DataDive builds the governed data model behind retail reporting — POS, ecommerce, inventory, supplier and finance data brought into one place, so store, merchandising and finance teams stop arguing about whose number is right and start working from the same one.
Each pillar can be built as its own phase — most retail engagements start with the first one and expand from there.
Retail performance reporting.
A single governed view of sales, stock and margin across stores, channels and SKUs — built once in the warehouse, not recalculated differently in every spreadsheet and system export.
Inventory & stock analytics.
Stock-on-hand, stock-in-transit and reorder signals pulled from POS, ecommerce and warehouse systems into one current view, so stockouts and overstock get caught before they cost margin.
Merchandise & category analytics.
Category, range and SKU-level performance — sell-through, markdown impact, category margin — without a manual pull from the merchandising system every reporting cycle.
Customer & ecommerce analytics.
Store and ecommerce transactions, loyalty data and marketing platforms brought into one customer view, instead of two teams reporting two different pictures of the same customer.
Finance & operations reporting.
POS, ERP and accounting data reconciled in the pipeline before reporting, not by finance manually matching numbers across three systems every month-end.
AI-ready retail reporting.
Sales, stock and customer data modelled and governed cleanly enough that forecasting and AI tools can actually use it — the same foundation the dashboards run on, not a separate export.
We work with what you already run — the usual retail data sources feeding one governed model.
The delivery, not just the pitch.
BigQuery or Snowflake warehouse structured around your stores, channels and product hierarchy.
Sales, stock and margin definitions built once in dbt, tested, and reused everywhere instead of redefined per dashboard.
Executive, merchandising, inventory and finance views built on the same governed model.
Scheduled pipelines, not a manual export-and-upload process someone has to remember to run.
Reconciliation and freshness tests built into the pipeline, so mismatches get caught before a dashboard ships, not after.
Clear documentation of the model and metrics, handed to your team — or supported by us on an ongoing basis if you'd rather not own it.
Illustrative of how the six pillars typically get grouped into dashboards — the actual pack for your business is built around your data, not a fixed template.
Sales, margin and trend versus target, by store and channel, for leadership.
Stock cover, aged stock, and reorder signals across stores and warehouses.
Category and SKU-level sell-through, markdown impact, and category margin.
Customer acquisition, retention and channel mix across store and online.
Reconciled sales-to-finance reporting and operational cost tracking.
Years in data & BI
Salesforce AppExchange
consulting partner
AWS partner,
Google Cloud experience
BigQuery, Snowflake,
dbt, Tableau capability
[NK — replace with the real, anonymized detail: what retail business (size/type, no name), what was broken (e.g. sales/stock/margin mismatch, late inventory reporting), what we built, and what changed. Keep it specific and honest rather than a generic "leading retailer" claim — that's the whole point of this page.]
Every retail engagement starts with the same Data Health Check methodology used across DataDive's other work — a short, practical review before any build commitment.
Start with a Retail Data Health Check — a short, practical review, not a sales pitch.
Book a Retail Data Health Check