Why Retail Analytics Backlogs Are Costing Your Business Real Margin

Retail analytics decisions: markdown timing, campaign activation, inventory replenishment. They are supposed to be data-driven. But at most retailers, the data isn't ready when the decision needs to be made.

Your merchandising team sends a request to data engineering on Monday. The report comes back Thursday. By then, the sell-through window has shifted, the promotion has already launched, and the markdown decision gets made on intuition anyway. This isn't a data engineering problem. It's a structural one.

The gap between when retail leaders need data and when they actually get it is costing margin, conversion, and customer loyalty. It just doesn't always show up in any report, because the reporting is what's broken.

Why Retail Data Is So Hard to Unify

Retailers don't have a shortage of data. They have a surplus of siloed data with no fast path to insights.

POS transactions, loyalty program events, e-commerce clickstream, CRM records, paid media signals, WMS inventory feeds: each lives in a different system, often maintained by a different team, often with a different definition of "customer" or "SKU." The job of unifying them falls on data engineering, which means it competes with every other priority on the pipeline backlog.

The result: segmentation runs take 24 to 72 hours. Inventory visibility lags by half a day. Demand forecasts are built on last week's data. And when a business leader wants to ask a new question, say "What's our repeat purchase rate among loyalty members who bought in the last 30 days?", the answer takes a week to produce. For a merchandising team, that week of delay has a direct cost: markdown decisions made on stale sell-through data, re-engagement campaigns launched after the churn window has already closed, replenishment orders placed against inventory counts from yesterday.

External pressure is making this worse. Third-party cookies are largely gone, forcing retailers to activate first-party data at scale. But most first-party data is distributed across systems that were never designed to work together, without a unified, governed access layer across any of them.

What Retail Business Leaders Actually Need From Their Data Stack

Retailers who are closing this gap share a common pattern. They've stopped trying to centralize all their data in a single warehouse and started building a unified query layer that reaches data where it already lives.

The capability looks like this: a merchandising analyst can query across POS history, loyalty events, and promotional calendars in a single SQL query, without waiting on data engineering to build a pipeline first. An e-commerce team can access real-time behavioral data alongside inventory levels and feed it directly into a recommendation engine. Marketing can build a first-party audience segment, apply consent controls, and activate it in hours instead of days.

The underlying requirement isn't another data warehouse. It's a single query layer where analysts can access data directly, with the right governance built in, without copying data or creating new compliance exposure. Because Dremio is built on open standards like Apache Iceberg, there's no lock-in and no mandatory migration. Your existing cloud storage stays intact. Most retail teams are querying live data within days of connecting their first source.

This is what Dremio's Agentic Lakehouse makes possible for retail teams: a single query layer across POS, e-commerce, loyalty, CRM, and supply chain data, without pipelines, without data copies, and without waiting.

How Retail Analytics Teams Break Free From the Weekly Reporting Cycle

The merchandising analytics use case is a concrete example that most retail business leaders recognize immediately.

A large specialty retailer's planning team was running weekly markdown and assortment reviews. The data for each session took three to four days to prepare: someone in data engineering pulled together sell-through data from POS, stock levels from the WMS, promotional lift from the CRM, and external competitive signals, then loaded it into a spreadsheet environment for the planning team to work with. The actual meeting lasted two hours. The data prep took most of the week.

With Dremio, merchandising analysts query directly across POS, WMS, and promotional data without copies, without pipelines, and without submitting tickets. The AI Semantic Layer means analysts can ask in the language they already use: "show me Q4 repeat buyers by category" or "which SKUs are trending toward stockout this week," without knowing which table that data lives in or filing a ticket to find out. Autonomous Reflections handle query acceleration automatically, delivering sub-second response times on datasets with hundreds of millions of records without requiring ongoing engineering effort to maintain.

The shift isn't just about speed. When planners have access to fresh, always-current data, the nature of the questions they ask changes. They stop reviewing last week's performance and start identifying in-week signals: which SKUs are trending toward stockout, which promotions are underdelivering against margin targets, which categories are absorbing unexpected return volume. The weekly reporting session becomes a real decision session.

The same pattern extends across your organization. Once your merchandising team has access to live data, marketing can tap the same unified layer for attribution modeling and supply chain gets real-time inventory visibility without separate integration projects for each team.

How Germany's Largest Retailer Cut Analytics Costs by 40%

Schwarz Group, the parent company of Lidl and Kaufland and Germany's largest retailer, operates across 32 countries and processes several petabytes of daily data. Their analytics infrastructure had hit a wall: ingestion costs on their previous cloud warehouse were growing faster than the value coming out of it, and the data duplication required to run analytics was slowing everything down.

After moving to Dremio, Schwarz Group reduced cloud analytics costs by 40% and used that unified data foundation to activate more than 100,000 AI and analytics models across their retail and supply chain operations. The infrastructure cost reduction funded the capability expansion, a pattern that repeats across retail organizations that move to an open, federation-first architecture.

Retail leaders aren't waiting on technology to catch up. The data you need to make better markdown, segmentation, and inventory decisions already exists in your systems. The question is whether your data stack makes it accessible when you need it, or a week later. If your teams are spending more time preparing data than acting on it, the architecture is working against you.

See how retail teams are using Dremio. Explore the retail solutions page or book a 30-minute demo to see how this works on a merchandising analytics use case.

No ETL required. No data copies. No lock-in.