Why observability data is crucial for digital transformation
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In 2022, observability data volumes could increase between two to five times, according to Era Software’s 2022 State of Observability and Log Management report. That means companies could be looking at exabytes of data to manage in five years. Current tools aren’t up to the task, 79% of IT practitioners say, and costs will skyrocket in 2022 if existing tools don’t evolve.
But storage isn’t the only issue; 96% say that even more critical is finding efficient ways to apply that data to solving business problems – and 100% say their organizations would benefit from innovation in observability.
“It’s becoming harder and harder for engineering and technology organizations to figure out which pieces of this growing pile of log data are most important,” says Todd Persen, CEO and co-founder, Era Software. “As those data volumes have gotten bigger than humans can even review or grasp, the tools to store that data have started to break down.”
The problem with traditional monitoring
As companies move into the digital age, they’re also moving from traditional two-tier application architecture to multi-tier application architecture across multiple cloud environments and managed services. The IT team doesn’t have direct control over these services and instead is dependent on what the cloud provider reports. Even though the provider is responsible for the performance of the company’s application, they might not understand or have a view of the underlying technology and how it’s performing.
Without observability, the acceleration of digital transformation could be a risky journey, resulting in poorly performing services that will ultimately impact both the customer experience and the bottom line. But while observability is a straightforward goal, and many organizations realize that existing monitoring tools cannot keep up with the massive data volumes created by modern cloud environments, they’re looking for new ways to efficiently extract critical insights from observability data.
“It’s not even just the number of systems — it’s that the operational modes of these systems have become so complex that even if you’re the developer who built the system, you can find yourself at an impasse,” Persen says. “How do you gain insight into what it’s actually doing as a dynamic system, and what metrics matter? What do you need to look at to tell why a system is failing?”
Security also remains a considerable challenge since security organizations need to analyze massive amounts of log data to identify potential security incidents and for security audits and compliance reporting. However, many organizations are forced to limit the number of logs they ingest or store because it’s too expensive to keep them all. As a result of this forced picking and choosing, many security leaders say they don’t have the logs they need to troubleshoot security incidents, which negatively impacts response efforts and increases vulnerability.
Why observability is essential
Observability bridges the gap between legacy technology and modern approaches to data management. It’s an evolution of traditional monitoring towards understanding deep insights from analyzing high volumes of logs, metrics, and traces collected from many modern cloud environments. It ensures the delivery of reliable digital services in the face of the increasing complexity of cloud services. And it’s more and more necessary for any company that’s embarking on digital transformation.
“People realize that as they’re going on this digital transformation journey, they’re adopting more tools and more products and adopting more scope and more things they need to monitor and observe,” Persen says. “Observability is an enabler because it lets people have the confidence that these new systems are doing what they want. But at the same time, it’s become table stakes for digital transformation — having a good observability story is an essential part of success.”
The State of Observability and Log Management report also revealed that IT departments are erasing data to manage the cost of collecting and storing log data with more traditional tools. But ditching the data means losing critical information needed later for forensics and security analysis.
“Imagine you have an attack and don’t have the data to figure out where it’s coming from – you’re exposing your organization to risk,” Persen says. “Not only are you exposing yourself, but if you’re not properly logging everything and potentially masking personally identifiable information, you can accidentally expose that PII.”
Key observability tools
While the cloud offers unprecedented efficiency, unlocks innovation, and slashes costs, it’s also made it a lot more complicated to figure out how to execute cloud digital transformation the right way. How do you build a business on top of these complex systems?
“At the end of the day, most companies are not in the business of managing or dealing with infrastructure. They’re in the business of providing a core service,” Persen says. “How do they stay effective while going down this relatively new and uncharted course? It’s hard, and we see our role as trying to find a way to provide a consistent set of tools in the observability space that can fit anywhere that the customer needs to go.”
Platforms like Era Software Observability Data Management, which process data between different sources and destinations at scale, plus cost-effectively store and optimize it for analysis, are the way of the future.
IT and security teams should look for a platform that can gain insights from raw data, reducing TCO for existing observability and log management solutions while preserving information in low-cost object storage. This data can be used for forensics, auditing, baselining, and seasonal trends analyses.
Persen also notes the importance of a platform that’s not dependent on any particular architecture but has the flexibility to function in systems from traditional on-prem to hybrid cloud to cloud.
And one of the biggest benefits overall of observability data platforms like these is significant cost savings due to the efficiency the technology brings to observability workloads. When you consider the budget dedicated to log management, reducing costs means having the option to store more data and improve visibility. This leads to more reliable services, freeing up resources to invest in innovation.
There’s a broader benefit too: By centralizing everything and removing artificial limits on who can access what amount of data or how much should be logged, data is democratized for the entire organization, allowing everyone to see the full view and derive insights from it.
“Data democratization, providing access to the entire organization, allows everyone to get big business benefits,” Persen says. “It’s not just data made available to ITOps for troubleshooting. You see everything. You see customer interactions. You see information about application performance. You see the trends in your customers. It’s a gold mine of data for the entire organization.”
Dig deeper: Read the 2022 State of Observability and Log Management report here.
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