Category Archives: Predictive Analytics

Comparison Corrective, Preventive and Predictive Maintenance

Comparisons between Corrective, Preventive and Predictive IT Maintenance

By | IT Operations, Predictive Analytics | No Comments

Keeping the lights up for the IT team is a never ending job. With the advent cloud and Internet of Things, support and maintenance tasks and costs are impacting the companies’ workforce and budget.

Attempts to reduce these costs have led to the development of several maintenance strategies and solutions along time. There are three types of common IT maintenance practices, offered by IT Managed Service Provider (MSP):

  1. Corrective Maintenance implies that the IT equipment and devices are repaired after a failure has occurred. As long as an equipment is under a warranty agreement, the equipment owner does not usually pay for the repair though he can experience an unexpected malfunction.
  2. Preventive Maintenance requires performing periodic inspections and other operations at a schedule predetermined by the service provider mostly on the basis of time in service.
  3. Predictive Maintenance, which heavily rely on machine learning system and statistical model, can schedule an intervention based on some sensory information representing the current condition of the IT equipment, devices and its subsystems and a probabiliy of failure. This approach should, on one hand, minimize the risk of unexpected failures, which may occur before the next periodic maintenance operation, and on the other hand, reduce the amount of unnecessary preventive maintenance activities.

As when the business grows, the IT infrastructure and applications implementation gets into complex integration. The failure touch points are beyond IT team to comprehend.  Hence, this is where the predictive analytics come in with the capability to understand reliability of equipment’s at any point in time, to identify and isolate potential failures before they occur; to predict and plan for scheduled maintenance and downtime and to reduce unnecessary time-based maintenance operations is required.

This is the new era of IT support and maintenance – there will be a tremendous reduction of manpower to perform predictive maintenance in the future, except if there is a requirement of physical intervention. An alarming state for human brain and who is to blame?


By | General Insights, IT Operations, Predictive Analytics | No Comments

Many of traditional Managed Service Provider (MSP) engagements delivered around fixed-pricing models, a big junk of the costs from this pricing model is the cost of IT labor.

For MSP to maintain its profitability, one well-known MSP company, InfoSys  has been making massive investments in software to automate as many processes as possible. As a result, the need to take thousands of people out of the process.

The software automation is geared towards Smart and Predictive Managed Services.  It features the big data analytics services capability in capturing massive amounts of machine data that can be used to diagnose potential problems long before they occur. Additionally profound technical advance is machine learning algorithms that promise to automate IT operations at levels of unprecedented scale.

While  MSP is forcing to look at the profitability and find ways to be more cost-effective, the customer is demanded to reduce the stress and overhead of IT security.

From that perspective, many “agentless” IT monitoring tools are starting to incorporate machine learning algorithms to identify and help resolve potential issues before they become a problem.

For MSPs, the goal going forward is no longer how quickly they can fix an IT issue, but rather what can be done to prevent it from occurring in the first place. Given the current level of IT complexity most organizations face today, there is an assumption that something will go wrong with IT sooner or later. It’s the role of the MSP to mitigate the impact of that complexity on the business.

Evolution of IT monitoring and management systems

By | General Insights, Predictive Analytics | No Comments


Some of the driving factors why there is an increasing trend and demand in IT Operation Analytics.

  1.  One is the people. Traditionally operational folks are recognized by their ability to solve problems. However, the amount of problems is growing due to the complexity and rapid pace of change in IT environments, while the amount of people is not (the infamous “do more with less” strategy), so you need to find a way to reduce the amount of fires.
  2. Another factor is high complexity. The modern infrastructure combines elements of physical/virtual/cloud environments. The infrastructure and software used is multi-tiered, and mixes legacy and modern technologies. On top of each environment component comes with tons of configuration to accommodate needs of different users or clients. All of them switched to DevOps and Agile development, changing their environment and systems every day. Complex environments which change all the time increase the number of fires that need to be put out.

One of the approaches to reduce fires is to recognize what is happening in your environment that can lead to trouble. Big data and IT analytics are part of that. If you want to identify issues proactively — performance, capability, and security — you need to use all of the IT operational data and analyze it.


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