"It's not what happens to you, but how you react to it, that matters." - Epictetus
At the beginning of this series, we talk about the asset management and mapping components of Visibility, now we finish the monitoring phase of visibility. I had a slight change in the way I wanted to present this part of the series. I was going down the rabbit hole of monitoring and alerting, but instead we decided to focus on some of the critical business values Visibility brings to organizations, and specifically how monitoring fits into those values.
We start with cost saving of monitoring. I know what you are already thinking, how is monitoring going to save me money, and your right. Monitoring alone doesn’t save an organization any money, in fact most monitoring solutions implemented in today’s organizations are slowing them down or not helping in any way, shape or form. But hear me out…When you implement the monitoring we discussed in the last post, and you overlay AI/Operations (AI/Ops) things start to get very interesting and affecting you bottom line.
Many IT Operations teams are overwhelmed by incident volume, resulting in prolonged outages and downtime. Organizations must endure SLA penalties, damage to brand equity and lost revenue. By using AI or machine learning to correlate alerts, changes and topology data, AI/ops detects incidents as they start This reduces the frequency and impact of outages that affect critical infrastructure, applications and services. When an outage does occur, AI/Ops helps level one teams quickly understand the root cause and escalate to the right team immediately, instead of sifting through hundreds / thousands of alerts. We have seen reductions in resolution rates as much as 50%.
IT scale and complexity are increasing at a pace that is challenging for IT Ops teams to handle. To keep up, IT execs must constantly increase the headcount of their IT Ops teams, which is expensive and inefficient. Hiring and training new employee’s is expensive, the value of AI/Ops is having a platform of event correlation and automation that helps reduces alert volume by more than 95% and automates repetitive operator tasks. This enables IT Ops teams to cope with the growing volume of data and handle a much larger volume of incidents without growing headcount.
You have 15-20+ monitoring tools (commercial, homegrown, legacy) that are expensive to maintain, support and/or renew. This is on top of one the previous posts of asset management and the technical debt that represents. Monitoring is more than just know when something breaks. Using Visibility and the AI/Ops we utilize data driven reports, monitors, and automation to identify the tools that are used and the ones that aren’t. You can also start to understand what assets are truly being used and which aren’t. While technical debt doesn’t show up on a balance sheet, it does hide itself in your bottom line.
Alerts, like unsolicited opinions, can clutter your digital space, making it harder to focus on what truly matters. As stated before, most organizations have 15-20+ monitoring tools. These tools provide extreme value and deep visibility into their part of the infrastructure and application landscape, but unfortunately, they also create a lot of noise.
Noise that tends to drown teams in tickets and take their focus away from the real incident until it is too late. In the past, we would have recommended setting up some systematic process for triaging alerts, or tell you classify alerts based on severity or relevance. Sounds good in theory!
Today through AI/Ops we are capable to correlate alerts together, timestamp them and lay them out visually like a story. This allows organizations to reduce alerting noise by 90%, telling a story of what, where and when the actual problem started happing.
If your environment is anything like every other organization, you experience thousands of changes every week in infrastructure, code, systems, application, and data. Legacy root cause analysis – which focus on low level hardware and network issues, can’t easily identify these types of changes. Constantly we see organizations utilize high-cost level 3, Dev/Ops and other teams to spend tens of hours on bridge calls trying to isolate the root cause of an on-going incident or outage. Not only does it increase your Mean Time To Recovery (MTTR), but it is expensive as hell!
With AI/Ops we use machine learning to surface the probable root cause of incidents and outages. Whether the change was implemented, or it was really was a low-level hardware or network issue. Because we know the assets, know the topology, are monitoring at all levels and we can pinpoint the start of an incident or outage, this gives level 1 support engineers the probable cause right at their fingertips.
We all know that you can’t predict the future. But what if we utilize and analyze historical data to better understand trends. And what if we could utilize machine learning and algorithms to study that historical data and trends, allowing us to do more proactive mitigation.
Above we said we use machine learning to surface the probable too cause of incidents, and puts that right the fingertips of our teams. We also said above that we utilize alert correlation to simplify and reduce noise.
The next step in all of that is to enrich our data. From topologies, relationships, dependencies and more, by combining alerts, past incidents and adding enriched content to these, we can start to establish incident routing, workflows and integrate automations to fix problems before they become significant or do damage to the organization. This would reduce the frequency and impact of outages that affect critical revenue generating and non-revenue functions.
The proper use of asset management, mapping, monitoring, and AI can dramatically impact your organization. We know it is not sexy, but what if all of this could reduce your outages, make your organization more secure, increase your bottom line, and (arguably the most important) make your employee’s life’s a little less stressful and fulfilling?