Stabilizing Data Platform Operations through 24×7 Managed Services and AI-Led Automation

Overview

A U.S.-based financial services provider was facing challenges in maintaining stability and efficiency across its complex ETL and data platforms. The existing support model struggled with high incident volumes, lack of automation, and weak knowledge management, resulting in SLA breaches and operational inefficiencies. Coforge partnered with the client to implement a structured, SLA-driven managed services model with AI-led automation to ensure resilience, scalability, and operational excellence.

Capabilities

Automation

Challenge

Ensuring 24×7 Stability and Reducing Operational Inefficiencies in Complex Data Environments

Current support model struggling to maintain 24×7 stability across ETL and data platforms
High incident volumes delaying resolution and impacting business continuity
Limited automation leading to inefficiencies and repeat issues
Weak knowledge management framework
Lack of structured managed services model causing SLA breaches and compliance risks
Reduced customer confidence due to inconsistent service delivery

Tools & Technology

Shell scripting, SQL, Azure Databricks, Talend, Application Monitoring tool, Python and Hadoop

Solution

AI-Driven Managed Services Model with Proactive Monitoring and Operational Excellence Framework

Coforge implemented a comprehensive managed services strategy:

Deployed a dedicated 24×7 managed services model across ETL pipelines, Hadoop, Talend, Databricks, SQL, and Python platforms
Enabled structured knowledge transfer framework for seamless transition and faster stabilization
Implemented AI-assisted support and automation-first strategies for proactive monitoring of 600+ Talend jobs
Introduced Shift-Left practices and cognitive automation to reduce ticket volumes and improve resolution cycle times

Established robust service governance with real-time reporting, root cause analysis, and continuous improvement frameworks

Impact

Reducing Costs, Improving Resiliency, and Driving SLA-Based Operational Performance

Reduced total cost of ownership (TCO) through optimized support operations
Improved year-on-year productivity through automation-led delivery
Enhanced system resiliency with proactive monitoring and faster incident recovery
Standardized operations with knowledge base, runbooks, and governance frameworks
Strengthened talent quality and stability through continuous knowledge transfer
Enabled real-time reporting for improved decision-making and visibility

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