Near Real-Time Retail Operations Reporting for Faster Execution and Expansion Decisions

Retail and consumer businesses don’t lose money because they lack dashboards—they lose money because critical decisions are made on stale, inconsistent, or incomplete operational data. We built a near real-time reporting layer that unified corporate and retail operations metrics—profitability by region and store, daily inventory and POS visibility, and delivery status tracking—so leadership could act daily with confidence and scale operations based on facts, not gut.

At a glance:

• Industry: Retail / Consumer brands
• Core problem: Decision-making was slowed by delayed, fragmented operational reporting
• What we delivered: Near real-time reporting across profitability, inventory, POS, and delivery operations
• Primary impact: Faster issue detection, improved operational execution, and data-backed expansion planning (e.g., warehouse footprint decisions)
• Core stack: OneLake (storage), Fabric Warehouse (analytics), Power BI (reporting)

The challenge: the business ran daily—but reporting ran weekly

Corporate and retail operations were generating huge volumes of signals (sales, inventory movement, fulfillment, delivery updates), but the reporting layer couldn’t keep pace. Teams were forced into manual reconciliations, different versions of the truth, and delayed insights—creating a consistent lag between “what happened” and “what the business did about it.”

What we set out to solve:

• Unify corporate and retail operational reporting into a single decision layer
• Provide profitability insights by region and store (not just revenue)
• Enable daily inventory and POS reporting to spot stock and sales anomalies early
• Track delivery status and fulfillment execution for operations follow-ups
• Standardize KPI definitions so every function reads the same numbers
• Make the reporting reliable enough to support expansion decisions (not just monitoring)

“If your operations run daily but your insights arrive weekly, you’re managing yesterday’s problems.”

What “good” looked like (success criteria)

We aligned on outcomes that a COO/Head of Ops would care about: daily visibility, trusted KPIs, and actionable exception handling. The system needed to work for leadership (macro performance) and store/ops managers (micro execution).

Success criteria:

• Daily cadence: Near real-time refresh for core operational KPIs
• Trust: One metric layer and consistent definitions across functions
• Actionability: Exception-first views (what’s off, where, why it matters)
• Coverage: Profitability + inventory + POS + delivery execution in one workflow
• Scalability: Able to scale across stores/regions and add new sources without rework

Solution overview

We implemented an “operations control tower” reporting system: data lands into OneLake, curated and modeled into an analytics layer (Fabric Warehouse), and served to business users via Power BI dashboards built around daily operational decisions. The emphasis wasn’t visuals—it was governed KPIs, near real-time refresh, and exception-driven workflows that teams could act on.

1. Data foundation for near real-time reporting (OneLake)

We established a unified landing zone in OneLake so operational data could be standardized and refreshed frequently. This removed “multiple sources, multiple truths” and created a foundation where operational and financial metrics could be reconciled consistently.

2. Analytics warehouse built for operations + finance alignment (Fabric Warehouse)

We modeled the data into an analytics layer that supported both operational drilldowns and executive-level rollups. This included profitability views by region/store, daily inventory snapshots and movement signals, POS performance, and delivery status tracking—built in a way that stays performant as volumes scale.

3. KPI governance + metric consistency (the part that makes BI “decision-grade”)

Instead of letting every dashboard define KPIs differently, we established a governed metric layer: clear definitions, dimensional consistency (store/region/time/product), and validation checks. This reduced stakeholder debates and made reporting dependable enough to drive operational actions.

4. Operations dashboards in Power BI (control tower + drilldowns)

We delivered dashboards designed around daily decisions:

• Leadership control tower (macro trends + exceptions)
• Region/store drilldowns (local performance and issues)
• Inventory health views (stock risk and movement)
• Delivery and fulfillment status (execution bottlenecks and delays)

This made the reporting layer practical for daily ops—not just end-of-month reviews.

Implementation playbook

We delivered this in a phased approach to reduce disruption: lock KPI definitions first, establish the near real-time data flow, build the warehouse model, then roll out dashboards with validation and adoption loops.

• Phase 1: Discovery + KPI alignment — define “profitability,” inventory signals, POS metrics, delivery states, and owners
• Phase 2: Data foundation — OneLake ingestion + refresh cadence + quality checks
• Phase 3: Warehouse modeling — Fabric Warehouse schema + governed metric layer
• Phase 4: Reporting + adoption — Power BI dashboards, exception workflows, validation, and training
• Phase 5: Ongoing support and enhancements

Impact

• Faster operational decisions due to near real-time visibility instead of delayed reporting
• Better profitability management through region/store-level transparency
• Improved inventory execution with daily stock and POS signals (fewer surprises)
• Stronger delivery oversight via clear execution and status tracking
• Data-backed expansion planning (reporting credible enough to guide footprint decisions)

Technology stack

• OneLake — unified storage and landing zone
• Microsoft Fabric Warehouse — analytics layer for modeling and performance
• Power BI — control tower dashboards and drilldowns