By Akhilesh Tripathi, global head, Digitate
Workload management is the key to making an organization – and the individuals within it – more efficient and productive through the strategic organization of the things or processes in a workspace. But it often isn’t being done right or strategically. How can organizations improve workload management in a way that makes it a business enabler?
Workload management is core to a company’s success
Batch systems prepare the business to run. That’s why workload management is one of the cores and critical processes in an IT setup. For instance, banks and insurance firms need to perform certain calculations by a certain time; retailers need accurate daily processing inventory, billing books and general ledger. Any delay or failure in your workloads can severely impact the brand image, in addition to potential financial losses.
Batch systems can be one of the most effective ways to reduce a company’s costs while also increasing employee efficiency, but it hasn’t always been done well. That’s at least partly because it has become so complex, with a hundred thousand or more jobs spread across business functions, complex inter-dependencies and multiple job schedulers. There’s a good reason that the market for integrated workplace management solutions is quickly growing – it’s clearly needed.
Enterprises haven’t always used their technology to address this key internal issue
Even though enterprises tend to chase after cutting-edge technologies to address important issues, they often operate without knowing how to apply their technology to solve their own problems. Consequently, the distance between business and IT increases, reaching a state where the gap becomes a technology debt — a debt that only increases over time, even with the most modern technologies in operation.
It’s imperative to have a mechanism to predict the performance of the workload system and to apply fixes before the problems occur. This is why workload management requires a cognitive approach involving a technology-agnostic, comprehensive blueprint of the job streams. This approach would profile the normal behaviour analysis, coupled with a context-aware, self-triaging and self-healing mechanism.
The problem with existing workload management systems is that they do not consider the batch data in conjunction with the various key performance indicators (KPIs) in the infrastructure based on a context-aware system. In an environment with changing jobs and dependencies, changing infrastructure and a changing business workload, lack of context leads to a lack of end-to-end understanding, unexpected outages, inherently reactive operations and a process that is extremely difficult to predict.
Transforming workload management requires a shift from reactive to proactive
It’s a highly complex process to manage millions of batch jobs. Cross- and hierarchical dependencies, diversified holiday calendars due to geographic spread, and a lack of automated performance metrics on the job scheduler contribute to this situation. It worsens when enterprises have multiple batch job scheduler solutions. In addition, the need for business to stay relevant, agile and creative in the competitive market introduces more than a thousand changes to the profiles of the batch jobs each week.
It’s quite obvious how IT operations fall behind with the demanding nature of business driving changes in workload behaviour. Add to this the fact that performance benchmark reports are becoming irrelevant in light of increasing technology debt. A singular focus on incident management is misguided as well.
Timely and predictive processing of batch jobs is vital to ensure stable business operations and high-quality customer experience. A new solution is needed that helps move this from a reactive to a predictive approach, incorporating machine learning, AI and automation to deliver agile and autonomous batch operations. This would enable the proactive ability to both fix issues before they occur and enable scenario planning for optimized batch runs.
Toward a Smarter Solution
Batch systems have become increasingly complex in response to today’s business demands, but they have not been able to keep pace with the current speed of business. Technology debt holds organizations back from a system that truly meets their needs. Those needs shift on a daily basis, clearly demonstrating that a new system is required. As AI and automation prove their worth in more and more business applications, forward-thinking organizations will apply these technologies to their batch jobs to create predictable workload management that eliminates guesswork and unexpected downtime and serves all stakeholders better.
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