Four Ways to Survive the IT Operations Big Data Deluge

Four Ways to Survive the IT Operations Big Data Deluge

Four Ways to Survive the IT Operations Big Data Deluge

The days of managing monolithic style applications running on a single platform are over. Organizations are now committed to delivering their customers a far richer variety of digital services using multiple channels. This means applications are more likely to execute from the cloud, via a multitude of microservices interacting with virtualized resources, containers and software-defined networks.

In this new normal, teams can no longer afford to get bogged down with reactive fire-fighting and lengthy war room sessions. But with so many moving parts, increased application complexity, and dizzying rates of software delivery, what strategies can IT operations employ to prevent being wiped-out by waves of operational big data?

The traditional approach is to buy more monitoring tools; one for every new wave of technology adopted. But this doesn’t scale, negatively impacts margins, and only provides narrow views into the all-important customer experience. So putting tools aside, where should organizations turn?

Well, to the data for starters – or more importantly, to the business problems and opportunities they solve and uncover.

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This is hardly an epiphany. Web scale companies understand implicitly the importance of data and gleaning valuable insights. By developing analytics-driven applications, implementing at scale and democratizing usage, these businesses continuously raise the bar in terms of productivity, agility and customer engagement.

In a DevOps context these businesses thrive because their IT teams are equally analytics-driven. Not only do they surpass today’s expectations for delivery speed and quality, they leverage data insights to drive improvements at every stage of the digital service continuum. So before perusing the extensive tools catalog, stop and consider four higher value strategies.

Many teams will collect masses data points; however, what characterizes a strong analytics-driven culture is a focus on collectively leveraging metrics for the benefit of the business as a whole. In a pinch this will involve:

Fast, real-time action or recommendations when insights are uncovered. Nothing demotivates teams faster than finding something valuable and then not being able to act on it.

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Analytics has limited value when only used by IT operations to support their daily grind. Better methods and techniques treat data as enterprise asset many teams can use, share and leverage in a variety of different contexts.



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