Navigating the Data Haystack: AIOps as the Beacon in the Era of Information Overload

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Finding a needle in a haystack is next to impossible—so much so, it’s become shorthand for the frustration of a fruitless search. Now, imagine you’ve been told there may be a needle in the haystack, or one could appear at any time, even in a place you already searched. You spend the most valuable hours of your day searching for the needles, but in our case the needle is performance or reliability anomalies. Meanwhile, the haystack grows larger by the second, as more and more is added to the overflowing pile.

Now, imagine it’s not a needle at all, but something that could fundamentally harm—or, an even more tragic miss, help—your business. You’d feel stuck at a standstill, unable to either combat threats or capitalize on new opportunities or innovations.

This is the difficult situation today’s data professionals find themselves in. They’re on constant patrol for security and performance anomalies within increasingly complicated and opaque distributed database environments. We call that “firefighting mode” because data professionals are so busy putting out those fires that they are left with little time to focus on innovating or capitalizing on new opportunities for the business. They have an overwhelming amount of information at their fingertips but lack the actionable insights they need. Data is one of the most valuable assets an enterprise has, yet it’s fundamentally useless unless it can be leveraged, understood, and applied effectively.

The amount of data produced daily is mind-boggling—and growing. For reference, more data was collected between 2019 and 2021 than in all human history. There’s also the matter of where that data is housed. Though modern multi-cloud environments can present a myriad of benefits for businesses, they can also become difficult to administrate, manage legal compliance, hamper agility, and hinder the ability to quickly roll out updates and remediate issues. On top of that, with companies responsible for more data than ever, including potentially sensitive personal information, threat actors are also always on the prowl, hoping to steal this information, leading to massive financial and reputational damage for the organization.

All told, the task put to today’s database and IT teams isn’t a simple one. Even with basic monitoring in place, ITOps and DevOps teams lack full visibility across the performance and health of their applications and database environments, especially when combinations of on-premise, hybrid, public, and private clouds are involved. Even with their basic monitoring systems, the best these teams can hope for is a fragmented, hodgepodge view into their enterprise IT systems.

So how can such teams be expected to not only manage these exploding amounts of data on behalf of their organization, but do so while pushing innovation forward, avoiding burnout, and navigating important security and compliance regulations?

Thankfully, humans are no longer expected to overcome these challenges alone.

Today we have valuable allies in artificial intelligence (AI), machine learning (ML), and AIOps to help us transform distributed series of databases and clouds into treasure troves of usable information.

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