and on-premises deployment
Commercial & S&OP data marts
Unified patient, channel, and shipment schemas
We design dimensional star and snowflake schemas that link ex-factory shipments, 852/867 wholesaler movements, specialty pharmacy dispenses, and patient starts around shared product, time, and account dimensions.
What it gives you: Give brand, finance, and supply teams one governed warehouse to forecast and report from.
Cloud and on-premises deployments
SQL Server, Oracle, Snowflake, and OCI
Whether your security posture requires an on-premises Microsoft SQL Server or Oracle Database behind your firewall, or a cloud warehouse in Snowflake, AWS, Azure, or Oracle Cloud, we architect and tune the environment end to end.
What it gives you: Deploy your planning warehouse in the exact cloud or on-premises environment your IT team mandates.
Longitudinal patient data modeling
Cohort, persistency, and dosing tables
Raw claims and hub records are structured into longitudinal patient-month cohorts with pre-calculated persistency, compliance (PDC, MPR, DACON), and line-of-therapy attributes ready for econometric and survival modeling.
What it gives you: Cut patient-funnel model refresh times from days of ad-hoc SQL to minutes.
Gross-to-net data structures
Channel, payer, chargeback, and rebate marts
We model gross sales, wholesaler chargebacks, Medicaid and 340B utilization, commercial rebates, and co-pay assistance by NDC and channel so gross-to-net reconciliations trace cleanly to unit forecasts.
What it gives you: Connect unit demand forecasts directly to net revenue and accrual reporting.
Snapshot and version history
Slowly changing dimensions and forecast archives
Every S&OP cycle, assumption update, and hierarchy restatement is stored as an immutable versioned snapshot, allowing analysts to compare any historical plan against actuals even after territory or brand realignments.
What it gives you: Measure forecast accuracy and bias across cycles without losing historical context.
Query & batch performance tuning
Partitioning, indexing, and compute optimization
We optimize table partitioning, columnstore indexes, materialized views, and warehouse compute sizing so multi-year cohort queries and RoadMap GPS 360 rollups return in seconds while keeping cloud spend predictable.
What it gives you: Eliminate slow dashboard loads and overnight batch bottlenecks.
Consumer Goods & Retail
Consumer goods and retail planning requires fast rollups from individual store and SKU combinations up to brand, category, key account, and total company views across years of weekly and daily history.
What we factor in
- SKU × store × week granularity
- Product, brand, and pack hierarchies
- Customer and banner hierarchies
- POS, shipment, and inventory tables
- Promotional event and deal ledgers
- Hybrid SQL Server and cloud scale
Multi-tier hierarchy modeling
SKU-to-brand and ship-to-to-banner rollups
We model alternate and ragged hierarchies across products (UPC, SKU, pack, brand, category) and customers (store, DC, ship-to, retail banner, channel) so forecasts reconcile automatically at every level.
What it gives you: Let sales plan by account, marketing by brand, and supply by DC from the exact same database.
RoadMap GPS & SQL Server backends
High-performance relational & OLAP schemas
RoadMap GPS and GPS 360 run on Microsoft SQL Server to handle massive enterprise planning datasets. We design, index, and maintain the underlying SQL Server schemas, stored procedures, and OLAP views for sub-second slice-and-dice.
What it gives you: Scale spreadsheet-familiar planning to hundreds of thousands of series without file bloat.
POS and CPFR data warehousing
Store-level sell-through and inventory marts
Store-week point-of-sale data from NielsenIQ, Circana, and retail partners is warehoused alongside internal shipments, DC inventory, and open orders to power collaborative replenishment and demand sensing.
What it gives you: Query years of SKU-by-store velocity and on-hand balance without hitting row limits.
Trade promotion and pricing repositories
Event calendars, lift baselines, and deal costs
We structure historical promotion calendars, feature/display flags, everyday and promoted price points, and trade funding into clean analytical tables ready for elasticity and lift decomposition models.
What it gives you: Give revenue management a single historical ledger of every promotion and what it earned.
Legacy-to-cloud warehouse migration
On-premises SQL/Oracle to Snowflake or Azure
When transitioning from legacy on-premises databases or scattered Access/Excel marts to Snowflake, Azure Synapse, or AWS Redshift, we migrate schemas, rewrite stored procedures, and validate parallel outputs.
What it gives you: Modernize your data stack without disrupting active S&OP cycles or downstream reports.
