Putting Workload Management at the Center of ITOps Transformation

It is ironic that IT, which is considered synonymous with technology and automation, may itself be in dire need of process automation to eliminate its manual, repetitive, and mundane aspects.

This is because, IT operations (ITOps) is both a victim and a beneficiary of fast-scale technological changes. On one hand, IT products and services have gained massive scale and complexity, making it difficult for small and medium-sized teams to manage IT operations. On the other hand, innovations such as Virtual Networks, Containers, Cloud, and AI (Artificial Intelligence) have brought new opportunities to improve and automate provisioning, maintenance, monitoring, remediation, and similar tasks to reduce manual dependence on ITOps processes.

However, as ITOps deal with a large array of automation strategies for each function, such as Workload Automation, Robotic Process Automation (RPA), and Process-Specific Automation, IT organizations are facing challenges in deciding where to aim their transformation efforts first.

The case for workload management-led transformation

Workload Automation is an obvious candidate, as in most organizations, workloads or batch jobs represent the largest volume of automated operations. These batch jobs are business-critical, since many functions, such as order management, payment transactions, report creation, logistics depend on their prompt completion. Any delay in completing these functions may derail critical business processes. Moreover, many tasks associated with workload management functions are repetitive, time-consuming, and resource-intensive, hence reinforcing the need for transformation.

Furthermore, due to some of the limitations of existing workload management technologies, many organizations consider workload management as an ordinary tool, and not as a key aspect of IT automation and transformation strategy. Visibility in workload ecosystems is extremely fragmented, and therefore it is hard to manage. In most organizations, thousands of batch jobs and files are associated with multiple schedulers, legacy systems, and cloud platforms with complex interdependencies, making it a modern-day equivalent of an old-fashioned phone switchboard with plugs.

Technology that meets the current needs

Having worked with ITOps teams in large enterprises across the globe, Digitate^TM is aware of technologies that can give business and IT leaders more visibility in workload management. Hence, we created a specialized product ‘ignio^TM AI.WorkloadManagement’, which provides a closed-loop solution for proactive and autonomous workload management. It combines the power of Machine Learning (ML), Artificial Intelligence (AI), and automation to deliver predictable, agile, and silent batch operations.

The latest release of ignio^TM AI.WorkloadManagement is very significant as it takes major strides in reducing the technology gaps mentioned above. It puts the workload management function in the driver’s seat of a larger ITOps transformation. ignio^TM AI.WorkloadManagement is already enjoying a unique position in the workload management technology space, being the only solution that can:

• Work across a heterogenous batch environment and can map batch to business and to infrastructure layers.

• Provide multi-layered intelligence on workload operations, including descriptive, diagnostic, predictive, and prescriptive analytics.

• Enable closed-loop operations to go beyond predictions for diagnosis and recommendations and can take automated corrective actions.

• Leverage domain-specific AI/ML models to provide accurate Service Level Agreement (SLA) predictions.

Our latest release expands on these strengths by going beyond workload management and offers the same capabilities to other key IT processes, such as transactions, orders, requests, and so on.

Additionally, it can now support predictions on files and job-files dependencies, which is a key to normal business functioning. This ensures a single pane of glass visibility for ITOps across major IT processes, putting workload analytics at the center of all monitoring and anomaly-detection activities.

The release also comes with a next-generation Watch dashboard. This feature focuses on monitoring the entire gamut of IT processes and detecting any anomalies hidden among thousands of transactions, increasing IT observability.

An advanced operations console complements this visibility with centralized access to various levers to take corrective actions manually if needed. The new release not only provides a wealth of capabilities, however, it also makes installation easier by using data-mining techniques to eliminate the need for complex adapters while connecting to the existing third-party workload automation tools.

Business teams also get more visibility in measuring the impact of the solution on their processes through a governance dashboard to view the effectiveness of the use cases.

The way forward

The new release of ignio^TM AI.WorkloadManagement offers a single solution for 360-degree monitoring of IT processes, expanding its control beyond the scope of workload automation. By extending workload monitoring, analytics, and predictions to other processes, it helps in building synergy between ITOps and business teams with the ability to cater to the domain-specific requirements. It also helps the business teams to become more agile, as they can now go beyond the traditional workload solutions and propose newer schedulers matching their goals, without worrying about the efforts required to onboard and monitor them.

Click here to learn more about ignio^TM AI.WorkloadManagement or contact us for a detailed discussion.