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href="https://auth.causely.app/oauth/account/login" target="_blank" rel="noopener noreferrer" class="justify-center whitespace-nowrap text-base font-medium font-general-sans transition-all duration-300 ease-in-out focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 group flex items-center relative"><div class="rounded-full px-[33px] h-[48px] flex items-center transition-all duration-300 ease-in-out overflow-hidden border border-white-custom group-hover:border-causely-green"><span class="font-general-sans font-medium text-base leading-[1.001] transition-all duration-300 ease-in-out text-white-custom group-hover:text-causely-green">Sign in</span></div></a></div></div></div></div></header><div><article class="container max-w-4xl py-12"><div class="mb-8"><h1 class="text-4xl font-bold mb-4">The “R” in MTTR: Repair or Recover? What’s the difference?</h1><div class="flex items-center mb-8"><div class="flex -space-x-2 mr-3"><img alt="Causely" loading="lazy" width="40" height="40" decoding="async" data-nimg="1" class="rounded-full ring-2 ring-background" style="color:transparent" srcSet="/_next/image?url=%2Fimages%2Fnewsroom%2FCauselyIcon.png&amp;w=48&amp;q=75&amp;dpl=dpl_MN81ko9c8G2XkeHz1xLBkP1rX43E 1x, /_next/image?url=%2Fimages%2Fnewsroom%2FCauselyIcon.png&amp;w=96&amp;q=75&amp;dpl=dpl_MN81ko9c8G2XkeHz1xLBkP1rX43E 2x" src="/_next/image?url=%2Fimages%2Fnewsroom%2FCauselyIcon.png&amp;w=96&amp;q=75&amp;dpl=dpl_MN81ko9c8G2XkeHz1xLBkP1rX43E"/></div><div><p class="font-medium">Causely</p><p class="text-sm text-muted-foreground">September 17, 2024</p></div></div></div><div class="relative aspect-video mb-12"><img alt="The “R” in MTTR: Repair or Recover? What’s the difference?" loading="lazy" decoding="async" data-nimg="fill" class="object-cover rounded-lg" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="100vw" srcSet="/_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=640&amp;q=75 640w, /_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=750&amp;q=75 750w, /_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=828&amp;q=75 828w, /_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=1080&amp;q=75 1080w, /_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=1200&amp;q=75 1200w, /_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=1920&amp;q=75 1920w, /_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=2048&amp;q=75 2048w, /_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=3840&amp;q=75 3840w" src="/_next/image?url=https%3A%2F%2Fstorage.ghost.io%2Fc%2Ff4%2F30%2Ff4302d25-b539-4f59-b4cb-e3882f8f5026%2Fcontent%2Fimages%2Fwp-content%2Fuploads%2F2024%2F09%2Fmttx-e1726595097344-1.png&amp;w=3840&amp;q=75"/></div><div class="prose prose-invert prose-dark max-w-none"><h2 id="finding-meaning-in-a-world-of-acronyms">Finding meaning in a world of acronyms</h2><p>There are so many ways to measure application reliability today, with hundreds of key performance indicators (KPIs) to measure availability, error rates, user experiences, and quality of service (QoS). Yet every organization I speak with struggles to effectively use these metrics.  Some applications and services require custom metrics around reliability while others can be measured with just uptime vs. downtime.</p><p>In my role at <a href="https://www.causely.ai/?ref=causely-blog.ghost.io">Causely</a>, I work with companies every day who are trying to improve the reliability, resiliency, and agility of their applications. One method of measuring reliability that I keep a close eye on is MTT(XYZ).  Yes, I made that up, but it’s meant to capture all the different variations of mean time to “X” out there.  We have MTTR, MTTI, MTTF, MTTA, MTBF, MTTD, and the list keeps going.  In fact, some of these acronyms have multiple definitions.  The one whose meaning I want to discuss today is MTTR.</p><h2 id="so-what-s-the-meaning-of-mttr-anyway">So, what’s the meaning of MTTR anyway?</h2><p>Before cloud-native applications, MTTR meant one thing – Mean Time to Repair. It’s a metric focused on how quickly an organization can respond to and fix problems that cause downtime or performance degradation.  It’s simple to calculate too:</p><figure class="kg-card kg-image-card"><img src="https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/wp-content/uploads/2024/09/screenshot-2024-09-17-at-1-39-26-pm.png" class="kg-image" alt="MTTR meaning: How to calculate" loading="lazy" width="480" height="117"/></figure><p>Total time spent on repairs is the length of time IT spends fixing issues, and number of repairs is the number of times a fix has been implemented.  