By Akhilesh Tripathi, Digitate
Whether it’s an average Wednesday or Black Friday, your online and brick-and-mortar stores need to run like well-oiled machines. With payment gateways, warehouse management applications and point of sales transaction, managing all this activity is a tall order.
But it’s a must. That’s why the timely and predictive processing of batch jobs is vital to ensure stable business operations and high-quality customer experience. Workload management is the key to making your retail organization – and the individuals within it – more efficient and productive. But it often isn’t being done right or strategically. How can retailers improve workload management in a way that makes it a business enabler?
Workload Management: Necessary But Complex
Workload management is one of the primary, crucial processes in an IT set-up, as batch systems prepare the business to run. For instance, 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.
Though using batch systems is a highly effective way to lower a company’s costs while also increasing employee efficiency, 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.
Cognitive Context Has Been Lacking
Retailers are often on the leading edge of technology adoption as they seek to remedy thorny problems, but they often operate without knowing how to apply their technology appropriately for their circumstances. 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.
Companies absolutely need some way to forecast how the workload system is going to perform 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 behavior analysis, coupled with a context-aware, self-triaging and self-healing mechanism.
The sticking point regarding most workload management systems available today is this: 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.
The Need For Proactive Workload Management
Handling millions of batch jobs is extremely complicated. 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 companies 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.
Clearly, as business demands spur changes in workload behavior, IT operations is going to fall behind. 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.
To make sure that customers have a high-quality experience and that business operations remain stable, timely and predictive processing of batch jobs is essential. 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.
A Stable And Resilient Strategy
Workload management is critical to retailers’ success, but complexity has become the enemy as batch systems have been unable to match the speed of today’s business demands. The old way of doing things, and the old systems used to do them, won’t work anymore. Batch operations need a paradigm shift from reactive to proactive, and AI and automation are helpful partners in this shift. These technologies help make your IT backbone stable and resilient in the face of constant change, enabling predictable workload management that frees up resources from constant firefighting so you can focus on optimizing your systems and preparing them for growth.
About The Author
Akhilesh Tripathi is the global head of Digitate, a software venture of Tata Consultancy Services. He has been a driving force since the venture’s inception and is critical to global revenue generation and service delivery. Previously, as the head of TCS Canada, Akhilesh drove the Canadian entity to be among the top 10 IT services company in its market. His 23-year career with TCS also includes his role as the head of enterprise solutions and technology practices for TCS North America. In that role, he led the management of strategic alliances with software vendors and participated on the advisory councils of several strategic vendor partners.