In today’s real-time, instant gratification business world, it might surprise some that underlying many of the systems and applications that provide this real-time reality are job scheduling and workload automation systems. These systems manage the critical feeds and processing to ensure that real-time applications support the business and its customers with accurate and relevant information. These critical workload processes support everything from financial market transactions to the placement and shipment of customer orders. Its mind boggling how complex and important these processes are, yet very few people are aware of the role these systems play until something fails, or doesn’t execute properly or on time.
Workload automation products and solutions come in many flavors from many vendors. Larger enterprises predominantly run the “Big 3” vendors’ products. Surprisingly, they don’t run just one vendor’s solution. Often, teams are running multiple products from different vendors as well as various specialty solutions from smaller vendors to meet the specific needs of certain applications. This adds to the complexity of these environments and the costs associated with managing them as the business process spans multiple systems and therefore multiple scheduling tools.
Although these workload solutions provide monitoring capabilities for the jobs that are in flight or scheduled, they do not provide an analytical approach to managing, monitoring, and optimizing the workload under their control based on the business processes that are being run. This is where workload analytics comes into play.
While virtually every area of your business and most areas of IT are embracing analytics, the area of workload automation does not. Some use historical run data to develop trends, however, this is only an after-the-fact Band-Aid that can tell you that you have already failed or that you might somewhere eventually fail. It cannot point out specific future problem areas or provide you with accurate predictions for processing times and capacity utilization. With analytics, problem resolution for long-running or failed processing is much easier. These analytics can reduce mean time to repair by up to 99%, and they can limit the amount of human capital required to diagnose a problem. Workload analytics can also help developers and engineers build better workload processes and redesign current processes for more efficiency and higher reliability. We have seen many teams avoid the needed re-engineering of their current workload processing because it is too complex, has no documentation, or the original developers have moved on. Analytics help to dissect your workload, understand how it works, and target optimization efforts.
Having a specialized analytics solution for workloads enables companies to improve their existing service deliveries and to get enough lead-time to spot potential SLA breaches and actually do something about them before they affect the business. These solutions also help teams gain insights into their complex systems, so they can drive continual improvement in IT operations.