Google Cloud Platform > Case Studies > ATB Financial: Accelerating SAP data insights and boosting business outcomes with BigQuery

ATB Financial: Accelerating SAP data insights and boosting business outcomes with BigQuery

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Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Application Infrastructure & Middleware - Data Visualization
Applicable Industries
  • Construction & Infrastructure
  • Finance & Insurance
Applicable Functions
  • Logistics & Transportation
  • Quality Assurance
Use Cases
  • Fraud Detection
  • Real-Time Location System (RTLS)
Services
  • Cloud Planning, Design & Implementation Services
  • Testing & Certification
About The Customer
ATB Financial is an Alberta, Canada-based financial institution with nearly CA$55.6 billion in assets. ATB’s more than 5,000 team members deliver exceptional experiences to over 800,000 clients through hundreds of branches and agencies, a 24-hour Client Care Centre, four entrepreneur centers, and multiple digital banking options. Founded in Alberta in 1938 to give citizens an alternative source of credit during the Great Depression, ATB Financial (ATB) is now the province’s largest financial institution. Like many financial services companies, ATB continuously seeks to innovate to expand services, increase client satisfaction, and achieve operational efficiencies.
The Challenge
ATB Financial, a Canada-based financial institution, was facing challenges with its legacy data platform. The analytics processes were ad hoc and deeply manual, often requiring multiple data copies and heavy manipulation to produce reports. The legacy data environment needed enhancements to support future business requirements that have a high dependency on real-time data analysis and insights. ATB was only able to manage structured data using its traditional data warehouse platform. The bank was seeking to innovate to expand services, increase client satisfaction, and achieve operational efficiencies to compete with big, established banks as well as financial technology startups.
The Solution
ATB chose BigQuery as a serverless data warehouse and data lake for its next-generation, cloud-native architecture. BigQuery serves as a core component of what ATB calls its Data Exposure Enablement Platform (DEEP). This next-generation platform replaces the bank’s legacy monolithic data architecture, providing a standardized enterprise data model that eliminates inefficiencies and inconsistencies and simplifies analytics. With BigQuery, ATB can now acquire and ingest data from a variety of internal and external sources, at scale, in real-time. It can enrich data through advanced analytics and artificial intelligence, providing valuable insights for decision-makers, with breakthrough speed. It also enables self-service analytics for all internal organizations, making data more accessible through easy-to-use reports, dashboards, and data visualization tools.
Operational Impact
  • The Google Cloud solution proved its worth during the COVID-19 pandemic by helping the bank intelligently identify clients in financial distress. The bank’s business leaders now enjoy more read access to data, allowing them to make better-informed decisions in real time. Data scientists and analysts enjoy easy-to-use data visualization, analysis, and reporting capabilities. ATB has leveraged these improvements to increase in client satisfaction and revenues through contextual recommendations and customized interactions, while also leveraging them to reduce theft and reputational damage through stronger fraud detection. The solution enables IT teams at the bank to reduce operating expenses and complexity by migrating its infrastructure to the cloud, accelerate time-to-value and improve choice by leveraging more third-party services, mitigate risk by securing data, improving governance, and increasing service availability.
Quantitative Benefit
  • Enables business insights from multiple data sources 117 times faster than previous approaches
  • Realizes millions in efficiency and technology cost savings by retiring legacy environment
  • Accelerating the delivery of new datasets by 30% compared to previous approaches

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