Some organizations look at this over a week or a month in production. It’s a great metric to understand how resilient your system is and how quickly the team can fix a known issue.  Unfortunately, data suggests that most IT organizations’ <a href="https://www.cncf.io/blog/2024/04/18/the-challenges-of-rising-mttr-and-what-to-do/?ref=causely-blog.ghost.io" rel="noopener">MTTR is increasing</a> every year, despite massive investments in the observability stack.</p><p>For monolithic applications, MTTR has historically been an excellent measurement; as soon as a fix is applied, the entire application is usually back online and performing well.  Now that IT is moving toward serverless and cloud-native applications, it is a much different story.  When a failure occurs in Kubernetes – where there are many different containers, services, applications, and more  all communicating in real time – the entire system can take much longer to <em><strong>recover</strong></em>.</p><h2 id="the-new-mttr-mean-time-to-recover">The new MTTR: Mean Time to Recover</h2><p>I am seeing more and more organizations redefine the meaning of MTTR from “mean time to repair” to “mean time to <em><strong>recover</strong></em>.”  <em><strong>Recover</strong></em> means that not only is everything back online, but the system is performing well and satisfying any QoS or SLAs <em><strong>AND</strong></em> a preventative approach has been implemented.</p><figure class="kg-card kg-image-card"><img src="https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/wp-content/uploads/2024/09/mttx-e1726595097344.png" class="kg-image" alt="" loading="lazy" width="1920" height="975" srcSet="https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/size/w600/wp-content/uploads/2024/09/mttx-e1726595097344.png 600w, https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/size/w1000/wp-content/uploads/2024/09/mttx-e1726595097344.png 1000w, https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/size/w1600/wp-content/uploads/2024/09/mttx-e1726595097344.png 1600w, https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/wp-content/uploads/2024/09/mttx-e1726595097344.png 1920w" sizes="(min-width: 720px) 720px"/></figure><p>For example, take a common problem within Kubernetes: a pod enters a CrashLoopBackoff state.  There are many reasons why a pod might continuously restart including deployment errors, resourcing constraints, DNS resolution errors, missing K8s dependencies, etc.  But let’s say you completed your investigation and found out that your pod did not have sufficient memory and therefore was crashing/restarting.  So you increased the limit on the container or the deployment and the pod(s) seems to be running fine for a bit…. but wait, it just got evicted.</p><p>The node now has increased memory usage and pods are being evicted.  Or, what if now we created noisy neighbors, and that pod is “stealing” resources like memory from others on the same node?  This is why organizations are moving away from <em><strong>repair</strong></em> because sometimes when the applied fix brings everything online, it doesn’t mean the system is healthy. “Repaired” can be a subjective term. Furthermore, sometimes the fix is merely a band-aid, and the problem returns hours, days, or weeks later.</p><p>Waiting for the entire application system to become healthy and applying a preventative measure will get us better insight into reliability.  It is a more accurate way to measure how long it takes from a failure event to a healthy environment.  After all, just because something is online does not mean it is performing well.  The tricky issue here is: How do you measure “healthy”?   In other words, how do we know the entire system is healthy and our preventative patch is truly preventing problems?  There are some good QoS benchmarks like response time or transactions per second, but there is usually some difficulty in defining these thresholds.  An improvement in MTBF (mean time between failures) is another good benchmark to test to see if your preventative approach is working.</p><h2 id="how-can-we-improve-mean-time-to-recover">How can we improve Mean Time to Recover?</h2><p>There are many ways to improve system recovery, and ultimately the best way to improve MTTR is to improve all the MTT(XYZ) that come before it on incident management timelines.</p><ul><li><strong>Automation:</strong> Automating tasks like ticket creation, assigning incidents to appropriate teams, and probably most importantly, automating the fix can all help reduce the time from problem identification to recovery.  But, the more an organization scrutinizes every single change and configuration, the longer it takes to implement a fix.  Becoming less strict drives faster results.