The Business Leader’s Guide to Predictive SLA Management- Part 2

Rahul Pandey

By – Somdipto Ghosh

(Senior Product Marketing Manager | Digitate)

Leveraging AI to Ensure Timely Operations

Our last blog talked about the importance of meeting Business Service Level Agreements (SLAs) and why business leaders need to ensure that the key IT processes such as Workload Automation are executed without disrupting any important business functions.

A recent study by EMA highlighted the increasing demand for Workload Analytics to meet various requirements such as capacity analytics, workflow optimization suggestions, SLA monitoring, root cause analytics, and proactive warnings. The report also mentioned that 34% of respondents used Workload Automation (WLA) specific analytics solutions, while 26% said general-use analytics solutions for Workload Management.

To be effective in ensuring smooth Digital Operations, a Workload Analytics solution needs to meet two very critical requirements:

1. Capture a wide blueprint across the Workload Ecosystem

Most Workload Analytics solutions are created to provide visibility across a specific set of tools. While these specialized solutions may meet the need of IT teams managing specific automation providers (schedulers), from a business point of view, this strategy is not an effective one. Modern business processes depend on Workload Ecosystems that are very complex, with interdependencies between various Workload Automation tools, in-house-developed batch jobs, custom schedulers, legacy schedulers, and various hardware and network systems. Due to the interconnected nature of operations, a failure at any of these layers may cause missing an SLA (which can lead to penalties or lost business). Scheduler-specific analytic solutions are often unable to detect this problem as it falls outside their scope and ability to monitor.

Digitate has a long history of working with customers across industries, and this was a common point of concern with every big or small organization. Hence, Digitate recently launched Business SLA Prediction – a conveniently packaged solution to help businesses fill this gap by creating a 360-degree blueprint covering multiple schedulers, in-house jobs, and business workloads used by the batch jobs, as well as data from historic runs of the batch jobs. It then uses this detailed blueprint and continuously monitors the entire ecosystem, giving both IT and business leaders 360-degree visibility for key SLAs.

2. AI-based intelligence to adapt to a changing ecosystem

The IT ecosystem is rapidly evolving with the frequent addition of new applications or changes in infrastructure, so workload processes need to be realigned periodically to meet the changing needs.

Workload processes are also dynamic as the volume of workloads, or batch jobs may differ in day-to-day operations, making it difficult to rely on static benchmarks to know if the processes are running smoothly. Hence, the analytical tools using only past benchmarks cannot accurately predict future needs and often produce wrong inferences from data, which can be disastrous for the business function.

To address this, organizations need a more intelligent analytics system that can leverage AI-based models and real-time data to provide accurate information. Accurate predictions are a crucial necessity for meeting business SLAs. Last-minute detection of batch failures leaves little time for resolutions – especially in complicated IT ecosystems, posing significant risks for business continuity. That’s why modern workload operations focus on being proactive, which requires early lookaheads for any potential SLA misses. It is also worth noting that every delay or failure in a workload process may not result in an SLA miss; teams often find it challenging to prioritize actions as it requires in-depth understanding to know which issue to prioritize and how soon the solution is needed, to ensure no downstream SLA is missed.

Digitate’s Business SLA Prediction is specifically designed to meet these requirements head-on. It leverages domain-specific AI/ML models for workload processes to predict a batch system with high accuracy and adapt to expected/unexpected changes. The solution monitors the entire ecosystem for any hidden process anomaly. With every anomaly, it can

  • Perform detailed Impact Analysis and assess SLAs at risk
  • Pinpoint the root cause of an observed/predicted SLA violation
  • Recommend corrective actions to prevent an SLA violation
  • Recommend the desired time by which the failure should be fixed to minimize any adverse impact.

The solution also continuously learns, incorporating user feedback to provide more preferred actions to solve potential SLA violations. So, for business leaders looking to safeguard their critical business functions against any failure in IT workload processes, AI-based analytics is an absolute necessity. With the right solution, business leaders not only get better visibility, but they also get better assurance that the business SLAs will be met. This helps coordinate with various IT teams to resolve issues as the solution alerts all relevant stakeholders and provides a detailed analysis of causes and solutions.

To find out how Digitate Business SLA Prediction solution helps business leaders ensure SLA Compliance with predictive analytics, click here. 

Related posts

Leave a Reply