Building a self-service platform for a major networking company and significantly improving its operational performance - Brillio

About the Customer

The client is a major California-based wireless networking company, with a history of over 20 years. Its solutions provide clients seamless wireless network services, monitor networks meticulously, and future-proof networks for IoT (Internet of Things), the next generation of devices.

Business Challenge

Over its growth journey, the customer has acquired various warehouses and different facilities, as well as developed proprietary warehouse management systems, adding significant complexity to its IT landscape. Due to disparate systems, with around 10+ WMS applications used across 150+ warehouses, it was a challenge to provide a self-service platform to customers to place orders and help them more effectively manage their business.

The client also aimed to integrate data across warehouses and provide a single source of truth through the consolidation of data into a data lake, to improve operational efficiency and achieve scalability based on future business needs.

The challenges the customer faced:

  • The manual process of developing and maintaining multiple-source reports using Excel.
  • Manually generated insights were shared via Excel/email to be used by stakeholders.
  • Pnb-Pdw was employed for most data pulls, along with BMT (Backlog Management Tool).
  • The nature of data flow only allowed the users to get the data after 2-3 days, as DXC was also involved in pushing the files through jobs.
  • No predictive or prescriptive actions were in place, with the nature of the analysis more diagnostic.


Brillio’s solution was to set up a Data Lake on AWS Cloud and provide the ability to take the data stored in various systems across the enterprise and consolidate it into a single data store. This would achieve easier access to various analytics and application projects.

Brillio proposed having a cloud-based solution using the ELT process that would retain the source integrity and also have a transformed layer known as SC360 that could be leveraged for analytics, reporting, API consumption, and operational use.

Standardization: Consolidate data sources and key reports across sources (SPDST/BMT/Supply-Demand plan/IBP, and remove Pnb-Pdw as a source). Standardize KPIs and design persona-specific views to enable better decision-making.

Communication/Collaboration: Enable users to share updates, make changes, and notify other users.

Visibility: Enable end-to-end view of supply chain performance like order tracking, inventory tracking, etc. to enable better decision making, and measure hierarchical performance based on stakeholder access.

Advanced Analytics: Deep dive into trends to identify areas of improvement and perform Root Cause Analysis, and enable teams to predict/prescribe actions resulting in business process optimization.

Key considerations:

  • Cloudera deployed on AWS Ec2
  • AWS S3 for Landing and DR
  • AWS IAM for Access management
  • AWS Cloud Watch for log monitoring
  • AWS Cloud Formation Templates for infra automation
  • AWS SQS for alerts and notifications
  • AWS RDS(MySQL, Postgres, Aurora) for metadata management and Data Mart.

AWS services used
Rds, Redshift, Dynamodb, Athena, Glue, Cognito-sync, Cloudfront, Secretsmanager, Ses, Kms, Codedeploy, Config, SNS, States, Cognito-identity, S3, Apigateway, GuardDuty, Cloudformation, Elasticloadbalancing, IAM, Elasticbeanstalk, ES, Codecommit, Cloudwatch, SSM, Lambda, Route53, EC2, Cognito-IDP, Elasticmapreduce, Datapipeline, ACM.

Third-party solutions used
Power BI, Sage Maker

Benefits and Business Impact

With Brillio’s solution – a significant improvement of the new v2 model was achieved by implementing the new pipeline. Also, there was a significant improvement in the query performance (15 sec from 45+ sec) by normalizing multiple queries, as well as data quality improvements by incorporating an automated tested framework. It ensured uniformity of Schema being implemented for various source systems and provided a single source for all APIs and dashboards to consume the data.

  • A DQ dashboard with a mechanism to identify the issues with the root cause and improve consistency, accuracy, and reliability of data at the source systems for high confidence/trusted business reporting.
  • Edge over its competitors by providing a self-service portal to its customers, helping them effectively manage their business.
  • Enhance their customer experience as the visibility and tracking of orders made simple.
  • The high availability of data in a centralized data lake improved the business’s strategic decisions and operational excellence.
  • Use data science on integrated data to identify a cost-effective operating model.
  • Predictive analytics enablements like budget allocation and warehouse space forecasting.
  • Transport management systems technologies enable improved effectiveness in tracking trucks and boost efficiency during loading and unloading.
  • Automated Test Framework ensured the reliability, consistency, and accuracy of data.
  • Complete ownership of data in the data lake, including data from third-party systems.

Target State Architecture

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