Business Goals
Our client has over 50 years of consultancy experience working in energy and utilities management, responding to the changing needs of organisations across sectors from manufacturing to hospitality and retail to logistics. They have the expertise and insight to help successfully control costs, improve margins, achieve regulatory compliance and protect operational resilience. They help their customers make the right procurement decisions, better manage the complex process of utility billing, provide data and insight that improves operational and financial performance and provide expert consultancy that optimises consumption.
They looked for a cost-effective data integration and management solution that could be designed and developed quickly and easy to maintain.
Technological Challenges
They relied on a traditional, relational database management system as their data warehouse. And it was challenging to manage the growing volume of data.
As part of other ongoing digital transformations, newer SaaS-based applications were introduced into their IT landscape. But unfortunately, those modern applications could not share and retrieve data from the legacy DB.
- Difficulty in accessing data quality data
- High data retrieval time
- Data was not meeting the desired standards to run analytical reporting
- High costs in maintaining on-premise hardware & software
How NeosAlpha Helped
Our data engineers analysed the sources of different data types and established a data relation model. They also understood the need to handle vast volumes of data in a short period. So, scalability, integrity and availability were key factors in the detailed data architecture. SQL Server Integration Services (SSIS) were opted to build enterprise-level, scale-out serverless data integration and data transformations connecting systems on-premise and the cloud.
Azure SQL Server Integration Services (SSIS) is a cloud-based data integration tool that is part of Microsoft’s Azure Data Factory service. Azure SSIS enables organisations to move and transform data from various sources to a cloud-based data warehouse or other data storage solutions. It provides a scalable and flexible solution for data integration, allowing organisations to process and analyse large volumes of data quickly and efficiently.
Our implementation covered the following key areas,
- Cloud data warehouse: The first step was to create a cloud-based data warehouse using Azure SQL Data Warehouse. The data warehouse was designed to handle the growing volume of data and provide scalability and elasticity.
- Data modelling: The next step was to create a data model that defined the structure of the data warehouse. The data model was designed to ensure that the data was consistent and accurate.
- Data integration: The third step was integrating data from various sources into the cloud-based data warehouse. Azure SSIS Data Integration was used to extract data from source systems, transform it into the required format, and load it into the data warehouse. The solution also involved setting up incremental load processes to update the data warehouse with new data.
Results
- Well-integrated data across various data sources and applications
- Efficient access to data
- Lower maintenance costs
- Higher availability
- Improved data analytical capabilities and business intelligence
