“Too many cooks spoil the broth.” It’s an old saying we’ve heard many times in childhood. If we put it in today’s IT monitoring context, we could change it to “too many tools spoil the insights and efficiency.” IT teams across organizations have deployed multiple tools over the decades to monitor and track the performance of networks, databases, and applications and to ensure the smooth running of the business. Add to it the daily challenges IT executives face, such as hybrid and multi-cloud, the Internet of Things (IoT), data management, distributed architectures, cloud-native applications, microservices, containerization, and increased security threats.
With these evolving complex IT landscapes, it has become challenging for existing monitoring tools to provide proper root cause analysis and other valuable insights to the business. Various teams and departments are overloaded with alerts and disjointed analytics and have difficulty accessing the correct actionable insight needed to identify, prioritize, and resolve issues quickly. According to Gartner
,[i] many enterprises already have upward of 15 monitoring tools and do not wish to add further complexity.
Observability Takes Your Monitoring to a Whole New Level
Given these complexities, today’s IT teams need insights across the full stack in near real-time to help them provide a better customer experience, take control of costs, and make faster business decisions. So, in today’s modern environments, an observability platform goes beyond monitoring and expedited problem resolution by providing insights, automated analytics, and actionable intelligence through the application of cross-domain data correlation, machine learning (ML), and artificial intelligence (AI) for IT operations (AIOps) across massive real-time and historical metrics, logs, and trace data.
An observability platform also helps IT teams work more collaboratively with software development for IT operations (DevOps), security operations (SecOps), and other line-of-business (LoB) teams. The five common blind spots an observability platform can eliminate are:
Blind Spot One: No Single Source of Truth
Disparate tools often lead to missed alerts and low cross-team collaboration. The results? A good amount of finger-pointing and no single source of truth on which executives can rely. This means inconsistent, error-prone responses and poor service delivery.
Observability enables proactive management and a better digital experience through near real-time and predictive intelligence. With all teams looking at the same data as a single source of truth, collaboration is fostered, and previously siloed teams become partners with a common goal.
Blind Spot Two: Unfocused Automation and Remediation
Manual tasks are prone to human error and often become the root cause of issues. Not having a clear line of sight leads to conflicting answers during analysis, which is risky and can result in poor customer experiences and lost revenue.
Combined with AIOps and ML, Observability can speed up the process with automated analytics, actionable intelligence, and predictive recommendations. AI and ML can help you avoid blindly running towards alert noise and spikes by letting you know if spikes are within a specific normal range, and if they are outside of those bounds, VMs can be automatically spun up to address the issue.
Blind Spot Three: Shallow Operational Views
Too many tools can lead to too many panes of glass and low/slow data correlation. But perhaps the biggest issue is excessive alert noise, which creates inefficient ITOps and low productivity when teams do not know where to start or how to prioritize. If 50 alerts arrive, are they 50 unique issues or 10 issues with five alerts per issue? How can teams cluster alerts and make recommendations?
Observability helps reduce alert noise. It also allows teams to visually assemble disparate data points into a complete and detailed picture of the environment which is continuously visualized and analyzed to ensure business service delivery.
Blind Spot Four: Cost Inefficiency
Monitoring tool sprawl increases overhead and licensing fees—
and wastes budget. Plus, it requires more people across multiple domains to maintain and run these tools.
Observability reduces costs and speeds up time to value by giving teams faster insights into component relationships, deviations, and dependencies. A consolidated tool strategy can lower spending on tools and licensing and ensure all the data points are related and connected. No additional staff is needed, and there’s the potential to reassign headcount to other high-priority tasks.
Blind Spot Five: Lack of Cross-Domain Correlation
Monitoring is typically domain-focused and primarily for on-premises data centers. With hybrid and multi-cloud adoption (and complexity) exploding, blind spots can occur, resulting in wasted CapEx and OpEx. And, with so much data flowing in and out, how do you know whose data’s accurate? How are the tools collecting data, and at what intervals do they collect it? This leads to manual correlation across domains and is a recipe for disaster.
Observability provides deep visibility across massive real-time and historical metrics, logs, and trace data, as well as collaboration across ITOps, DevOps, and SecOps teams. Ultimately, you have an opportunity to get everyone onto the same pane of glass—no more debating who’s right and who’s wrong because of what disparate systems say.
Watch this
on-demand webcast by Brandon Shopp, GVP of product management, as he talks more about observability, how it can make your organization more productive and agile, and how to transition to an observability strategy to reduce costs and risk.
[i]Gartner, Innovation Insight for Observability, Padraig Byrne, Josh Chessman, September 28, 2020, refreshed on March 9, 2022. https://www.gartner.com/doc/3991053 
