What is mean time to detect? And why does it matter?

Have you ever wondered about your IT team’s efficiency in detecting incidents? Mean time to detect (MTTD) is an incident management key performance indicator (KPI) that reveals your productivity during the first stage of incident resolution and enables investigation into opportunities for improvement.

If your ITOps and DevOps teams can lower their MTTD, you can more quickly identify issues, minimize potential downtime, and maintain system reliability. We’ll walk you through calculating your organization’s MTTD and strategies to keep it as low as possible.

What is MTTD?

Mean time to detect (MTTD) is defined as the average time between the start of an incident and the moment an organization identifies the issue. MTTD is also called the mean time to identify (MTTI) or mean time to discover.

As an incident management KPI, MTTD plays a crucial role in shaping the incident management strategies of IT teams. The time to detection is part of the overall time to resolution, and keeping this time low ensures better experiences, lower risk, and better availability.

Maintaining low MTTD values becomes critical for all IT teams in this context and allows your teams to address potential IT incidents before the impact reaches end users. Read on to learn more about:

• What is MTTD?
• What’s the difference between MTTD and MTTR?
• How do you calculate MTTD?
• Why keeping a low MTTD is necessary for incident management
• What’s a ‘good’ MTTD?
• Best practices to keep your MTTD down
• Reduce your Mean Time to Detect with AIOps
• BigPanda AIOps ensures faster detection, improved incident management

What’s the difference between MTTD and MTTR?

Mean time to detect refers to the average time taken to identify an issue or outage in IT systems, revealing the efficiency of monitoring and alerting processes.

In contrast, mean time to resolve (MTTR) measures the average duration required to fix the identified issue, indicating the effectiveness of the response and repair strategies within IT operations and DevOps teams.

How do you calculate MTTD?

You can calculate MTTD mathematically with the following formula:

MTTD = (Total sum of incident detection times) ÷ (# of incidents)

Let’s use a hypothetical scenario to illustrate how you can calculate your MTTD. The table below shows five unique incidents for Organization A in a month, complete with their initiation and detection times:

In this example, the total detection time was 198 minutes and the total number of incidents 5. Applying the above formula, the MTTD for Organization A for the month of January is 198 / 5 = 39.6 minutes.

Why a low MTTD is necessary for incident management

Tracking and enhancing your organization’s MTTD can help you evaluate the effectiveness of your incident management processes, including your log management and monitoring strategies. A low MTTD signals robust incident management, while a high MTTD suggests a lackluster monitoring approach, leading to delayed incident discovery and incident escalation.

What’s a good MTTD?

Aim to keep your MTTD time as low as possible. A lower MTTD means you’re discovering and solving problems quickly.

BigPanda customers have an average MTTD of around 10 minutes. However, we recommend that you aim for an MTTD of less than 1 minute. It’s cause for concern when MTTD stretches beyond 30 minutes or even into hours.

Addressing issues swiftly prevents them from snowballing into larger headaches and is more cost-effective than waiting until it’s too late. In contrast, longer detection times usually mean longer downtime, which affects availability and leads to unhappy customers.

Best practices to keep your MTTD down

When it comes to slashing your MTTD, adopting smart practices is the name of the game. Here’s a rundown of some proven best practices tailored for ITOps teams:

• Create a clear incident response process: It’s unrealistic to expect your IT team to improvise during a crisis. What you need is a well-defined, meticulously documented incident response process. Make it accessible and keep it updated so there’s no room for guesswork.
• Embrace monitoring and observability: Establish a comprehensive monitoring system to deliver a holistic view of IT infrastructure health and enable early detection of anomalies or potential issues.
• Leverage AIOps: Automate event analysis, identify patterns and correlations and automate incident response to keep MTTD at a minimum, fostering optimal operational efficiency.
• Prioritize ongoing training: The flood of insights from monitoring tools demands a deep understanding from your ITOps team. Equip them with the know-how to navigate the complexities of IT incidents by investing in continuous training. This ensures they extract vital information from monitoring tools, preventing service disruptions and outages.
• Implement blameless post-mortems after IT incidents: View every incident as a learning opportunity. Instead of assigning fault, conduct blameless post-mortems to uncover the root causes, preventive measures, and opportunities for earlier detection. The emphasis is on process improvement so teams can focus on preventing the issue or, at least, detecting it and fixing it sooner.
• Drive continuous improvement to ensure preemptive action: Introduce continuous improvement programs to proactively address potential pitfalls. Analyze and enhance processes regularly to identify trends and areas for refinement. Tracking MTTD over time allows you to spot trends and trigger investigations. If MTTD is on the rise, dig into the reasons—did detection time increase, or did more incidents occur?

Reduce mean time to detect with AIOps

MTTD isn’t just an important KPI on a dashboard; it’s a diagnostic tool that helps you pinpoint the root causes of issues, streamline your response processes, and optimize the overall reliability of your IT systems.

AIOps harnesses advanced AI and machine learning (ML) technologies to transform IT operations, improving and adding critical context to analytics and enabling automation to reduce MTTD. When it comes to lowering MTTD, AIOps assume a pivotal role by automatically and reliably revealing key incident analysis, incident impact, and probable root cause.

To expedite incident identification, AIOps can augment companies’ existing L1 teams, helping them to maintain high system availability. When combined with your business’s unique topology and other data, it can supply meaningful insights in a fraction of the time of humans alone.

“Centralizing our operations with AIOps and BigPanda allowed us to have a much earlier MTTD, which gave us a head start to resolve operational incidents,” says Alvin Smith, Vice President of Global Infrastructure and Operations at InterContinental Hotels Group (IHG)

BigPanda AIOps ensures faster detection, improved incident management

BigPanda AIOps delivers AI-driven insights and automation not only empowers IT teams to identify and address incidents promptly, and cultivates a proactive approach to IT operations management.

• Single pane of glass: BigPanda provides ITOps teams with a unified single pane of glass for seamless incident management. This intuitive real-time interface enables teams to observe and investigate incidents, assess their impact, identify root causes, and collaboratively streamline resolution.
• Automate root cause analysis: AI/ML correlates alerts across your hybrid cloud deployments with change, topology, and available CMDB data to build actionable and contextually rich incidents in real-time. BigPanda AI automatically identifies the factors causing IT incidents and suggests actions on how to resolve them in real-time.
• Improve coordination and workflows: BigPanda enables efficient incident sharing and escalation through seamless integration with ticketing, notification, and chat tools to keep your teams and tools up to date and working together.

Schedule your personalized demo today and discover firsthand how BigPanda transforms incident management by offering a smooth and cohesive experience for enhanced visibility and resolution capabilities.