Modernizing Data & Analytics on Azure Cloud for a Full-Service Restaurant Group

The Client

The multi-brand family of restaurants features some of the most recognizable and successful brands in full-service dining. The company employs more than 175,000 team members in more than 1,800 restaurants, creating memorable experiences for nearly 360 million guests in hundreds of communities across North America.

CLIENT PROFILE
EMPLOYEES: 200,000+
INDUSTRY: Food Service/Restaurants
FOUNDED: 1968
 

Primary Objective/Problem

The client has been operating a mature, large on-premises data warehouse and BI environment for over a decade. The business leadership has initiated a robust data science program to solve complex business problems. Business and IT were experiencing several challenges with the existing data infrastructure; business agility was being compromised due to the lead and lag time needed for dealing with the ever-growing data. The client initiated a data platform and BI modernization strategy program which centered on:

  • Establishing a data & analytics cloud modernization strategy on the Microsoft Azure cloud platform

  • Architecting a secure Azure cloud data architecture

  • Migrating storage and computing workloads to Azure

Smartbridge has extensive experience in data and analytics

Data and Analytics

Key Challenges

  • The analytics teams had limited access to the data they needed. The potential data sources and the storage and computing infrastructure requirements were growing faster than the IT team could keep up with.

  • The rapid growth of data assets and the heavy computing load imposed by the data science models began to impact the data warehouse’s performance, resulting in ETL completion delays and reporting issues.

  • The data science team was experiencing challenges to scale and operationalize analytics models. The data and data science models compute server environments were hitting their computing performance limits.

  • The external data sources and data sets were known, but they were siloed and undocumented. Information about the specifics of the data they contained and what it would take to migrate the data to the cloud was limited.

The challenges were limiting the analytics team’s ability to deliver critical insights to support business growth initiatives related to Customer, HR, Marketing, and Operations.

Migration to the cloud to take advantage of its elasticity and scaleability was the most logical option available.

Solution/Smartbridge Approach

The client selected Smartbridge to execute a data & analytics modernization program on the Azure cloud. We completed the program in 2 phases –

  1. the planning, design, and architecture phase
  2. the initial implementation and cloud migration phase

The project included the following components:

  • Establish Azure cloud infrastructure blueprint and provision Microsoft Azure services

  • Design and implement data lake, analytics workspace, and cloud data warehouse

  • Replicate on-premises data warehouse to Snowflake cloud database

  • Create dynamic data lake ingestion pipelines using Azure Data Factory

  • Define and build critical business use cases across finance, HR, and marketing

  • Design and implement operational processes to ensure the ongoing success of the program and solution set

Smartbridge executed the project using a blended onshore-offshore delivery model to achieve agility, scalability for a cost-effective implementation.

modernizing data and analytics

Solution/Smartbridge Approach

The client acquired new and advanced data management and processing capabilities, actionable recommendations, and a clear path to hyper-analytical capabilities in the future.

  • Successfully achieved several quick wins for the analytics team

  • Architected a robust future-state Azure cloud architecture consisting of data lake, Snowflake data warehouse, analytical computing workspaces, and MicroStrategy BI reporting capabilities

  • Migrated data from the enterprise data warehouse to the cloud data warehouse

  • Provisioned the data lake, built ingestion pipelines for all prioritized and quick win datasets

  • Established the analytics workspace which enabled the analytics team to start to work on the quick win use cases while the remaining roadmap workstreams were in progress

  • Established the foundation for the future state analytics and data science program

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