Revolutionising fashion with AI

Industry

Technology

Data Science Delta Lake ETL Azure Machine Learning

Introduction

NashTech provided a Lakehouse Solution that has fostered a scalable and collaborative environment across data science, leading to a 70% reduction in operational costs.

A Swedish multinational clothing company headquartered in Stockholm is the second-largest global clothing retailer, behind Spain-based Inditex. It is one of the world’s largest fashion companies with more than 120,000 employees worldwide and operates in 74 countries with over 5,000 stores under the various company brands.

The challenge

Data is at the heart of everything the company does as a key disruptor and innovator in the fashion and retail industries. They needed to strengthen their supply chain and forecasting operations to simplify costs and maximise earnings as they opened locations throughout the world at a rapid pace.

However, their on-premise Hadoop system limited their ability to ingest and analyse data from millions of consumers, which was required to run predictive models. They turned to the NashTecch Lakehouse Solution to simplify infrastructure administration, provide performant data pipelines at scale, and streamline the machine learning lifecycle, enabling them to make data-driven decisions that accelerate business growth.

“Technology is a great equaliser that enables our clients to compete with the largest banks in the world. One of the significant technology advantages that NashTech expertise Solution provides is the ability to share across our product portfolio. The significant events that occur throughout an end user’s financial journey, from opening an account to initiating a home or small business loan to saving for college or retirement,” said Vice President, hosting architecture.

Legacy architecture unable to support company growth

In order to improve supply chain efficiencies, they chose to utilise data and AI to improve decisioning and operations. However, their legacy Hadoop based architecture was inefficient and wasn’t able to scale to meet their rapid business requirements.

• Massive volumes of data from over 5,000 stores in over 70 markets, with millions of customers every day.
• Data engineering was challenged with fixed size clusters, complex infrastructure that was resource intensive and costly to scale, and data security issues.
• Struggled to scale operations to support data science efforts against all of this data coming from various siloed data sources.
• Time-to-market suffered because of significant DevOps delays, which impacted the ability of their data scientists to build, train, and deploy models quickly. It would take a whole year to go from ideation to productionisation.

The solution

Simplifying data operations boosts ML innovations.

NashTech provides them with a Lakehouse Solution that has fostered a scalable and collaborative environment across data science and engineering, allowing data engineers and scientists to focus on the entire data lifecycle instead of managing clusters, to train and operationalise models rapidly with the goal of accelerating supply chain decisions for the business.

The organisation has since expanded the use of NashTech expertise Solution to other projects, including one where Kafka is being used to standardise and move data from Apache Cassandra databases to Molecula’s Cloud Data Access platform. “This solution uses multiple NashTech’s expertise Solution features,” their team members explained. “We structure the data from our Cassandra databases using a model stored in Schema Registry, and we use NashTech Replicator to replicate topics across multiple data centers.”

“We move nearly 1.5 trillion dollars through our platform each year, so reliability is critical for us; we cannot have data loss or message-write failures.” As we continue to extend our platform into loan origination, loan decisioning, and other areas, the need to reliably share data becomes more critical. Having NashTech’s expertise as part of our software architecture enables us to easily move data across products and across data centers, public and private, to fulfill that need.”

• A fully managed platform with automated cluster management simplifies infrastructure management and operations at scale.
• A collaborative notebook environment with support for multiple languages (SQL, Scala, Python, R) enables a diverse team of users to work together in their preferred language — creating a unified cross-team environment to fuel productivity.
• Integrated NashTech Solution with Azure and other technologies like Apache Airflow and Kubernetes, so elastic model training at a massive scale can be achieved.

The outcome

Smarter decisioning, dramatic cost savings

Even a 0.1% improvement in the accuracy of a single model has a huge impact on the business. With NashTech, they make data more accessible for every decision maker, making the business grow faster and more relevant.

• Improved operational efficiency: Features such as auto-scaling clusters have improved operations from data ingestion to managing the entire machine learning lifecycle — reducing operational costs by 70%.
• Better cross-team collaboration: Unified analytics environment for both data scientists and engineers has dramatically reduced the number of components needed to go into production with easy setup and management.
• Huge business impact with faster time-to-insight: The ability to be more granular in decision making has allowed them to improve strategic decisioning and business forecasting.