Cost and workload governance
Storage tiering and warehouse sizing
For cloud warehouses, we separate ingestion, batch forecasting, and BI reporting workloads, configure auto-suspend and clustering keys, and eliminate redundant table scans that inflate monthly cloud bills.
What it gives you: Keep query latency low and cloud warehouse bills under control as data volume grows.
Cloud, on-premises, and hybrid warehouse architectures
Every organization has different data residency, latency, and infrastructure requirements. Some life science and defense teams require on-premises databases behind their own corporate firewall; others run entirely in cloud data warehouses or operate a hybrid model that pairs a cloud lakehouse with a dedicated Microsoft SQL Server planning mart for RoadMap GPS and RoadMap GPS 360. RoadMap designs, deploys, and maintains both.
| Deployment Model | Supported Platforms | Ideal Use Cases |
|---|---|---|
| On-Premises & Private Cloud | Microsoft SQL Server, Oracle Database (including OLAP DML), PostgreSQL | Enterprises requiring strict on-premises data sovereignty, local LAN performance for RoadMap GPS / GPS 360, or direct co-location with on-prem ERP systems |
| Public Cloud Warehouses | Snowflake, Oracle Cloud Infrastructure (Autonomous Data Warehouse), Azure SQL / Synapse, Amazon Redshift, Google BigQuery | Elastic compute scaling for multi-partner data ingestion, enterprise BI, and high-throughput batch forecasting via the Geneva Forecasting™ API |
| Hybrid Planning Architecture | Cloud warehouse (Snowflake / AWS / Azure) synced with a dedicated SQL Server or Oracle planning mart | Organizations centralizing enterprise data in the cloud while giving demand planners governed, low-latency desktop and browser workspaces |
Dimensional data models built specifically for forecasting
Generic enterprise data warehouses are often structured around transactional accounting ledgers rather than time-series continuity. When a product changes pack size, a customer banner reorganizes, or a week has zero orders, naive queries produce broken history series that ruin statistical forecasts.
Our data architects build forecast-ready dimensional models that handle the nuances of real-world planning:
- Continuous time spines: Explicit calendar tables covering daily, weekly, retail 4-4-5, and fiscal monthly periods so zero-demand weeks are preserved accurately for intermittent demand methods like Croston’s.
- Product supersession & phase-in/phase-out: Chaining predecessor and successor SKUs or NDCs so new package sizes inherit relevant historical seasonality and trend without double-counting during transition windows.
- Immutable forecast version snapshots: Storing every monthly or weekly statistical baseline, planner override, and consensus sign-off alongside actuals to power automated Forecast Value Added (FVA) and accuracy tracking.
Frequently asked questions
What is data warehousing & modeling?
Data warehousing and modeling structures enterprise data so it answers business and planning questions at scale. RoadMap Technologies designs, tunes, and migrates both on-premises databases (Microsoft SQL Server, Oracle Database) and cloud data warehouses (Snowflake, Oracle Cloud, Azure, AWS), building dimensional schemas and planning marts tailored for forecasting, S&OP, and OLAP reporting.
Do you support both on-premises SQL Server/Oracle and cloud warehouses like Snowflake?
Yes. We architect, tune, and maintain on-premises Microsoft SQL Server and Oracle databases, cloud warehouses including Snowflake, Oracle Cloud Infrastructure, Azure, and AWS Redshift, and hybrid deployments that sync a cloud warehouse with a dedicated SQL Server planning mart.
How is a forecasting data mart different from a standard corporate warehouse?
A forecasting data mart enforces continuous time spines (preserving zero-demand periods for intermittent demand models), links predecessor and successor SKUs across packaging transitions, reconciles ragged product and customer hierarchies, and stores immutable snapshots of every historical forecast cycle.
Can you migrate our legacy on-premises planning database to the cloud without breaking S&OP?
Yes. We migrate schemas, tables, and stored procedures in phases, run parallel validation to verify that every rollup and report matches to the penny, and cut over between planning cycles so active S&OP meetings are never disrupted.
How do you improve slow query and batch performance on large datasets?
We optimize table partitioning, columnstore and covering indexes, materialized OLAP views, and warehouse compute sizing, routinely reducing multi-hour batch loads and slow dashboard queries to seconds.
Tell us about your data
We route you to the right data engineer, scope the work, and build with security in mind from the first conversation.