Automation Must Drive Data Analytics, Management to Conquer Complexity

As organizations tighten their belts, CIOs need tools that relieve the pressure on valued engineering talent. They need AI-powered automation.

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Many organizations face a painful squeeze on their IT resources in 2023.

An uncertain economic environment has organizations tightening budgets and implementing hiring freezes. Meanwhile, the long and lingering engineering talent shortage continues as organizations ask more from ITOps, DevOps, site reliability engineering (SRE), and security teams.

Further, unless something is done, companies will heap only more stress on their already-overworked IT people. For that reason, in the next year, organizations will increasingly adopt automation tools that eliminate the complexity, waste, and time-consuming chores of IT operations teams. Though automation is not new, a change in focus is coming, driven by the need to reduce engineers’ stress, enable them to work on more innovative projects, speed up reaction time to security and application issues, and make IT operations more cost-efficient.

The rise of advanced artificial intelligence (AI) and automation technologies will accelerate the erosion of the silos that separate observability, security, and business data. These solutions will provide real-time, accurate, and vital context for all this data and fuel the rapid generation of critical, data-backed insights.

Engineers spend too much time on operational weed-pulling

Consider the challenges confronting CIOs now.

In addition to a slumping economy, everything is digital, and software powers nearly all operations. While organizations once centralized these tasks in a single data center, they’re not even located in a single cloud anymore. We live in the era of multicloud and hybrid cloud architectures, with thousands of applications and millions of microservices — all producing mind-boggling amounts of information in the form of telemetry data, logs, and traces.

CIOs must create, operate, and secure all these services in a dynamic and complex multicloud environment — with fewer resources.

According to research, 71% of CIOs from the world’s largest organizations say all this data is beyond humans’ ability to manage. More than half say their teams may become overloaded if they don’t find a more automated approach to IT operations.

Against this backdrop, much of engineers’ time is wasted on operational weed-pulling. Data scientists and analysts spend 80% of their time preparing data and only 20% analyzing data. Cleaning, preparing, and making sense of data undermine the analytical work that pushes companies forward.

The end of ineffective war room meetings 

Then, there’s the time spent on war room meetings that occur anytime there’s a security incident or performance issue. Maybe an Amazon Web Services system goes down or the website is offline. Those events frequently bring together different teams: the application owner, IT operations, SRE, or security teams. They meet to try to identify the problem and its precise location in relation to events happening around the same time.

Finger-pointing is not unusual. Each team arrives with data from its own silos, reflecting different parts of the organization’s cloud ecosystem.

But none of these teams have a clear view of the problem because no one group has access to all the data. Additionally, too much of the fact-finding relies on manual, laborious processes. Is it any wonder why a problem that should take three or four hours to solve often takes two or three days?

And in the spirit of radical candor, taking too long to identify, recover, and provide answers is bad for IT teams’ reputations.

Teams need a platform that monitors infrastructure, applications, and the network

Still, what can CIOs and IT teams do to correct the situation? Although they find themselves saturated in data, they often have few ways to access the necessary information. They need insights from the data that precisely reveal a problem’s nature, location, and business impact so their teams know how to act. For issues that fall into predictable resolution patterns, even better is an auto-remediation workflow.

Today, making sense of events and incidents is too time-consuming and requires too many manual procedures, such as rehydrating and indexing data, because teams manage multiple data pools. All of this is too clunky and slow to respond to changes to applications or security threats.

IT leaders need a tool that monitors infrastructure, applications, and the network in real-time and at enterprise scale. They need the means to store and analyze all the organization’s metrics, events, logs, and trace data in a single location. To avoid alert storms and accelerate mean time to resolution (MTTR), they also need a way to preserve the relationships in the context of the user experience, the business events, and topology. They can’t just dump the data — unrefined and without context — into cheap storage because this frequently creates more problems than it solves.

Organizations need a data management and analytics solution that is efficient, contextual, and enables deep analytics when they need it. Otherwise, it’s like removing all the columns in a spreadsheet and then trying to correlate an event. Nothing makes sense.

And just imagine if observability meant using one or two solutions instead of 12 or more tools.

Consider why organizations have all these different slices of data locked in different tools that don’t communicate with one another. Whatever the reason, it’s not as important as preserving teams’ time — time to create and drive your innovation agenda — instead of wrestling with operational complexity. Find a solution that enables teams to place all the observability, security, and business data in a single location and then see how it simplifies and accelerates innovation.

Adrian Phillips

Adrian Phillips is the Director of Product Marketing for Dynatrace.


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Adrian Phillips
Adrian Phillips is the Director of Product Marketing for Dynatrace.