</li><li><strong>Well-defined Performance Benchmarks:</strong> Lots of customers I speak with have a couple KPIs they track, but the more specific the better.  For example, instead of making a blanket statement that every application needs to have 200ms of response time or less,  set these metrics on an app by app basis.</li><li><strong>Chaos Engineering:</strong> This is an often-overlooked <a href="https://www.splunk.com/en_us/blog/learn/chaos-engineering.html?ref=causely-blog.ghost.io#:~:text=Improve%20failure%20recovery%20%2D%20Since%20chaos%20tests,engineering%20enhances%20failure%20recovery%20and%20reduces%20downtime" rel="noopener">methodology to improve recovery rate</a>.  Practicing and simulating failures helps improve how quickly we can react, troubleshoot, and apply a fix.  It does take a lot of time though, so it is not an easy strategy to adhere to.</li><li><strong>Faster Alerting Mechanisms:</strong> This is simple: The faster we get notified of a problem, the quicker we can fix it.  We need to not just identify the symptoms but also quickly find the root cause.  I see many companies try to set up proactive alerts, but they often get more smoke than fire.</li><li><strong>Knowledge Base:</strong> This was so helpful for me in a previous role. Building a KB in a system like Atlassian, SharePoint, or JIRA can help immensely in the troubleshooting process.  The KB needs to be searchable and always changing as the environment evolves.  Being able to search for a specific string in an error message within a KB can immediately highlight not just a root cause but also a fix.</li></ul><p>To summarize, MTTR is a metric that needs to capture the state of a system from the moment of failure until the entire system is healthy again.  This is a much more accurate representation of how fast we recover from a problem, and how resilient the application architecture is.  MTTR is a principle that extends beyond the world of IT; its applications exist in security, mechanics, even healthcare.  Just remember, a good surgeon is not only measured by how fast he can repair a broken bone, but by how fast the patient can recover.</p><blockquote><em>Improving application resilience and reliability is something we spend a lot of time thinking about at Causely. We’d love to hear how you’re handling this today, and what metric you’ve found most useful toward this goal. Comment here or <a href="https://www.causely.ai/?ref=causely-blog.ghost.io#contact">contact us</a> with your thoughts!</em></blockquote></div></article><section class="bg-main-background py-0 overflow-hidden px-6 md:px-6 xl:px-[104px] 3xl:px-[284px] pb-[107px] md:pb-[143px] xl:pb-[178px] 3xl:pb-[200px]"><div class="mx-auto max-w-[1280px] px-0 3xl:max-w-[776px]"><div class="flex flex-col gap-6 md:gap-6 3xl:gap-[24px] items-center justify-center px-0 md:px-12 3xl:pl-[48px] 3xl:pr-[40px] 3xl:pt-0 rounded-[32px]"><div class="bg-main-background flex flex-col gap-[3.367px] items-start overflow-hidden relative"><div class="relative h-[267.418px] w-[352px] md:h-[277px] md:w-[352px] 3xl:h-[378px] 3xl:w-[542px]"><img alt="" loading="lazy" decoding="async" data-nimg="fill" class="object-contain" style="position:absolute;height:100%;width:100%;left:0;top:0;right:0;bottom:0;color:transparent" sizes="(max-width: 1919px) 352px, 542px" 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initial-scale=1\"}],[\"$\",\"meta\",\"2\",{\"name\":\"theme-color\",\"content\":\"#020817\"}]]\n2a:I[97531,[\"/_next/static/immutable/chunks/011me_93942kq.js\",\"/_next/static/immutable/chunks/1c_nr-0i0gjnt.js\",\"/_next/static/immutable/chunks/3lgnztd7blcu2.js\",\"/_next/static/immutable/chunks/17dj4bkkdjper.js\",\"/_next/static/immutable/chunks/0vbcyr25-edwj.js\",\"/_next/static/immutable/chunks/2-s-9-xpk6j_j.js\",\"/_next/static/immutable/chunks/0ojqgjv--k_a7.js\",\"/_next/static/immutable/chunks/3cxalgnb-xm18.js\",\"/_next/static/immutable/chunks/3ithhqp6kca4w.js\"],\"BlogPostRender\"]\n2d:I[27201,[\"/_next/static/immutable/chunks/011me_93942kq.js\",\"/_next/static/immutable/chunks/1c_nr-0i0gjnt.js\",\"/_next/static/immutable/chunks/3lgnztd7blcu2.js\",\"/_next/static/immutable/chunks/17dj4bkkdjper.js\",\"/_next/static/immutable/chunks/0vbcyr25-edwj.js\"],\"IconMark\"]\n2b:T24c4,\u003ch2 id=\"finding-meaning-in-a-world-of-acronyms\"\u003eFinding meaning in a world of acronyms\u003c/h2\u003e\u003cp\u003eThere are so many ways to measure application reliability today, with hundreds of key performance indicators (KPIs) to measure availability, error rates, user experiences, and quality of service (QoS). Yet every organization I speak with struggles to effectively use these metrics.  Some applications and services require custom metrics around reliability while others can be measured with just uptime vs. downtime.