The Business Leader’s Guide to Predictive SLA Management- Part 1

Rahul Pandey

By – Somdipto Ghosh

(Senior Product Marketing Manager | Digitate)

Understanding the Need

The COVID-19 pandemic has had an astounding impact in terms of promoting digital business processes and making us accustomed to a digital-first experience.

In the last 2 years, we have opened accounts and used banking services without stepping foot in a bank. We have switched jobs or worked on complex new projects without ever visiting the office premises. We have consulted doctors without going to a hospital and have enjoyed food from our favorite restaurants without stepping out of the house; and somewhere down the line, we have become so accustomed to this digital life. The very thought of doing any of these things the old way is often uncomfortable and fraught with uncertainties.

This has also changed the role of business teams. Every business leader now understands that the key to recovery and success in the post-pandemic era is effective digitalization. However, with increased technology adoption comes amplified business risks from IT.

Every Business is now a Digital Business

Every business process is adopting technology as a competitive differentiator to help improve customer experience and empower teams. This has enhanced the business leader’s role and interest in managing IT technology and given shape to new roles such as that of business technologists. Business leaders are now more involved in ensuring smooth IT operations, leading the deployment, management of digital tools and applications involving their business processes.

A key part of this is defining and managing business deliverables and Service Level Agreements (SLAs) ensuring business process requirements and dependencies are being met. This means business technologists need to be actively involved in ensuring that any failure in the underlying technologies and IT processes does not impact the business SLAs. Workload automation continues to be the most widely used technology in most organizations. It’s responsible for the largest chunk of IT processes that meet business SLAs and keep the business running.

Missing the Business SLAs can have multiple implications

In the last year itself, there have been several instances of companies being affected by workload automation failures. This has far-reaching impacts that go beyond the scope of just IT teams. Many business SLAs are created to meet regulatory compliance requirements, and any delay or failure to meet them can attract huge penalties. For example, most banking organizations need to share daily transaction reports with auditing institutions; failure to do so can be a very costly affair.

Business SLAs linked to the customer journey can impact sales and customer loyalty. Imagine in a retail environment what might happen if stores are not replenished on time, or price changes are not reflected in the POS systems. Failure to meet business SLAs can also create reputational risks, like what may happen if annual financial reports are not published on time, or if payroll processing gets delayed and employees are not paid.

Some business SLAs can have life-threatening consequences. Industries such as aviation and healthcare rely heavily on timely data availability. Failure to access accurate information, or any process failure in an air-traffic-control system can lead to disasters.

Business Automation brings its own set of Challenges

As organizations develop and deploy new tools, every new deployment increases operational risk, as increasing computing requirements, and added dependencies, produce a higher risk of an IT failure impacting business processes.

The challenge is two-fold.

First is the risk of change. Any change in the existing workload management ecosystem (be it application modernization, new application deployment, or infrastructure change) can trigger unforeseen challenges, impacting the entire ecosystem.

Secondly, the focus on optimal use of available resources is crucial. This has inherent risks which need mitigation. Most applications and automation now work in parallel and try to make the best use of the available computing resources. Any process anomaly, delay in start time, or volume change can negatively impact other processes, creating a cascade that causes outages of critical business processes.

It is very difficult to identify the specific failures which may have a business impact, as compared to those that may not impact the end business at all. That’s why business SLA management is a continuous and complex activity. Organizations need to continuously monitor the entire workload ecosystem and assess what any of the IT process failures mean for their business processes.

Additionally, failure resolution is not always straightforward and requires a deep understanding of the IT ecosystem as well as clear visibility into real-time status. Often the failure is noticed too late to take any corrective actions.

The need for Predictive Intelligence

While business teams are now responsible for ensuring SLAs are met, they need to collaborate with various IT teams to detect and resolve issues in the IT processes. Such collaborations take time, and if the processes impact key business SLAs, relying on reactive processes where the issue is first detected, then triaged, and finally resolved with help of multiple teams is a risk to be avoided.

Business leaders and technologists need predictive diagnostics, which can minimize downtime. They also need a 360-degree view into each layer of IT operations that impacts their business SLAs to reduce dependence on IT teams. More importantly, they need timely information that is intelligent and notifies in advance any IT issue and its potential impact. Thus giving sufficient time to deal with it without the risk of impacting business. Fortunately, Artificial Intelligence (AI) and Machine Learning (ML) have finally evolved to the point of offering this kind of predictive insight and are a good fit to meet most of these challenges.

We’ll explore the role of AI and ML in predictive SLA management in a future blog post.

Additionally, by adopting an intelligent solution for monitoring, business teams can reduce their dependence on resource-intensive manual monitoring. This helps leverage the teams more for strategic projects in adopting technology for business, without needing to worry about availability.

To find out how the Digitate Business SLA Prediction solution uses predictive analytics to help business leaders ensure SLA compliance, click here.

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