Key Industry Trends That Are Reshaping IT Operations in 2018 - Evanios

Key Industry Trends That Are Reshaping IT Operations in 2018


Today, IT operations is at a crossroads. Business demands are escalating, as enterprises pursue aggressive digitalization strategies. It’s no longer enough to keep the lights on – IT operations is expected to align with and enable business priorities. And yet, operational challenges continue to increase. The complexity of applications is growing rapidly, application architectures are morphing as they move to the cloud, and agility is at a premium. Yesterday’s operational models are no longer enough. Instead, IT operations is faced with a rapidly evolving landscape that demands new solutions.

Here is a look at some of the key industry trends that are reshaping IT operations.

 

ITOM and ITSM Convergence

Traditionally, ITSM and ITOM have been separate. Yes, events led to incidents, but the information flow between ITOM and ITSM was primarily limited to event data and infrastructure context. Event management tools had no visibility of ITSM data since ITSM was an opaque downstream system. As a result, event management lacked business, service and operational context. For instance, there was no way to understand the business impact of events, so there was no meaningful way to prioritize them. Root cause analysis is another example – when a critical event occurred, event management systems had no history of how similar issues were diagnosed and resolved in the past.

Today, vendors like Evanios are breaking down these barriers between ITSM and ITOM. Leading-edge event management systems now have access to ITSM’s rich set of business and operational data – ranging from historical incident data and service dependency maps to change requests. This is shifting IT operations to the left, with event management tools now able to accurately rank and diagnose service issues before incidents are opened – significantly accelerating MTTR while reducing the burden on IT operations staff.

 

Machine Learning

As digitalization accelerates, the pace of change is also increasing exponentially. With DevOps, application delivery is growing rapidly, but IT operations is struggling to keep up. The velocity and volume of data is now becoming overwhelming, as is the cost of failure. For example, a 2017 ITIC study found that 33% of enterprises said application downtime cost at least $1 million an hour. IT operations teams face a deluge of operational noise, escalating demands and static budgets – and they are increasingly looking to artificial intelligence as the solution.

According to Gartner, 40% of all large enterprises will combine machine learning and big data to augment IT operations by 2022. This is what Gartner refers to as AIOps – or Artificial Intelligence for IT Operations By applying machine learning across the operational lifecycle, IT operations teams can now derive real-time insights into IT services, infrastructure, and processes. This includes areas such as predicting service outages, ranking events based on business impact, automating root cause analysis, and accelerating remediation based on intelligent recommendations.

 

Containers and Microservices

As development continues to increase agility and adopt the cloud, containers and microservices are rapidly gaining momentum. By breaking down large software projects into independent microservices, development organizations can accelerate development, right-size teams, enhance application resilience, and increase reuse. Containers complement this by enhancing deployability, shielding microservices from OS dependencies as they transition from development/test to production environments.

However, microservices and containers increase operational complexity. The number of interconnected components increases dramatically, as does the volume of monitoring data. Siloed monitoring approaches – including standalone APM tools – will no longer work. Instead, IT operations needs to invest in solutions that automatically integrate, correlate and analyze monitoring data at scale, synthesizing point data from multiple monitoring tools into end-to-end service visibility.

 

IoT and Edge Computing

The Internet of Things is now a reality, not future hype. According to IHS Markit, the number of IoT devices was set to reach 20 billion in 2017, and that growth is continuing unabated. That includes 3.6 billion industrial devices, the most rapidly growing IoT sector. The upshot is that enterprises are leveraging the IoT at a dizzying pace, and all of these IoT devices need to be managed. And, it’s not just about devices – the IoT is changing the nature of IT environments. Edge computing is a prime example of this, with corporations looking to move IoT data processing into micro-data centers located close to IoT device concentrations, rather than backhauling data into a centralized cloud environment.

That places an enormous burden on IT operations, as data volumes skyrocket and complexity accelerates. It’s no longer about managing centralized applications and services. Instead, we are moving to a massively distributed IT infrastructure – one that can’t be managed with today’s manual monitoring processes. Instead, IT operations need to invest in automated event management platforms that scale to the challenge. Otherwise, IT operations will become the primary bottleneck that limits IoT innovation.

If you’d like to see how Evanios can help you handle these (and other) IT Operations trends, you are welcome to contact us for a demo.

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