\u003c/p\u003e\u003cp\u003eIn my role at \u003ca href=\"https://www.causely.ai/?ref=causely-blog.ghost.io\"\u003eCausely\u003c/a\u003e, I work with companies every day who are trying to improve the reliability, resiliency, and agility of their applications. One method of measuring reliability that I keep a close eye on is MTT(XYZ).  Yes, I made that up, but it’s meant to capture all the different variations of mean time to “X” out there.  We have MTTR, MTTI, MTTF, MTTA, MTBF, MTTD, and the list keeps going.  In fact, some of these acronyms have multiple definitions.  The one whose meaning I want to discuss today is MTTR.\u003c/p\u003e\u003ch2 id=\"so-what-s-the-meaning-of-mttr-anyway\"\u003eSo, what’s the meaning of MTTR anyway?\u003c/h2\u003e\u003cp\u003eBefore cloud-native applications, MTTR meant one thing – Mean Time to Repair. It’s a metric focused on how quickly an organization can respond to and fix problems that cause downtime or performance degradation.  It’s simple to calculate too:\u003c/p\u003e\u003cfigure class=\"kg-card kg-image-card\"\u003e\u003cimg src=\"https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/wp-content/uploads/2024/09/screenshot-2024-09-17-at-1-39-26-pm.png\" class=\"kg-image\" alt=\"MTTR meaning: How to calculate\" loading=\"lazy\" width=\"480\" height=\"117\"\u003e\u003c/figure\u003e\u003cp\u003eTotal time spent on repairs is the length of time IT spends fixing issues, and number of repairs is the number of times a fix has been implemented.  Some organizations look at this over a week or a month in production. It’s a great metric to understand how resilient your system is and how quickly the team can fix a known issue.  Unfortunately, data suggests that most IT organizations’ \u003ca href=\"https://www.cncf.io/blog/2024/04/18/the-challenges-of-rising-mttr-and-what-to-do/?ref=causely-blog.ghost.io\" rel=\"noopener\"\u003eMTTR is increasing\u003c/a\u003e every year, despite massive investments in the observability stack.\u003c/p\u003e\u003cp\u003eFor monolithic applications, MTTR has historically been an excellent measurement; as soon as a fix is applied, the entire application is usually back online and performing well.  Now that IT is moving toward serverless and cloud-native applications, it is a much different story.  When a failure occurs in Kubernetes – where there are many different containers, services, applications, and more  all communicating in real time – the entire system can take much longer to \u003cem\u003e\u003cstrong\u003erecover\u003c/strong\u003e\u003c/em\u003e.\u003c/p\u003e\u003ch2 id=\"the-new-mttr-mean-time-to-recover\"\u003eThe new MTTR: Mean Time to Recover\u003c/h2\u003e\u003cp\u003eI am seeing more and more organizations redefine the meaning of MTTR from “mean time to repair” to “mean time to \u003cem\u003e\u003cstrong\u003erecover\u003c/strong\u003e\u003c/em\u003e.”  \u003cem\u003e\u003cstrong\u003eRecover\u003c/strong\u003e\u003c/em\u003e means that not only is everything back online, but the system is performing well and satisfying any QoS or SLAs \u003cem\u003e\u003cstrong\u003eAND\u003c/strong\u003e\u003c/em\u003e a preventative approach has been implemented.\u003c/p\u003e\u003cfigure class=\"kg-card kg-image-card\"\u003e\u003cimg src=\"https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/wp-content/uploads/2024/09/mttx-e1726595097344.png\" class=\"kg-image\" alt loading=\"lazy\" width=\"1920\" height=\"975\" srcset=\"https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/size/w600/wp-content/uploads/2024/09/mttx-e1726595097344.png 600w, https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/size/w1000/wp-content/uploads/2024/09/mttx-e1726595097344.png 1000w, https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/size/w1600/wp-content/uploads/2024/09/mttx-e1726595097344.png 1600w, https://storage.ghost.io/c/f4/30/f4302d25-b539-4f59-b4cb-e3882f8f5026/content/images/wp-content/uploads/2024/09/mttx-e1726595097344.png 1920w\" sizes=\"(min-width: 720px) 720px\"\u003e\u003c/figure\u003e\u003cp\u003eFor example, take a common problem within Kubernetes: a pod enters a CrashLoopBackoff state.  There are many reasons why a pod might continuously restart including deployment errors, resourcing constraints, DNS resolution errors, missing K8s dependencies, etc.  But let’s say you completed your investigation and found out that your pod did not have sufficient memory and therefore was crashing/restarting.  So you increased the limit on the container or the deployment and the pod(s) seems to be running fine for a bit…. but wait, it just got evicted.\u003c/p\u003e\u003cp\u003eThe node now has increased memory usage and pods are being evicted.  