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The 1 Lesson Missing From All This CrowdStrike Chatter

Split - 1 Lesson Missed From the CrowdStrike Drama

It’s been a week since the CrowdStrike incident, and the hefty $5.4 Billion cost to Fortune 500 Companies is putting enterprises on high alert. By now, everyone’s talking about how enterprises should revise their software rollout strategies. This includes canary deployments and progressive delivery with feature flags, so teams can limit the blast radius of issues and rollback to safety. We wholeheartedly agree with these strategies. However, we were surprised that nobody is talking about this one thing.

Release Monitoring: the One Best Practice Nobody Mentions

Even though gradual rollouts can limit the blast radius of release issues and feature flags do make it easier to quickly shut off a problem once detected, what’s missing from the CrowdStrike conversation is how to go about quickly detecting issues and knowing exactly which update is causing problems. Progressive delivery can’t really help you if you don’t have a way to quickly and accurately detect the impact of each change in your release.  “Did a feature create a negative impact on the user experience? Does an update have the potential to tank one of your latency metrics?”  Questions like these are critical and release monitoring can answer them.

Progressive Delivery Without Monitoring Is a “Press and Pray” Approach

If you’re doing progressive delivery with feature flags and don’t have a way to quickly observe the impact of your releases at each stage of a gradual rollout, you’re not getting much value from it. Requiring your team to halt everything and manually look for warning signs across numerous gradual rollouts is only adding risks and costs. In most cases, teams won’t have the space for the hyper-vigilance releases need. 

So how do you capture and make sense of the volume of data behind every feature flag that’s released in every gradual rollout? How do you do that at an enterprise with over 500 engineers managing numerous rollouts at different stages in production?

Get the Right Tools in Place

Application monitoring tools are integral to the overall health of a software’s infrastructure. However, enterprise teams can’t rely on them alone to catch feature-level issues as early degradations tend to fly under the radar in a gradual rollout. In progressive delivery, teams need release monitoring that can identify the cause and location of a feature-related issue when and where issues emerge.

To achieve measurability with every rollout, that takes a data-driven feature management solution. We recommend one that watches performance and behavioral metrics for every feature flag that’s released. One that’s complemented by automatic alerts to instantly triage anomalies as soon as they’re detected. Nobody does this better than Split.io, and it’s a big reason why Harness just acquired the company back in May. 

A Gradual Rollout Shouldn’t Mean a Slow Rollout

In the delicate dance between caution and speed, companies should resist the urge to pump the brakes on rollouts due to fear. This is a common reaction from software teams, and it will only be compounded by the recent CrowdStrike fiasco. So, before you decide to freeze your releases, or add extra cycles, know that slowing down can actually expose you to further risks. Be sure you have the right tools and processes in place. Best practices says: canary deployments and progressive delivery with feature flags to limit the blast radius, automated pipelines to ensure that no software test is missed, and last but not least: proper release monitoring to ensure you catch releases issues automatically. After all, a gradual rollout doesn’t have to be a slow one that opens your enterprise up to new risks. 

About Split

Split Feature Management and Experimentation, recently acquired by Harness, gives you the confidence to move fast without breaking things. Set up feature flags and safely deploy to production, controlling who sees which features and when. Connect every flag to contextual data, so you can know if your features are making things better or worse and act without hesitation. Effortlessly conduct feature experiments like A/B tests without slowing down. Whether you’re looking to increase your releases, to decrease your MTTR, or to ignite your dev team without burning them out–Split is a way to revolutionize the way the work gets done.  Switch on a free account today or Schedule a demo to learn more.

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