How Micropole supported VOO in its complete migration of the Business Intelligence, Big Data and AI landscape to the Cloud.
Find out how Micropole helped one of Belgium's biggest telecom operators with its transformation project.
Context
VOO is a well-known Belgian telecom operator, mainly active in the Wallonia and Brussels regions.
Issues
- Increase customer knowledge to accelerate acquisition and improve loyalty and retention.
- Support digital transformation by offering a unified view of the customer and his behavior.
- Meeting the new challenges of compliance (GDPR).
- Radically reduce the total cost of ownership of data environments (4 different BI environments + 3 Hadoop clusters before the transformation).
- Introduce enterprise-wide Data Governance and solve the problem of shadow BI (more than 25 FTEs on the corporate side to process data).
Solution: an enterprise-wide, cloud-based data platform powered by AWS
To meet these needs, Micropole's experts first carried out an in-depth study, covering all aspects of the transformation, both organizational (roles, responsibilities, teams, skills, processes, governance) and technical (global architectural scenarios, from hybrid cloud to full cloud solutions in PaaS mode).
Based on the results of this study, we implemented an enterprise-wide Cloud Data Platform, combining traditional BI processes with advanced analytical capabilities. In parallel, we also redefined the data organization and associated processes, while introducing enterprise-level data governance.
Benefits of implementing the Cloud Data Platform
- Over 70% reduction in total cost of ownership
- Greater agility within the company
- Considerable reinforcement of the organization's capabilities
Focus on the AWS architecture based on key data services
1) Data Lake
Amazon S3 acts as a central layer for data ingestion, ensuring long-term persistence.
Some data is pre-processed on Amazon EMR, where clusters are dynamically created several times a day. These clusters exclusively process new data arriving in S3. Once processed, the data is stored in an Apache Parquet format optimized for analysis, then the cluster is deleted. Encryption and lifecycle management are enabled on the majority of S3 buckets to meet security and cost-effectiveness requirements. Currently, over 600 TB of data is stored in the Data Lake. Amazon Athena is used to create and maintain a data catalog, as well as to explore the raw data contained in the Data Lake.
2) Data Warehouse
The Data Warehouse runs on Amazon Redshift, using the new RA3 nodes, and follows the Data Vault 2.0 methodology. Data Vault objects are highly standardized and have strict modeling rules, enabling a high level of standardization and automation. The data model is generated from metadata stored in an Amazon RDS Aurora database.
3) DynamoDB
Amazon DynamoDB is used for specific use cases where web applications require sub-second response times. The use of DynamoDB's variable read/write capacity means that the more expensive high-performance read capacity can only be provisioned during business hours, when low latency and fast response times are required. These mechanisms, which rely on the elasticity of the services provided by the AWS cloud solution, are used to optimize the AWS monthly bill.
4) Machine Learning
A series of predictive models were implemented, ranging from a classic unsubscribe prediction model to more advanced use cases. Amazon SageMaker was used to build, train and deploy the models at scale, leveraging the data available in the Data Lake (Amazon S3) and Data Warehouse (Amazon Redshift).
And many more features!
The data platform custom-built for our customer Voo offers many other capabilities. The full range of services available on the AWS environment enables us to respond to new use cases every day, quickly and efficiently.
To find out more about how Micropole can help you with your Cloud Data Platform projects, click here.
