Optimizing ITOps for Digital Transformation

The key focus of Digital Transformation is removing procedural bottlenecks and bending the curve on productivity. As Chief Insights Officer, Forbes Media says, Digital Transformation is now “essential for corporate survival”.

Emerging technologies are enabling dramatic innovations in IT infrastructure and operations. It is no longer just about hardware, software, data centers, the cloud or the service desk; it is about backing business strategies. So, here are some reasons why companies should think about redesigning their IT services to embrace digital disruption.

DevOps for Agility

As companies move away from the traditional Waterfall model of software development and adopt Agile methodologies, IT infrastructure and operations also need to become agile and malleable. Agility has become indispensible to stay competitive in this era of dynamism and constant change. What started off as a set of software development methodologies has now permeated all aspects of an organization, ITOps being one of them. Development, QA and IT teams need to come out of their silos and work in tandem for constant productive collaboration, in what is termed DevOps.

Shorter development & deployment cycles have necessitated overall ITOps efficiency and among other things, IT enviroment provisioning to be on-demand and self-service. Provisioning needs to be automated and built into the CI/CD pipeline.  

Downtime Mitigation

With agility being the org-wide mantra, predictable IT uptime becomes a mandate. Outages incur a very high cost and adversely affect the pace of innovation. The average cost of unplanned application downtime for Fortune 1000 companies is anywhere between $1.25 billion to $2.5 billion, says a report by DevOps.com. It further goes on to say that, infrastructure failure can cost the bottom line $100,000/hr and the cost of critical application failure is $500,000 to $1 million/hr.

ITOps must stay ahead of the game by eliminating outdated legacy systems, tools, technologies and workflows. End-to-end automation is key. IT needs to modernize its stack by zeroing-in on tools for Discovery of the complete IT landscape, Monitoring of devices, Analytics for noise reduction and event correlation, AI-based tools for RCA, incident Prediction and Auto-Remediation. All of this intelligent automation will help proactive response rather than a reactive response after the fact, when the damage has already been done.

Moving away from the shadows

Shadow IT, the use of technology outside the IT purview, is becoming a tacitly approved aspect of most modern enterprises. It is a result of proliferation of technology and the cloud offering easy access to applications and storage. Users of Shadow IT systems bypass the IT approval and provisioning process to use unauthorized technology, without the consent of the IT department. There are huge security and compliance risks waiting to happen if this sprawling syndrome is not reined in. To bring Shadow IT under control, the IT dept must first know about it. This is where automated Discovery tools bring in a lot of value by automating the process of application discovery and topology mapping.

Moving towards Hybrid IT

Hybrid IT means the use of an optimal, cost-effective mix of public & private clouds and on-premise systems that enable an infrastructure that is dynamic, on-demand, scalable, and composable. IT spend on datacenters is seeing a downward trend. Most organizations are thinking beyond traditional datacentres to options in the cloud. Colocation is an important consideration since it delivers better availability, energy and time savings, scalability and reduces the impact of network latency. Organizations are only keeping mission-critical processes that require close monitoring & control, on-premise.

Edge computing

Gartner defines edge computing as solutions that facilitate data processing at or near the source of data generation. With huge volumes of data being churned out at rapid rates, for instance by monitoring or IoT devices, it is highly inefficient to stream all this data to a centralized datacenter or cloud for processing. Organizations now understand the value in a decentralized approach to address modern digital infrastructure needs. Edge computing serves as the decentralized extension of the datacenter/cloud and addresses the need for localized computing power.

CyberSecurity

Cyber attacks are on the rise and securing networks and protecting data is posing big challenges. With Hybrid IT, IoT, Edge computing etc, extension of the IT footprint beyond secure enterprise boundaries has increased the number of attack target points manifold. IT teams need to be well versed with the nuances of security set-up in different cloud vendor environments. There is a lot of ambiguity in ownership of data integrity, in the wake of data being spread across on-premise, cloud environments, shared workstations and virtual machines. With Hybrid IT deployments, a comprehensive security plan regardless of the data’s location has gained paramount importance.

Upskilling IT Teams

With blurring lines between Dev and IT, there is increasing demand for IT professionals equipped with a broad range of cross-functional skills in addition to core IT competencies. With constant emergence of new technologies, there is usually not much clarity on the exact skillsets required by the IT team in an organization. More than expertise in one specific area, IT teams need to be open to continuous learning to adapt to changing IT environments, to close the skills gap and support their organization’s Digital Transformation goals.

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AIOps – IT Infrastructure Services for the Digital Age

The IT infrastructure services landscape is undergoing a significant shift, driven by digitalization. As focus shifts from cost efficiency to digital enablement, organizations need to re-imagine the IT infrastructure services model to deliver the necessary back-end agility, flexibility, and fluidity. Automation, analytics, and Artificial Intelligence (AI) – comprising the “codifying elements” for driving AIOps – help drive this desired level of adaptability within IT infrastructure services. Automation, analytics, and AI – which together comprise the “codifying elements” for driving AIOps– help drive the desired level of adaptiveness within IT infrastructure services. Intelligent automation, leveraging analytics and ML, embeds powerful, real-time business and user context and autonomy into IT infrastructure services. Intelligent automation has made inroads in enterprises in the last two to three years, backed by a rapid proliferation and maturation of solutions in the market.

Artificial Intelligence Operations (AIOps) . Everest Group 2018 Report . IT Infrastructure

Benefits of codification of IT infrastructure services

Progressive leverage of analytics and AI, to drive an AIOps strategy, enables the introduction of a broader and more complex set of operational use cases into IT infrastructure services automation. As adoption levels scale and processes become orchestrated, the benefits potentially expand beyond cost savings to offer exponential value around user experience enrichment, services agility and availability, and operations resilience. Intelligent automation helps maximize value from IT infrastructure services by:

  1. Improving the end-user experience through contextual and personalized support
  2. Driving faster resolution of known/identified incidents leveraging existing knowledge, intelligent diagnosis, and reusable, automated workflows
  3. Avoiding potential incidents and improving business systems performance through contextual learning (i.e., based on relationships among systems), proactive health monitoring and anomaly detection, and preemptive healing

Although the benefits of intelligent automation are manifold, enterprises are yet to realize commensurate advantage from investments in infrastructure services codification. Siloed adoption, lack of well-defined change management processes, and poor governance are some of the key barriers to achieving the expected value.  The design should involve an optimal level of human effort/intervention targeted primarily at training, governing, and enhancing the system, rather than executing routine, voluminous tasks.  A phased adoption of automation, analytics, and AI within IT infrastructure services has the potential to offer exponential business value. However, to realize the full potential of codification, enterprises need to embrace a lean operating model, underpinned by a technology-agnostic platform. The platform should embed the codifying elements within a tightly integrated infrastructure services ecosystem with end-to-end workflow orchestration and resolution.

The market today has a wide choice of AIOps solutions, but the onus is on enterprises to select the right set of tools / technologies that align with their overall codification strategy.

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