Timely and predictive processing of batch jobs is vital to ensure stable business operations and high-quality customer experience. ignio AI.Workload Management brings a paradigm-shift in today’s batch operations from “reactive to proactive” and “manual to automated”. It combines the power of AI, machine learning, and automation to make the workload systems predictable, agile, and silent.

The challenge

Management of batch systems face two key challenges:

Scale, diversity, and complexity: Thousands of batch jobs, defined by complex precedence relationships cut across different business functions, and batch schedulers.
Changing environment: Different batch functions are performed on different days. As a result, the batch graph keeps changing every day.

The implications

These challenges collectively lead to the following chain of implications:

Lack of end-to-end visibility
Difficult to predict outages
Difficult to assess impact of change
Surprises & firefighting: Reactive operations
People dependence

The solution

ignio AI.WorkloadManagement combines the power of AI, machine learning, and automation to ensure proactive workload operations. Following are the key features of the product:

Learns the context of batch
Predicts and prevents SLA violations
Plans for growth and change
Diagnoses and resolves job failures
Identifies opportunities for proactive problem management

Out-of-Box integrations available for Azure Batch, Autosys, Control-M, ESP, TWS

ignio AI.Workload ManagementDigitate

ignio AI.Workload Management

Digitate

ignio AI.Workload Management

Digitate

ignio AI.Workload Management
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