Traditional IT infrastructure monitoring is starting to show its age. With machine learning, predictive analytics, and the ability to use time series databases, IT infrastructure monitoring should be much further ahead than it currently is. The reality of the situation is that the modern landscape has changed, and IT infrastructure will have to change with it.
The main challenge that companies are facing is how to approach IT infrastructure in a way that jives with what is currently happening in the industry. For example, there have been many infrastructure outages in recent years, and machine learning can help solve those problems. It will depend on whether companies are willing to use tools like time series databases and predictive analytics to improve things.
IT Infrastructure Is Difficult
The main problem is that modern IT infrastructure is quite complicated and vulnerable. Even multibillion-dollar companies like AWS still have considerable downtime every month. The biggest websites on the Internet face disruptions because of their infrastructure. The only way to change it will be to employ the use of predictive analytics and a quality data science platform that allows them to harness the massive amount of computing power available on the Internet.
Companies are beginning to figure out that downtime has a massive impact on their ability to do business. On top of that, there are also figuring out that it damages their reputation significantly, and it will cause a lot of problems for them as time goes on. It is for these reasons why having machine learning as a part of their IT infrastructure monitoring is so valuable.
Using Time-Series DBs and Machine Learning to Help Predict the Future
Current tools are vendor-dependent, meaning you will be locked into one type of IT monitoring tool if you aren’t careful. These tools are typically not what you find in a data science platform as well. What that means is that they are less likely to have features like predictive analytics and machine learning capability. However, there are specific steps that these tools can take to deliver that performance.
It usually lies in being able to use time series databases that are then run into machine learning algorithms. By focusing on these algorithms, you essentially gain the ability to predict downtimes and prepare for them.
Gain Actionable Insights Using Machine Learning
Ultimately, all machine learning algorithms and predictive analytics applications are there to serve some business need. The purpose of these in IT infrastructure monitoring is to help you identify anomalies and make corrections before it leads to your IT infrastructure crashing. By using these predictive technologies, you will be able to figure out when a problem is coming and stop it from happening.
It will also help you find the bottlenecks in your system, making it possible to debug them and turn them into a source of strength. You will have massive amounts of data to play with, and it will help you in ways that you didn’t think were possible.