Maintaining IT infrastructure has transformed from a largely manual process to an AI-driven approach to system maintenance. This benefit is great news for users, support technicians, and developers alike, who benefit from increased efficiency and reduced downtime.
Leveraging machine learning (ML) can reduce operational costs, too. Businesses that leverage IT maintenance, as 51% of companies say that they use predictive maintenance to improve uptime, and 11% say they conduct maintenance to reduce costs.
Utilizing advanced algorithms can improve productivity by identifying areas for improvement within IT environments, too. Blending historical and real-time data in this way is sure to lead to unexpected operational gains, too, due to the increased availability of data thanks to machine learning algorithms.
ML and Maintenance
Artificial intelligence has come a long way since its inception in 1955. Today machine learning can identify patterns in enormous data sets, replicate human cognition, and improve the function of any program that collects data. There are a few different types of ML programs that can be used to enhance predictive maintenance in IT environments, including:
- Supervised Learning: This method involves feeding an ML program sample data to learn from. Given access to enough data, the ML program can begin to produce the desired results.
- Unsupervised Learning: Much like humans, ML programs are capable of “learning” within largely unrestricted environments. In this scenario, the ML program finds patterns to produce desirable results.
- Semi-Supervised: ML programs sometimes require a small sample of labeled data to get the ball rolling. This approach is called “semi-supervised” and allows for greater control.
- Reinforced: Tools like Google’s DeepMind’s Deep Q-network use a trial-and-error approach to reinforce learning and improve the accuracy of ML operations.
Artificial intelligence tools that use ML are highly accurate and can improve the resilience of your IT infrastructure. This is all but imperative in the modern world of computing and data analytics, where the smooth functioning of digital assets is central to many company’s continuity.
Benefits of Predictive Maintenance and ML
Maintaining your IT environment requires a proactive approach to security and fault detection. Utilizing predictive maintenance can aid your efforts to reduce downtime, increase efficiency, and build a more effective IT environment. You can implement ML-drive predictive maintenance today by:
- Collecting Data: Devices connected to the IoT are constantly collecting usable data that can be used to detect faults and inefficiencies. You can empower the efforts of support technicians by using ML to crunch the numbers and produce usable reports.
- Data Analysis: Once you’ve collected data, you need an ML program to make sense of it. Advanced ML programs can clean your data, detect patterns, and alert you to changes in real-time data collection.
- Maintenance Planning: Figuring out that software or hardware like IoT devices need maintenance weeks before they begin to degrade can save you time and money. By keeping track of subtle changes in data, you can spot signs of degradation and take proactive action.
These steps will keep you ahead of schedule when repairing essential equipment or software. They’ll also aid your efforts to improve your IT environment, as greater access to data can help you identify areas for improvement. However, when pulling data from your mainframe, you will have to account for challenges associated with AI, including:
- Navigating the legacy nature of many older mainframes
- Properly following governance guidance to avoid legal issues
- Understanding and implementing GDPR/California Privacy Protection laws
Negotiating these challenges successfully will empower your data scientists, enhance the efforts of your IT support technicians, and reduce your risk of falling foul of legal issues. A proactive approach to ML implementation and predictive maintenance can help you communicate your maintenance plan to the wider company.
Communicating Your Maintenance Plan
IT specialists aren’t the only stakeholders that should be involved in your predictive maintenance plan. Depending on the scale of your company, you’ll also need to involve a host of employees from a range of departments. If you’re concerned that people will see ML-driven predictive maintenance as an obstacle to productivity, consider taking steps to visualize your plan. You can utilize process visualization today by:
- Clearly identifying the problem(s) you face;
- Gather data and use charts, graphs, and sticky notes to visualize the information you need to communicate;
- Brainstorm solutions with the stakeholders that will be impacted by your maintenance plan;
- Select a solution that will empower IT specialists and cause minimal disruption;
- Visualize this plan using flowcharts, workflow boards, or process models.
Visualizing your maintenance plan can increase stakeholder buy-in and appease those in departments like sales, finance, or HR. The best predictive maintenance plans can only be supported if stakeholders cooperate with your maintenance solutions.
Conclusion
Machine learning is at the backbone of modern maintenance efforts. Taking a proactive approach to implementing ML can help you collect more relevant data and spot inefficiencies in your current operations. When leveraged correctly, these data sets reduce downtime, enhance productivity, and can be easily visualized for those who don’t work in data science or IT.