CASE STUDY

Streamlined Data Management with
Automated Workflows and AI Solutions

Artificial Intelligence
Retail

Project Highlights

Scope

  • Streamlined Data Management with Automation and AI.
  • Self-Service Solutions for Workflow and Permissions Management.
  • Democratisation of Data Exploration through AI.

Operational Impact

Self-Service

Teams now operate independently on Looker and Dataform.

AI-Driven Decisions

Non-technical users now make data-driven decisions through Looker AI Assistant.

Infrastructure Automation

CI/CD pipelines ensure fast and secure updates across platforms.

Business Impact

Automation and self-service solutions have significantly streamlined operations, empowering teams and improving overall productivity. These metrics highlight the effectiveness of our efforts in reducing manual work and accelerating workflows.

Productivity Boost
30% improvement in team efficiency due to automation
Reduction in Manual Effort
90% of routine tasks automated
Data Accessibility
100+ non-technical users empowered with AI-driven data exploration tools
Faster Turnaround
50% reduction in workflow approval times through self-service systems

The Client

The client is the central IT service provider for a leading European fast fashion retailer, managing all digital operations and IT services for the company. They are responsible for ensuring seamless e-commerce platforms, supply chain systems, and customer engagement tools.

  • Provides full IT support for the retailer’s operations across numerous European markets.
  • Drives digital innovation to optimise processes along the value chain.
  • Ensures operational efficiency and adaptability in a competitive retail environment.
Retail-Client

The Problem

Manual Dependencies:

Data-related workflows were heavily dependent on manual intervention, creating bottlenecks and reducing overall efficiency. Teams spent significant time on routine tasks that could have been automated, preventing them from focussing on strategic initiatives.

Permission Management:

Managing permissions on platforms like Looker required administrative oversight, creating delays and unnecessary dependencies on IT or admin teams.

Data Accessibility for Non-Technical Users:

Non-technical teams struggled to interact with data without the help of analysts or engineers, making it difficult to fully democratise data usage across the organisation.

The Solution

Dataform Workflow Automation

Enhanced workflow management by automating complex dependencies.

Automated Looker Permission Management

Implemented a self-service tool that allowed teams to manage their own permissions.

Looker AI Assistant

Leveraged an LLM to enable non-technical users to query Looker models and create dashboards using natural language.

Airflow DAG CI/CD Automation

Automated the deployment and monitoring of Airflow Directed Acyclic Graphs (DAGs).

Central Terraform Repository

Implemented a unified Terraform repository for managing GCP infrastructure, supported by CI/CD pipelines.

Team & Technology

Tech Stack Utilised

airflow
Automation
terraform
Automation
github-actions
Automation
google-bigQuery
Data Management
dataform
Data Management
looker
Data Management
python
Programming Language

Team Composition

1 manager and team lead

3 developers

1 product owner

The Outcome

Increased Efficiency and Productivity

Automation of Dataform and Looker workflows drastically reduced manual intervention, while the introduction of AI-driven tools further enhanced productivity.

Empowered Teams with Self-Service Capabilities

Automating permissions management and creating workflow automation empowered internal teams to operate independently.

Data Democratisation

The AI-powered Looker Assistant enabled non-technical users to interact with data models through natural language, making complex data analysis and exploration accessible to a broader range of users.

Faster Issue Resolution and Support

By implementing automated workflow notifications and a dedicated support chat function, teams were able to identify and resolve operational issues more quickly.

Scalability and Long-Term Sustainability

The centralised Terraform repository and infrastructure automation created a scalable system that can grow with the company’s needs.

Operational Independence

The automation of key workflows, combined with the implementation of self-service systems, reduced dependencies on IT and admin teams.

Enhanced Collaboration

Workshops and knowledge-sharing sessions empowered team members to take ownership of their respective platforms, ensuring clear accountability.