Every day, servers process and store a vast volume of data. Companies need to organize and manage this information. It's a significant source of knowledge that is often hidden but is becoming an essential part of a company's strategy.
We could say that, thanks to Big Data, companies can gain extensive knowledge based on all structured, semi-structured, and unstructured data in their environment.
Likewise, we can say that all this information is processed based on the 5 Vs of Big Data: Volume, Variety, Velocity, Veracity, and Value of the data. I mention all of this because of the importance of understanding this information in detail and, most importantly, how we need to handle it.
Well, as everyone knows, all this information is hosted in Data Centers, so they need to be prepared to manage it properly and provide a service that meets the demands of new business models, given the paradigm shift we have encountered.
Naturally, Data Centers continue to support all their clients, but every day, it's more necessary to have tools that optimize and automate critical infrastructures and improve the quality of services and support with more comprehensive, even predictive, real-time information.
Hence, the importance of AI and its learning patterns, with which we can, on the one hand, analyze large volumes of data and, on the other hand, make operations, designs, planning, maintenance, etc., of critical infrastructures more effective.
For Data Center managers, making decisions is part of their responsibility, but now it's also absolutely necessary to make them based on data, to be sure that the decision is the right one. In an increasingly competitive world, making decisions based on predictions and analyses generated by Artificial Intelligence will be crucial.
We can say that with AI and automated services, it's easier to understand the normal behavior of networks, while also enhancing their security, reliability, efficiency, and all of this with lower energy consumption.
Advances in Machine Learning, which are closely related to AI, are making it possible for computers to have an increasing capacity for prediction and classification every day through algorithms. This is good news, for example, for Data Center maintenance services, making all operations and tasks much more profitable and efficient to provide a top-level service.
AI-driven hardware optimization is crucial for the industry. In the very near future, almost 100% of IT assets will run autonomously through AI. The sector will no longer rely solely on physical equipment to perform specific functions, as these devices (hardware) will have learning capabilities, providing them with business intelligence and making them more independent with each passing day.
The entire environment of Deep Learning in direct connection with AI is another crucial aspect within critical infrastructures, thanks to its learning capacity. Unsupervised learning adds value to the management of critical infrastructure, offering, among other things, cybersecurity through real-time threat detection, prediction, and prevention for Data Centers.
There are numerous significant advantages of Artificial Intelligence in critical infrastructures. In conclusion, I'd like to mention that at Bjumper, we can provide information on how, through a solution that combines hardware with learning capabilities and algorithms integrated into various AI technologies, we can enhance the management of the Data Center's climate (a crucial element for profitability and efficiency in critical infrastructures) by conducting a Study and Analysis of the Data Center's Climate. Analytical Climate Study Application
Today, we believe that optimizing the climate in Data Centers should be an obligation, not just a recommendation, in order to achieve greater benefits, improved efficiency, and a stronger commitment to the environment.
Combining profitability and environmental improvement – what more do you need to get started?
Let it work for you!
