Predictive maintenance is a proactive approach to monitoring the health of your endpoints. It helps your IT team flag problems and correct them before they create larger issues, extending the life of your hardware and keeping your network safe and secure. Predictive maintenance for endpoints is more proactive than other maintenance programs, and implementing a predictive maintenance program requires an upfront investment of time and resources. However, the payoff is well worth the initial set-up.
What Is Predictive Maintenance?
Predictive maintenance is a proactive approach to managing and maintaining endpoints. Instead of reacting to issues as they occur, like a hard drive failing or a laptop randomly rebooting, predictive maintenance relies on data and analytics to predict when an endpoint might fail or need service.
Using a combination of system logs, device performance, usage patterns, and other information, predictive maintenance can spot early warning signs of problems or potential failure and flag them for remediation before they become crucial issues, minimizing downtime and reducing unexpected (and expensive) repairs.
Predictive maintenance is different from preventative maintenance. Preventive maintenance is scheduled routine maintenance tasks that happen whether or not the endpoint needs service. It’s similar to taking your car in for a regular oil change, even if the car shows no signs of needing it. On endpoints, preventative maintenance could include regular software updates or cleaning up the hard drive, even if there are no performance issues.
It’s also different from reactive maintenance, which is fixing problems after they happen. Instead of checking the hard drive to see how it’s performing or cleaning it up every six months, the hard drive isn’t maintained or replaced until the endpoint’s performance noticeably degrades or the device fails.
Types of Predictive Maintenance Programs
Predictive maintenance is always proactive but comes in several forms, some more passive than others. Most companies opt for a hybrid approach, using a combination of some or all of these programs to ensure their endpoints perform at their peak.
Threshold-Based Monitoring
Threshold-based monitoring is the most basic type of predictive maintenance. It sets and uses predefined metrics for endpoint performance, like CPU temperature or battery health. If the endpoint crosses the threshold, it’s flagged for maintenance. Threshold-based monitoring is easy to set up and is good at identifying obvious issues, but this approach is limited. It often misses more gradual or subtle signs of performance issues.
Trend Analysis
Trend analysis collects and analyzes historical data to identify patterns or trends that suggest the endpoint may fail soon. For example, increasing disk errors could suggest there’s a problem with the hard drive that should be addressed. Trend analysis is more proactive than threshold-based monitoring and is better at identifying gradual or slow-developing problems. However, it requires good data collection and analysis.
Machine Learning or AI-Driven Monitoring
Machine learning or AI-driven monitoring is the most advanced type of predictive maintenance. It predicts endpoint failures based on complex patterns and interactions. For example, an AI-driven model can combine information on disk health, CPU usage, and event logs to forecast the likelihood the endpoint will fail. Machine learning methods can be highly accurate and pick up on subtle clues that there are problems with the endpoint that humans might miss. However, this kind of predictive maintenance can be expensive. In addition to the initial investment, someone needs to train and adjust the model regularly to ensure accurate performance.
Benefits of Predictive Maintenance Programs for Endpoints
No matter which predictive maintenance program or programs you use, the benefits are often worth the setup and ongoing monitoring.
Reduces Downtime
Predictive maintenance stops minor issues from becoming major ones that crash an endpoint. Addressing problems before they start means employees don’t lose time waiting for someone to fix their endpoint. Flagged issues can be scheduled for a repair when the user isn’t busy, or IT can arrange for a loaner device while larger repairs or a replacement is made.
Good for Budgets
A proactive approach to endpoint maintenance often means you can avoid costly emergency repairs or replacements. Knowing you’ll need eight new hard drives in a month or two means you can budget and plan for that purchase accordingly. Suddenly needing to buy eight new hard drives may not destroy your budget, but you may not get a great deal on the purchase.
What’s more, proactively fixing issues can extend the service life of an endpoint. While you may only get six months or a year out of it, knowing you can make an endpoint last without sacrificing performance reduces how often you need to replace endpoints, saving you money.
Data-Driven Insights
Even if you opt for a threshold-based predictive maintenance program, the data and information IT gathers are invaluable, providing them with insights about endpoint health and usage patterns. As they gather more data, they’ll be able to forecast more accurately how many endpoints they’ll need in a given year, when to buy them, and how often they need to upgrade endpoints.
