![From Days to Minutes: Retail Leader Forus Unleashes Lightning-Fast Insights](/_next/image?url=%2F_posts%2F2024-06-24-forus-case-study%2Fthumb.jpg&w=1920&q=75)
From Days to Minutes: Retail Leader Forus Unleashes Lightning-Fast Insights
Case Study: Forus
![Marian Pachon](/_next/image?url=%2F_authors%2FMarian%20Pachon.jpg&w=256&q=75)
Posted by Marian Pachon
on June 24, 2024 · 3 mins read
About The Company
![study](/_next/image?url=%2F_posts%2F2024-06-24-forus-case-study%2Fstudy.png&w=1920&q=75)
Forus is a leading Chilean retail phygital powerhouse, boasting a remarkable 40-year history in the industry. With a presence spanning Latin America, including Chile, Peru, Colombia, and Uruguay, they specialize in a range of brands dedicated to footwear, apparel, and accessories, totaling an impressive 519 stores.
The company’s operations encompass the entire spectrum of production, importation, design, marketing, retail, and distribution, catering to both top-tier international and domestic brands.
Challenge
Forus continuously invests in enhancing its operations and is committed to integrating the latest technological best practices to deliver exceptional customer experiences. Gustavo Rivera Luna, Chief Information Officer at Forus, aimed to establish a stronger Modern Data Stack Platform (MDS) as a robust foundation for modernizing Forus' data strategy. Their goal was to centralize product information and operations while elevating the 360-degree customer-centric strategy, incorporating relevant pre and post-purchase user recommendations. This strategic initiative was driven by Gustavo who took a proactive approach to seeking the best solutions to meet their evolving needs.
A key aspect for Forus involved transitioning towards a more data-driven approach and establishing a foundation for real-time capabilities. This shift aimed not only to enhance data-driven decision-making but also to enable immediate client engagement following key events in the customer journey.
Developing the MDS required the application of software engineering and Apache Airflow best practices. The goal was to create a sustainable, scalable, and user-friendly self-service solution. A critical decision in this process was the adoption of a multi-cloud approach, prioritizing advancements in data ingestion, processing efficiency, and data availability times. These aspects are critical for Forus and Gustavo’s department because they are a substantial part of their information strategy.
Solution
Our solution involved the strategic and technical design and implementation of a Modern Data Stack (MDS) built on the robust foundation of our partners, Google Cloud Platform (GCP). Leveraging various open-source components such as Airbyte, DBT, and Apache Airflow provided substantial benefits in scalability, modular design, and security.
The project involved deploying K8s on GKE, creating a Data Lake in GCS, extracting raw data from diverse SQL and NoSQL sources using Airbyte (e.g., Oracle, DB2, MySQL, MongoDB, etc.), processing the data with DBT, building BigQuery data models, and modernizing legacy on-premises Pentaho processes.
The utilization of DBT for structuring data models resulted in enhanced data governance, improved metadata strategies, and the visualization of entity lineage. Meanwhile, Apache Airflow played a crucial role as an orchestrator and task scheduler, offering improved visibility, control over scheduled task execution, traceability, and failure event notifications.
Additionally, we assisted an internal team reorganization, expanding roles and optimizing operational procedures to ensure a seamless workflow.
In summary, our deployed MDS successfully achieved the consolidation of data from various client countries, improved processing speed, ensured information accessibility for agile decision-making, implemented control systems across the entire data pipeline, and upgraded maintenance, quality control, and process traceability.
Impact
Our team of expert #DataNerds worked with the Forus team to achieve the following impact:
- Information availability times decreased from days to hours.
- Data freshness from 24h updates to near-real-time.
- Increase in data entity coverage by 30%.
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