Google Cloud Platform > Case Studies > Boa Vista's Digital Transformation with Google Cloud

Boa Vista's Digital Transformation with Google Cloud

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Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
  • Construction & Infrastructure
  • Retail
Applicable Functions
  • Product Research & Development
Use Cases
  • Construction Management
  • Infrastructure Inspection
Services
  • Cloud Planning, Design & Implementation Services
  • Training
About The Customer
Boa Vista Serviços is a leading analytical-intelligence and credit-bureau company in Brazil. The company has an extensive database of files on approximately 280 million people and corporations. Using this invaluable information, the company develops solutions to support credit activities and protection for its nearly 21,000 active customers, including financial institutions, fintechs, and Brazil’s largest corporate and retail groups. The company's work is driven by data and it employs over 800 people. Boa Vista's business is robust and requires cutting-edge technology to meet market needs quickly, deliver increasingly relevant products to its customers, and maintain a competitive edge.
The Challenge
Boa Vista Serviços, a leading analytical-intelligence and credit-bureau player in Brazil, was facing challenges with its previous technology infrastructure. The company's physical servers located in three data centers were hindering its speed and ability to innovate. Limited scalability of resources and processes meant that they could not train many analytical models simultaneously or implement modern machine-learning techniques. One of its products was on the verge of halting sales because the company's processing capacity had reached its peak, directly impacting business expansion. The company needed to embrace digital transformation by migrating all its information from physical servers to the cloud. They also needed to switch from a traditional organizational model to a squad-based model to focus more efficiently on various technology and business aspects and challenges.
The Solution
Boa Vista chose Google Cloud as its partner for digital transformation. The first area to be migrated was big data, where there was an urgent need to increase processing capacity. The company created a centralized data lake in the cloud to deliver more robust processing, high scalability, and improved information governance. With support from Google Cloud’s team, Boa Vista adapted its big data infrastructure to the cloud in a modern, efficient way. The company also migrated nearly 500 virtual machines (VMs) to the cloud using the Migrate to Virtual Machines solution. For products that needed to be rebuilt, they were directly migrated to Google Cloud’s data-analytics tools such as BigQuery and Dataproc. The company also automated more than 1,000 hours of operational work from Boa Vista’s infrastructure team.
Operational Impact
  • The migration to Google Cloud has significantly impacted Boa Vista’s analytics department. The team can now parallelize the processing of its analytical models without limits, using complex machine-learning techniques, which has increased the product’s operational efficiency. Model processing is up to 20x faster in some cases. The new infrastructure allows provisioning new environments in minutes instead of months and scaling the infrastructure according to demand and available financial resources. The costs of migrated products were also reduced. The company has also been able to launch new products, such as Analytika and the Vulnerability Index, which would have been unthinkable without the cloud migration. The migration has also enabled the creation of de-identified data catalogs of the products in the data lake, making it easier for business professionals to access the information, boosting their autonomy and input in the technology and business sections.
Quantitative Benefit
  • Ninefold increase in the number of analytical models created
  • Time to ingest large volumes of data reduced from up to 24+ hours to a few minutes
  • Ability to train models between three and five times longer than with the previous environment

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