Enterprise Platforms × Retail & E-Commerce
Power BI semantic models, merchandising dashboards, and inventory analytics for retail and consumer brands: governed datasets that handle the cardinality of SKU × store × day, DAX-optimized for the report patterns merchants actually use, and the lineage frame that survives the next ERP cutover.
The problem
Retail has the worst-shaped data in BI. SKU counts in the millions, store counts in the thousands, and time at daily resolution: the cross-product alone is enough to destroy naïve report performance. The pattern that fails retail Power BI estates: dashboards built without semantic-model discipline, ad-hoc DAX written by analysts who haven't seen production cardinality, and reports that take 90 seconds to load on a normal Tuesday.
We design retail Power BI estates around the cardinality reality: aggregation tables with explicit grain, calculation groups for the standard retail metrics (units, sales $, AUR, sell-through), composite models that route between aggregate and detail, and the DAX patterns that handle SKU-level analysis without melting the semantic model. The result is reports that load in seconds on production-shape data.
Where it ships
Concrete applications where enterprise platforms unlocks measurable value inside retail & e-commerce delivery constraints.
+58%
merchant report performance
Sell-through, sell-in, AUR, gross margin, and inventory weeks-of-supply at SKU × store × week with daily refresh. The metrics merchants run their day on: fast, certified, lineage-traced.
Stock-on-hand, replenishment lag, in-transit visibility, and shrinkage analysis. Composite models route SKU-level questions to the warehouse and store-level rollups to aggregations.
Online vs. store sales, click-and-collect attribution, channel-conflict detection, and unified customer 360. Replaces 6+ team-built dashboards with one certified channel-performance model.
Promotion-cycle attribution, lift modeling, markdown cadence, and post-promo recovery. Designed for the merchant question 'did this promo actually pay back?' with honest answers.
Cohort retention, RFM segmentation, lifetime value, and channel-mix per segment. Privacy-bounded; OLS hides PII for analysts who don't need it.
Power BI Embedded inside the operations app store managers actually use, surfacing the 5–7 metrics that drive daily decisions without forcing them into a separate BI tool.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion calibrated to retail & e-commerce delivery.
Two-week audit: data sources, SKU / store / customer cardinality, existing semantic models (if any), and refresh-cadence requirements. Output is the aggregation strategy and a prioritized consolidation plan.
Build the semantic model with explicit aggregation tables before anyone writes a measure. Star-schema discipline, calculation groups for retail standard metrics, composite-model boundary clearly drawn. RLS by store / region / role.
Reports get rebuilt against the certified model using the 7-pattern DAX playbook. Every measure documented; calculation groups handle variants; visuals tested at production cardinality.
Tenant governance frame: certified dataset policy, deployment pipelines, orphan-cleanup cadence. Quarterly retail-cycle review (peak readiness, post-peak retro, plan-cycle review).
Capabilities
Stack
Compliance overlay
Every retail & e-commerce engagement carries the evidence collection that procurement and audit teams expect on day one.
PAN tokenization at the source; aggregated tables only, no raw cardholder data in Power BI datasets. Audit logs retained per PCI requirements.
Customer 360 designed with explicit consent and retention rules. OLS hides direct identifiers; aggregation rules prevent re-identification. Right-to-erasure cascade through the semantic model.
Certified dataset policy, workspace structure tuned for retail org units (HQ, region, banner, store). Service principals for automated deployment; named owners on every certified asset.
Selected work
Where this fits
Common questions
Power BI wins where Microsoft is the system-of-record stack (D365, Office 365, Azure): the licensing is bundled and the integration is native. Tableau wins where visualization sophistication and self-service exploration are primary. Looker wins where the data team is already on dbt + warehouse-native semantics. For retail in a Microsoft estate, Power BI is the default.
Aggregation tables with explicit grain. Daily SKU × store × store-day in the detail layer; weekly SKU × region × week, monthly category × banner × month in the aggregation layer. Composite models route queries to the right grain transparently. Done right, this turns a 90-second merchandising report into a 5-second one.
Yes: Power BI mobile or embedded analytics inside the operations app. We design store-manager dashboards around 5–7 KPIs, not 25. The metrics are the ones that drive daily decisions: sales-vs-budget, conversion, transaction count, units-per-transaction, and the top three slow-moving SKUs.
Fabric works well for retail when the data architecture is being rebuilt anyway. Lakehouse handles the variety (POS streams, ERP transactions, customer events, marketplace feeds), and Direct Lake mode gets Power BI to query at speed without the import-refresh cycle. We recommend Fabric where the lakehouse-first architecture is the design intent, not as a wrapper around an existing warehouse.
We've integrated Power BI with D365, NetSuite, SAP, Shopify Plus, and BigCommerce. The pattern: warehouse / lakehouse owns the data integration, Power BI owns the semantic model. Direct connections to operational systems are reserved for low-volume operational dashboards, not the main analytics estate.
Cardinality + estate audit: 2 weeks, $40K–$80K. Single-domain semantic model build (e.g., merchandising): 3–5 months, $250K–$600K. Multi-domain consolidation (merchandising + inventory + customer): 6–10 months, $600K–$1.5M. Managed services post-launch: $15K–$60K monthly retainer.
Talk to us
A senior engineer plus the ATLAS department lead joins the first call, both with prior retail & e-commerce delivery experience.