Challenges of Predictive Maintenance for Endpoints
Like any maintenance program, setting the guidelines and sticking to them is sometimes easier said than done. Here are some of the challenges of predictive maintenance programs.
Complex Implementation
Advanced predictive maintenance tools require high-quality data from multiple sources to perform correctly. When you have users in multiple locations, or your endpoints are on different operating systems, ensure you have high-quality data collection to ensure the analysis and predictions are accurate. Integrating and implementing the underlying systems that capture the data can be complex and time-consuming.
Even simple programs may be more complex to add to your tech stack. You may need to make additional investments of time or money to ensure your tool integrates with your existing system without slowing the endpoint or network.
Data Quality
Even if you’ve done the best integration and implementation possible, ensuring the tool has accurate and reliable data is an ongoing concern. You need information about the endpoint’s
- Hardware (like disk health or battery health)
- Event and application logs (for critical errors, crashes, or resource conflicts)
- Usage patterns (when the device is used, which applications and patches are installed)
- Performance (slow boot times, high memory usage, or dropped packets)
Incomplete or inconsistent data collection can result in more false positives and false negatives, undermining faith in the tool and its predictions.
While you can gather the data using endpoint management tools, built-in system logs, or third-party tools, someone from IT still needs to monitor the data, data collection, and analysis. Thresholds may need regular adjusting, and an AI-driven system needs to be tested and fine-tuned regularly, which can create a burden for IT.
Culture Change
Predictive maintenance is very different from preventative and reactive maintenance. While there are many benefits to taking a more proactive approach to endpoint maintenance, it is a change. Switching to predictive maintenance often means changing workflows and reallocating resources to monitor everything the system has flagged. Companies that use machine learning for predictive maintenance may have to pull someone from IT to train and fine-tune the model.
Emerging Trends and Technologies
While predictive maintenance for endpoints isn’t new, many organizations haven’t adopted this strategy — yet. As more companies adopt predictive maintenance, here’s what they can expect.
Machine Learning on the Endpoint
As mentioned above, some predictive maintenance uses AI or machine learning to make predictions. But what does that mean?
In predictive maintenance, machine learning models are deployed directly on the endpoint, making it easy to analyze local data in real time and make predictions about future problems. The AI runs in the background, continuously analyzing data and forecasting device health. Because it’s on the device, it detects problems quickly and protects user privacy since less information needs to be sent elsewhere for analysis. What’s more, it doesn’t need the internet to work, so even when the endpoint is disconnected from the network, the AI is still collecting, analyzing, and predicting.
As an example, Microsoft Intune has a feature called Endpoint Analytics that uses machine learning to monitor endpoint health. It works in the background, collecting and analyzing local data, flagging possible issues, then recommending a fix (like installing a patch). Microsoft Intune integrates with dedicated patch management tools like Adpativa OneSite Patch. Once Endpoint Analytics identifies that an endpoint needs a patch, it can trigger OneSite Patch to deploy and install the fix.
Integration With Zero Trust Security
Many companies follow Zero Truss principles when an endpoint connects to their network: verify the endpoint is safe before it can access anything. An endpoint’s health (its security posture, is it missing critical patches) plays a role in whether it can access the network,
Predictive maintenance can act as one of the security signals that’s integrated into your Zero Trust policies. For example, a device that’s showing signs of degraded performance may be vulnerable to security risks. The device can be flagged for quarantined for remediation before it’s allowed to access the network.
Self Healing Systems
Self-healing endpoints can detect, diagnose, and repair certain issues autonomously. When integrated with predictive maintenance, the device not only predicts a failure, it can also take proactive actions to remediate or prevent it.
For example, a potential sign of failure is flagged on an endpoint. It can run a preconfigured fix to resolve the issue, like restarting a device that’s stuck or rolling back a recent update that’s causing the endpoint to crash. The endpoint can also alert IT when it heals itself so the team can monitor and track recurrent or widespread issues that may require human intervention.
Predict and Prevent
Predictive maintenance is often a shift in how you maintain and repair your endpoints, but adopting this approach can extend the life of your endpoints, improve endpoint performance, and save your company money.
Adaptiva OneSite Patch can be part of your predictive maintenance program. Use it to automatically deploy and install critical patches to ensure every device on your network complies with your configuration requirements. Schedule a demo today.
