Gabby Nizri is the CEO of Ayehu, Inc.
It is coming. We have been fully warned that a massive shortage of qualified technology professionals is going to quickly become our reality. But thanks to the increasing abundance of available data, along with innovative tools for gathering, processing, deciphering and storing that data, machine learning algorithms can now be used to produce fast, affordable results for businesses of every size and industry.
Automation and machine learning have become force multipliers and game changers for many organizations because it means that they are no longer constrained by data center staff or budget limitations. There are some basic yet unavoidable factors are driving the need for these new technologies, but fortunately technology leaders today have options to solve today’s most pressing challenges.
A Glaring Need
Today’s data center professionals are under increasing pressure to accomplish more with fewer and fewer resources. Increasing amounts of available information make manual data management a full-time endeavor. For those operations with limited staff or smaller budgets, finding extra support can be an uphill battle. Furthermore, customer demand and an increasingly competitive landscape have placed significant burdens on IT leaders.
Automation’s Role
IT automation technology facilitates a shift from human (manual) processes to machine, enabling the agentless execution of critical data center tasks and workflows, including but not limited to:
· Monitoring – Continuously scan and track the status of data center components with automated notification and escalation in the event of a problem
· Maintenance – Automatic performance of ongoing functions, such as updates and patches
· Scheduling – Organize the automatic execution of routine data center processes, including backups, downloads/uploads, replication, application events and more without the need for human effort
· Provisioning and Configuration – Development, testing and deployment of new applications, including physical, virtual and cloud servers
· Application Service Delivery – Automatic fulfillment of user requests within just minutes
· Optimized Workload Delivery – Automatic movement of network traffic for better load balancing and/or on-demand delivery of additional applications
· Security – Automate the incident response process from start to finish, creating a closed loop to quickly detect and address threats, mitigating damages
· Compliance – Audit and report on live configurations, compare current and past configurations to identify discrepancies and implement rules-based policies to adhere to regulatory standards – all without the need for human intervention
By adopting automation technology, data center professionals are no longer burdened with the challenge of performing many of their duties without disrupting end-users. Previously, things like system refreshes and updates had to be carried out after hours to avoid a potential service interruption. Now, these workflows can be scheduled and executed automatically, alleviating the need for IT staff members to work odd hours.
Automation + Intelligence
Beyond the basics of data center automation, some of today’s platforms are now incorporating intelligent technologies, like machine learning, into the mix. So, not only can IT teams streamline operations by automating manual processes, but they can now rely on the power of artificial intelligence to achieve continuous process improvement.
Automated platforms powered by sophisticated machine learning algorithms, use available data to dynamically create rule-based recommendations, insights and correlations. This enables better data-driven decision making and facilitates more optimized workflows and processes. As a result, not only does the data center itself run more efficiently, but the enhanced service that occurs as a result drives greater customer and employee satisfaction rates. In other words, everyone benefits.
Considerations for IT Leaders
Given the advantages automation can have for the data center, it can be tempting to automate everything. However, IT leaders are better served to take a more comprehensive approach to data center automation. When planning for automation, be sure to consider the following:
Automate intelligently. That is, don’t just automate processes for the sake of it. Instead, processes and workflows should be evaluated and weighed based on each one’s business value as well as the quantifiable outcomes that could be realized as a result of automation.
Practice in with the new and out with the old. Whenever possible, focus should be placed on automating newer processes and workflows first rather than trying to adapt old processes to automation – unless, of course, significant ROI could be realized by changing the existing workflows.
Start small and work from there. An incremental approach to automation is recommended, as it’s always easier to automate basic functions than it is to tackle large scale projects right off the bat. Start with smaller initiatives that can be built upon.
Test first, then deploy. It’s always advisable to experiment with automation changes in a test or QA environment before going live with them. In the absence of a test environment, look for ways to deploy automation incrementally rather than exposing the entire data center to a complete automation change.
There are many cases for which automation can and should be considered thanks to the rapidly evolving technology landscape. For example, IoT deployments are steadily on the rise and adding to the complexity of the data center, making the role of data management in business even more crucial. And increasingly IT teams need the ability to leverage the power of data to meet enterprise-wide objectives, but may lack the qualified staff and resources needed to produce what is needed. When applied appropriately and strategically, automation technology has the potential to empower data centers – even those with limited resources – to perform at the highest level and yield greater outcomes than were ever thought possible.
Opinions expressed in the article above do not necessarily reflect the opinions of Data Center Knowledge and Informa.
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