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Client engagement · anonymised

A Unified Data and Feature Foundation for an Australian Retailer

Data and feature management for an Australian retailer: Databricks, Delta Lake and Power BI cut reporting time by 80% and created one source of truth.

Industry
Retail
Region
Australia
Focus
Data & feature management
80%
Less time spent producing reports
10 weeks
From data audit to live reporting

Client engagement — the client is anonymised at their request; figures are as measured on the engagement.

Challenge

An Australian retailer of 50–150 staff had its business data fragmented across spreadsheets and operational systems. Reporting was manual, the same data existed in several conflicting copies, and there was no governance over the features the business wanted to use for analytics and machine learning — so every report started with reconciling numbers.

Solution

The approach: one governed data foundation on Azure, with a unified data model on Databricks and Delta Lake, automated pipelines feeding it, a feature store design for consistent analytics and machine-learning features, and a Power BI reporting layer on top — so the business works from a single, trusted version of its numbers.

Approach

  1. 1

    We audited the data landscape: every spreadsheet, system and report, where the copies disagreed and which numbers the business relied on.

  2. 2

    We designed a unified data model on Databricks with Delta Lake, with one definition for each core business entity and metric.

  3. 3

    We implemented ETL pipelines that load and clean data from operational systems into Azure Data Lake, replacing manual extracts.

  4. 4

    We designed a feature store so analytics and future machine-learning models use the same governed feature definitions.

  5. 5

    We built the dashboard and reporting layer in Power BI, on top of the unified model, with SQL views the team can extend.

Frequently asked questions

Why a feature store for a retailer that is not yet running many models?

It gives analytics and future models the same governed definitions from the start, so features do not have to be rebuilt, and argued over, when machine learning arrives.

What happened to the existing spreadsheets?

The data they held now flows through the pipelines into the unified model. Reports read from that model, so the duplicate copies are no longer needed for reporting.

Can the team extend the reports themselves?

Yes. Reports sit on SQL views over the unified model, so the team can add measures and dashboards in Power BI without touching the pipelines.

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