HK DATACREST

Retail

Turning a weekly copy-and-paste report into a daily view per store.

For a retailer with 34 stores and two online marketplaces, sales come from the ERP and store tills, stock from a warehouse system, and targets from a finance spreadsheet, and the weekly review pack takes one analyst most of Monday to build.

Industry
Retail
Solutions
Put all your numbers in one place · Spend less and run better on AWS
Engagement
Pilot, then build and run

The challenge

By the time the report is ready, it is already describing last week.

Store managers see their numbers a week late, and the numbers do not always agree: marketplace orders and returns are counted differently in different reports. Stock-outs on fast movers are noticed by customers before anyone in head office.

The approach

How it works.

  1. Trace every number

    Each figure in the current pack is traced back to its source, and its owner agrees one definition.

  2. Build the pipelines

    ERP, POS, warehouse, marketplace and returns data are loaded on a schedule into one warehouse, with checks on every load.

  3. Model the business

    Revenue, margin and stock cover are modelled per store, region and SKU, with last year and target alongside.

  4. Ship the views

    A weekly review dashboard, daily store views and a scheduled email replace the manual pack.

The solution

What gets built.

The solution is a set of scheduled data pipelines feeding a warehouse and an operations dashboard: revenue against target and last year, region and store performance, top-moving SKUs with stock cover, and a data-source panel that shows every check before anyone reads a number.

Weekly operations dashboard: revenue, orders, margin and stock cover, a revenue trend against target, revenue by region, top-moving products and data source checks.

Outcomes

What it delivers.

  • A review pack that builds itself ready before the Monday meeting.
  • One definition per number so stores, finance and marketplaces see the same figure.
  • Stock risk flagged daily so fast movers are reordered before they run out.
  • Bad data caught at the door by checks that run before every refresh.

Technology

Typical stack for this.

  • Snowflake
  • dbt
  • Python
  • AWS Lambda
  • Amazon S3
  • Dashboards
  • or whatever you already run

Related solutions

All use cases

Put all your numbers in one place

Pipelines, data checks and dashboards.

Spend less and run better on AWS

Setup, migration, cost reviews and serverless pipelines.

Start with a conversation

Tell us what's slowing your operation down.

A 30-minute call with the people who would build it. You leave knowing whether a pilot makes sense, and what it would cover.