Observability through AIOps enables developers to find problems faster by cutting through the noise
Years and years ago, a little-known entrepreneur named Mark Zuckerberg uttered the phrase, “Move fast and break things,” to his development team. Since then, dev teams have stuck to this “move fast and break things” mantra, where fast development far outweighs dealing with a few bugs down the road. Under this mindset, finding and fixing problems is essentially the last step in the development process.
The Problem With Breaking Things
This way of working has served dev teams well over the years as consumers continue to demand speedier innovation. But as end user expectations around deliverability speed rise, so do expectations around service reliability. Customers now want better products faster. Not only does the “break things” way of development lead to extra work for the dev teams down the road, but it also degrades customer trust over time as issues, from a buggy user experience to major security concerns, pop up once the product is already shipped.
Even Zuckerberg realized this years later, admitting, “What we realized over time is that it wasn’t helping us to move faster because we had to slow down to fix these bugs and it wasn’t improving our speed.” He realized this so much so that the Facebook team officially changed their mantra to “move fast with stable infra” in 2014. Sure, Zuckerberg shouldn’t be our role model after everything that has happened, but if he can realize the need for stable infrastructure, so should we.
So, how do dev teams meet growing end user expectations while cleaning up the now hurried development process for more stable systems?
Observability With AIOps and the Development Process
This is where observability strengthened by AIOps (artificial intelligence for IT operations) comes into play. Observability—or simply making data readily available from a system that you want to monitor—enhanced through AIOps allows dev teams to automate their monitoring. This automation can include applying AI/ML algorithms to all data; eliminating noise; detecting anomalies; correlating relevant metric anomalies, traces, changes and log events triggered by incidents; using contextual data to surface incidents; and identifying probable root causes of those incidents. This gives the teams a more complete picture of what’s happening in their systems, where the bugs are and how to resolve issues quicker, ultimately speeding the development processes overall.
Through observability with AIOps, dev teams can actually build systems faster by finding problems earlier, cutting out the time-consuming process of retroactively fixing mountains of problems after the product has already shipped. On top of that, finding these problems earlier ultimately leads to more reliable services—and reliable services mean happier customers. When the product actually works the first time around and there’s a system in place to find and fix evolving issues, there’s less legwork to make customers happy. It’s a win-win.
Observability with AIOps also means freeing developer time and effort to focus more on innovation and less on operations. And who doesn’t want to work on higher impact projects more often instead of spinning their wheels making sure the work they’ve already done is actually functioning correctly?
We Need to Stop Stalling
It’s easy to get stuck in your ways, but that is the only reason dev teams haven’t improved their processes to streamline workflows and build better products. Simply put, we have to stop stalling on making this change because, finally, moving fast doesn’t have to mean breaking things.
We can continue to move fast while building products that are better quality and keep customers happier if we change our mindset around the development process. As developers, it’s our responsibility to evolve with the best strategies for our team, customers, and business—and we now have a way to do so by embracing observability with AIOps.
