Business growth and network expansion happens in tandem, bringing forth an intricate web of connections, potentially leading to challenges in efficient network management and the looming threat of unexpected outages. The repercussions of such outages extend beyond operational disruptions, incurring huge costs for organisations.
Information technology (IT) departments are always juggling multiple tasks in the fast-paced world of modern business, from maintaining the network infrastructure to guaranteeing data security and enhancing system performance. Even the most seasoned IT professionals can get overwhelmed by the sheer volume of data created and the rising complexity of IT systems.
In this digital era, with the explosion of data and increasing complexity of IT infrastructures, organisations are struggling to manage their IT operations. By leveraging the cumulative power of AI and ML, AI for IT operations (AIOps) can ease the burden and help enterprises achieve a proactive operational state.
To deliver uninterrupted services to users and ensure that business remains up and running, it’s important to eliminate outages or downtime. While this seems simple and plausible in theory, in reality, businesses are often grappling with service disruptions due to unpredictable network failure, issues with infrastructure components, human errors, or natural disasters.
When business operations expand and mature, IT teams face an increased demand for technology that targets specialised functional areas. This sees the once-small IT teams evolving to operate as several sub-departments that co-exist with each other.
In part one of this e-book, we talked about ManageEngine’s journey, business model, and customer-driven culture. In part two, we’ll talk about the steps we’ve taken to strengthen our technology, our growing IT infrastructure, the teams and processes working behind the scenes, and how we cultivate deeper relationships with the people who have been a part of this two-decade long journey.
This e-book is a transformation story of ManageEngine from an ambitious start-up to a successful enterprise. We’ll cover ManageEngine’s inception story, the iterative journey of our business models (including the mistakes we made and the lessons we learned).
The field of machine learning is a hot topic. Processing online search requests, filtering spam automatically out of our email inboxes and understanding and replying to speech commands on smartphones are all machine-learning tasks being done on a daily basis.
“If you are an IT help desk technician, you’re a superhero!” You may have heard this many times from your help desk solution provider, if not from your customers and colleagues. Do you believe this notion? Do you really think you can be a superhero? Well, I believe you can.
Currently, the automation that operates within our service desk tools is static, has very little built-in intelligence, and requires periodic human intervention to correct course over time. However, things will change as smart automation is enabled through machine learning applications.
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