Workload automation has developed rapidly in the last few years, as it evolves to meet changing business requirements. There is now a proliferation of automation technologies across the digital enterprise, but workload automation for batch jobs is still the preferred technology for enabling most business-critical tasks with strict SLAs for timely completion. However often workload automation does not get the due attention it deserves in digital transformation plans. This blog aims to “bust” common myths that may prompt its being overlooked.
Myth #1: Workload automation is a legacy technology and doesn’t fit into modernization plans
Some organizations use the term “workload automation” interchangeably with “batch scheduling” – a legacy process of executing a series of automated tasks or programs at a specific date or time as a part of a lengthy process series of steps. Such a definition conjures up the image of a complex mesh of tasks, usually running in the middle of the night in the back rooms of IT departments without needing much rethinking. This implies that it doesn’t fit into the modern world of agile, responsive digital business.
Workload automation evolved from batch job scheduling to fit the modern enterprise’s needs – which include running processes across platforms and applications/operating systems, and leveraging public cloud capabilities. It is also more agile now with the ability to support both schedule- and event-based triggers like the arrival of a file or completion of another process, like end user interacting with an online portal, completion of a predecessor batch job and so on.
Workload automation is rapidly adapting to newer challenges with increased capabilities, like better orchestration capabilities and the ability to manage variable workloads.
A recent EMA (Enterprise Management Associates) study reinforces this line of thought, with 53% of respondents agreeing that workload automation tools should be expanded to orchestrate automation tools across the enterprise.
This makes workload management a strategic piece of automation for the autonomous enterprise, as it can help orchestrate functions across a wide variety of tools, technologies, and integration points.
Myth #2: Workload management doesn’t have strategic impact
Due to the legacy of running back-end tasks as batch jobs, workload management is often considered as a non-strategic piece of automation. From this perspective, its primary importance is to “keep the lights on” for other IT processes and applications that are more critical for business operations.
Over the last few years, one of the critical capabilities that evolved in workload management is the ability to set up, track, and prioritize business SLAs (Service Level Agreements). In simple terms, SLAs are commitments between IT and business teams that ensure IT processes meet business requirements. As businesses increasingly adopt digitization, they are now highly dependent on workload management because it can ensure timely completion of deliverables.
In fact, in another recent report, EMA stated that it believes “WLA (workload automation) is the class of software best positioned to become one of the key IT operations automation tools and even expand to directly automate business processes.”
Myth #3: Workload automation schedulers alone are sufficient to ensure smooth operations
Originally there used to be multiple workload schedulers for each type of OS (operating system) or platform. More recently workload automation platforms have appeared that aim to consolidate diverse types of workload processes in a single scheduler. As more and more workload processes are brought under a single scheduler, some organizations believe that the ability to centralize the workload management itself will ensure that they are ready to meet increasing demands.
While centralized workload automation providers help reduce the complexity and provide the functionalities needed by a modern automation provider, like most automation tools they need to be complemented by intelligent analytics and automation. Workload management today is dynamic and complex and needs intelligent systems to manage it better.
Typically, workload analytics are required for a variety of use cases, such as:
- Dealing with changes to the workload ecosystem due to modernizations and migrations
- Planning to meet sudden spikes in volumes due to dynamic business requirements
- Ensuring the IT system can meet seasonal peaks like financial year ends and Black Friday sales
- Simulating the impact of any IT outages that can cause down times in business processes
- Simulating the impact of change in the blueprint in the ITOps ecosystem
- Predicting key business SLAs in real time or for future dates.
Looking beyond the myths
To sum up, we at Digitate recognize that workload automation is a far more valuable component of the modern enterprise IT platform than its somewhat dowdy reputation and needs to be a key component of digital transformation plans.
Since we recognize workload management’s importance to IT operations and understand the need to have better AI-based analytics and predictions to make workloads more efficient and robust, we developed a unique product to not just manager workload-related incidents but provide intelligent analytics across SLA management, batch optimization, change analytics, peak season predictions, and many more workload specific use cases.
Digitate ignio™ AI.Workload Management has been developed to meet common enterprise requirements for workload management, by providing a strategic, analytical layer on top of the existing in-house or third-party automation engines. It leverages domain-specific AI/ML models to provide descriptive, predictive, and preventive intelligence to help organizations build agile workload management systems. It “learns” how your system works and then automatically takes necessary actions and fix delays and failures without manual intervention, ensuring critical business operations are not impacted.
To learn more, go to https://digitate.com/ignio-ai-workload-management/