Visionet is a digital technology solutions company built on the premise of disruptive innovation. The client is a leading life insurance provider, delivering its services to millions of customers. Their aim is to ensure financial security worldwide through their comprehensive range of offerings.
Challenges Faced by Client
The client encountered operational hurdles stemming from an outdated data platform, characterized by older versions of cluster and node configurations. To meet the ever-increasing data management demands, the client had migrated its data to a new system. However, this transition proved to be challenging, resulting in system clogging and extensive storage utilization. As a result, it compromised system functionality and performance, hampering business operations.
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Furthermore, insufficient consideration given to data volume implications, and lack of monitoring and data observability mechanisms, made scalability a major issue for the client. Data Engineers and Data Scientists also struggled in running and managing queries which led to various performance issues.
The legacy infrastructure further hindered the adoption of modern data strategies, while the absence of release management constrained effective data querying, access, and utilization by admins and users. Overcoming these challenges to improve overall platform performance proved daunting, as they contributed to operational inefficiencies and increased costs. This underscored the client’s need for a comprehensive solution to remain competitive in the digital landscape.
Solution Offered by Visionet
In addressing these challenges, Visionet implemented an advanced AI-powered Modern Data Platform solution. Utilizing AI algorithms, the platform effectively employed data compression techniques to minimize storage requirements while maintaining data integrity. Intelligent Data Purging was also implemented, leveraging AI to accurately identify and archive obsolete data, optimizing storage management.
The deployment of AI-powered predictive analytics enabled proactive forecasting of storage demands, facilitating scalable infrastructure. Automated data partitioning intelligently distributed data across multiple nodes and clusters, with redundant clusters decommissioned for enhanced efficiency. AI algorithms automated resource provisioning based on demand, optimizing resource allocation dynamically.
Additionally, mechanisms such as Parallel processing and Auto-scaling infrastructure adjusted server and computing node numbers in response to workload patterns. Natural Language Processing (NLP) techniques streamlined data processing workflows, including tasks like data cleaning, transformation, and enrichment.
Moreover, AI algorithms continuously monitored the platform’s health and performance, automating routine maintenance tasks and upgrades to ensure ongoing operational efficiency and up-to-date infrastructure.
The Impact
Leveraging an AI-based approach, Visionet created a high performing, result driven, and scalable system, highly compatible with the latest technologies. Some of the key benefits delivered include:
- Improved cost savings by 15 to 25% annually.
- Enhanced data security measures.
- Streamlined processes through automation, resulting in enhanced system efficiency and performance.
- Comprehensive end-to-end visibility of all information across the organization.
