Proactive and Predictive Batch Operations by ignio™ AI.Workload Management
Increased productivity and agility in business processes for global IT services and consulting organization
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With its Machine First Delivery Model™, TCS wants to optimize machine-human collaboration and drive business value, making the internal processes faster by simplifying the complex, dynamic IT ecosystem. One critical step taken towards this goal, was the elimination of low-value, time-consuming jobs by making its IT Operations, specifically batch jobs, ‘smarter’. Automated workloads and batch jobs are the backbone for day-to-day operations, but the sheer size and complexity of batch processes often catches the Business IT teams by surprise. This often leads to missed SLAs resulting in delayed business operations
The Challenge
1. Scale, Diversity and Complexity: TCS has a complex IT ecosystem which requires frequent update of records across 200+ applications. To achieve this TCS’ IT Unit, Ultimatix needs to run 7000+ batch jobs (scheduled programs that can run without further user interaction), daily. The batch jobs are responsible for business-critical processes such as payroll, financial period closure, invoices, billing, and revenue reports. TCS had already automated a substantial amount of these batch jobs, but manual intervention was required to interlink them. Moreover, many processes had independent batch scheduling tools, adding to the complexity of operations.
2. Siloed IT Operations: Due to interdependency between business processes, the batch jobs also have complex dependencies. Lack of visibility and co-ordination between different functions resulted in undue delays and failures, impacting overall business outcome.
3. Reduced Productivity: The team was involved in low-value, time-consuming jobs (debugging to locate issues, scheduling different batches at different times, and so on) leaving them with little time for process improvement.
4. Dynamic Environment: Periodically, new batches are deployed, and old ones are retired. At scale, the impact of such changes are difficult to gauge. Siloed operations and difficulty in predicting outages resulted in ‘surprises’ when a batch would fail unexpectedly. Hence, TCS needed to evolve from this reactive mode of operations.
The Solution — ignio WLM
ignio™ AI.Workload Management helped TCS leverage the power of AI / ML to implement an intuitive Batch Management System, transforming IT operations from reactive to proactive and predictive, and making business processes agile. TCS also deployed an orchestration solution leveraging its ecosystem to eliminate multiple disparate and incompatible schedulers and bring in uniformity through a single pane of glass.
ignio™ captures the meta-data and run history of batch systems and provides a view connecting business functions, batch jobs, and infrastructure providing a holistic view that helps gauge the business impact of the workloads.
ignio™ further provided TCS with Advanced Analytical Insights through an intuitive dashboard integrated with ignio™ AI.Workload Management. This brought in a paradigm shift in operations from siloed processes to an automated and analytics driven approach. ignio™ now helps detect anomalies, predicts future behavior, recommends optimizations, and plans for growth and change.
Additionally, ignio™ intelligently predicts batches prone to miss their SLAs, and proactively suggests potential diagnosis and fixes, by narrowing down the root cause. It also provides a holistic view on the overall business impact of workloads.
Measurable Value Across Every Dimension
Integrated operations through complete visibility on 4200+ interlinked processes - across 200+ applications and 27 business critical processes
Enabled faster time-to-market by improving the delivery time for important functions
Improved overall response time by reducing time to identify issues
Increased productivity by reducing manual efforts for process activities
Agility in business processes through readily available business information on-demand
Proactive notifications of potential SLA violations 4-5 hours ahead-of-time
