Traditional data architectures create a number of business challenges. On premise solutions typically have limited processing power and do not scale well. Because of the high cost of maintaining in-house infrastructure and poor adaptability to changing business requirements, on-premise architectures represent a poor ROI.
Data modernisation raises unique challenges for each company. How you approach these challenges will determine the success of your data transformation programme. By working in partnership with our clients we have helped them address significant data challenges to ensure that their transformation programmes meet business goals.
#1 Embracing cloud transformation
Business challenge
Recognising the limitations and growing cost of managing a traditional on-premises data warehouse, the business identified the need to transform their architecture to handle both structured and unstructured data with the required processing power and scalability.
Solution
Our Cloud Architect led a cloud transformation team to move on-premises MI Data Warehouse from a traditional Microsoft BI stack to a Cloud architecture, including Microsoft and Databricks technology, using Terraform for health monitoring and cloud spend optimisation.
#2 Tacking the challenge of data modernisation
Business challenge
Having shaped the strategy to achieve their future-state data architecture, the business requested our assistance to resolve a number of technical challenges across different aspects of the data architecture.
Solution
We were engaged as a consultant to the client’s Architecture team to advise them on the development of their modern data architecture. By combining our experience of Cloud and Kafka technologies, together with data models for digital enterprises, we were able to help the client resolve multiple technical challenges with the architecture, design and deployment of their target platform, including proof of concepts to validate system design.