Or, what if now we created noisy neighbors, and that pod is “stealing” resources like memory from others on the same node?  This is why organizations are moving away from \u003cem\u003e\u003cstrong\u003erepair\u003c/strong\u003e\u003c/em\u003e because sometimes when the applied fix brings everything online, it doesn’t mean the system is healthy. “Repaired” can be a subjective term. Furthermore, sometimes the fix is merely a band-aid, and the problem returns hours, days, or weeks later.\u003c/p\u003e\u003cp\u003eWaiting for the entire application system to become healthy and applying a preventative measure will get us better insight into reliability.  It is a more accurate way to measure how long it takes from a failure event to a healthy environment.  After all, just because something is online does not mean it is performing well.  The tricky issue here is: How do you measure “healthy”?   In other words, how do we know the entire system is healthy and our preventative patch is truly preventing problems?  There are some good QoS benchmarks like response time or transactions per second, but there is usually some difficulty in defining these thresholds.  An improvement in MTBF (mean time between failures) is another good benchmark to test to see if your preventative approach is working.\u003c/p\u003e\u003ch2 id=\"how-can-we-improve-mean-time-to-recover\"\u003eHow can we improve Mean Time to Recover?\u003c/h2\u003e\u003cp\u003eThere are many ways to improve system recovery, and ultimately the best way to improve MTTR is to improve all the MTT(XYZ) that come before it on incident management timelines.\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cstrong\u003eAutomation:\u003c/strong\u003e Automating tasks like ticket creation, assigning incidents to appropriate teams, and probably most importantly, automating the fix can all help reduce the time from problem identification to recovery.  But, the more an organization scrutinizes every single change and configuration, the longer it takes to implement a fix.  Becoming less strict drives faster results.\u003c/li\u003e\u003cli\u003e\u003cstrong\u003eWell-defined Performance Benchmarks:\u003c/strong\u003e Lots of customers I speak with have a couple KPIs they track, but the more specific the better.  For example, instead of making a blanket statement that every application needs to have 200ms of response time or less,  set these metrics on an app by app basis.\u003c/li\u003e\u003cli\u003e\u003cstrong\u003eChaos Engineering:\u003c/strong\u003e This is an often-overlooked \u003ca href=\"https://www.splunk.com/en_us/blog/learn/chaos-engineering.html?ref=causely-blog.ghost.io#:~:text=Improve%20failure%20recovery%20%2D%20Since%20chaos%20tests,engineering%20enhances%20failure%20recovery%20and%20reduces%20downtime\" rel=\"noopener\"\u003emethodology to improve recovery rate\u003c/a\u003e.  Practicing and simulating failures helps improve how quickly we can react, troubleshoot, and apply a fix.  It does take a lot of time though, so it is not an easy strategy to adhere to.\u003c/li\u003e\u003cli\u003e\u003cstrong\u003eFaster Alerting Mechanisms:\u003c/strong\u003e This is simple: The faster we get notified of a problem, the quicker we can fix it.  We need to not just identify the symptoms but also quickly find the root cause.  I see many companies try to set up proactive alerts, but they often get more smoke than fire.\u003c/li\u003e\u003cli\u003e\u003cstrong\u003eKnowledge Base:\u003c/strong\u003e This was so helpful for me in a previous role. Building a KB in a system like Atlassian, SharePoint, or JIRA can help immensely in the troubleshooting process.  The KB needs to be searchable and always changing as the environment evolves.  Being able to search for a specific string in an error message within a KB can immediately highlight not just a root cause but also a fix.\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eTo summarize, MTTR is a metric that needs to capture the state of a system from the moment of failure until the entire system is healthy again.  This is a much more accurate representation of how fast we recover from a problem, and how resilient the application architecture is.  MTTR is a principle that extends beyond the world of IT; its applications exist in security, mechanics, even healthcare.  Just remember, a good surgeon is not only measured by how fast he can repair a broken bone, but by how fast the patient can recover.\u003c/p\u003e\u003cblockquote\u003e\u003cem\u003eImproving application resilience and reliability is something we spend a lot of time thinking about at Causely. 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