Case Study Lakehouse Architecture

Analysis should not start
with correcting data.

When each system generates data with its own logic, teams waste time cleaning, validating and correcting information before they can analyze the business.

Industry
Retail and ecommerce

-65%
Data processing time

↓ -65%
Data processing time.

↑ Quality
and consistency of information.

↑ +10 M
Integrated daily logs.

↑ 3x
Agility in delivering insights to business.

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BEFORE

Data duplicated and spread across ERP, CRM, eCommerce, IoT and inventory.

Strategic reports take days to generate.

Teams spending more time cleaning and validating data than analyzing it.

NOW

Data organized in Bronze, Silver and Gold layers.

Automated cleaning, normalization and enrichment.

Reliable information ready for reporting, advanced analysis and 360º vision.

THE PROBLEM

The data are not ready to make decisions.

Each system generates information with different structures and rules.
Duplicated, fragmented and untraceable information.
Teams have to review, clean and validate data before analyzing it.
Management does not have a single, reliable view of the business.

THE SOLUTION

Development of Lakehouse Architecture with Medallion Model.

Bronze: centralization of raw data from ERP, CRM, eCommerce, IoT and inventory.
Silver: cleansing, normalization and enrichment through automated processes.
Gold: creation of business models, metrics and KPIs ready for Power BI.
Data Quality & Governance: rules to ensure consistency, integrity and traceability.

THE IMPACT

Less time preparing data. More time using reliable information.

The organization goes from redoing validations for each analysis to working on a common architecture that integrates, cleans and prepares the data from the source.

This allows for accelerated reporting, advanced analysis and 360º vision without duplicating processes.