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How does data science work?


The introduction of big data changed the dynamics of the world however the increased

volume also created issues for its storage. It was a challenging task for the organizations

to create a framework and way out to store data. Data Science can answer all the data-

related concerns, therefore it is important to study how it works.

What is Data Science?


In 2013, it was reported that 90% of the world’s entire data was built during 2011-2013. In

a timeframe of two years, data scientists collected and processed 9 times more

information than the combination of the previous 92,000 years. Researches show that by

the end of 2018 there was a global collection of 18 zettabytes of data, and the

expectation is that by the end of 2025 this number will be almost 10 times greater,

reaching 175 zettabytes. To have an idea of what this represents, if someone decided to

download 175 ZB at the average current internet connection speed, it would take 1.8

billion years to complete.



Image source: Sordum


Data science as a field that is growing swiftly with an aim to transform industries. It is difficult to define the term data science, because the concept is very broad. However, in simpler words, it is defined as the extraction of useful insights from unprocessed data. As the CEO of Siemens, Joe Kaeser, says, data is the oil of the 21st century, having significant importance in our daily lives, research, and businesses.


There is no doubt that every human being is contributing to the data sciences by their Google searches, social media posts, health and fitness trackers, etc., for even the people who do not have access to this sort of resource become a source of data by not accessing the internet. With the help of data received on a daily basis, data scientists form connections and patterns to develop new products, deliver important information, and make human lives much easier.


How does it work?


Data Science produces concentrated, rigorous, and comprehensive insights from raw data through a plethora of expertise. The skills required for data scientists include math, advanced computing, data engineering, statistics, and visualization to separate actionable information, which can produce innovative and efficient solutions.


Another important source for a data scientist is artificial intelligence, particularly the subareas of deep learning and machine learning. The focus is to build models and draw forecasts based on algorithms and other useful techniques.


Data Science and Monitoring



Image source: Adapted from upklyak


Businesses today can leverage a lot from the potential of AI and data science in the field of monitoring and evaluation, to strengthen productivity, efficiency, and agility. Many IT companies that own data centers, such as Google, Facebook, Microsoft, Amazon, and non-IT companies that produce the huge volume of data per day, are willing to adopt concepts and techniques that can help in process optimization and data clean up, product maintenance and support, and performance analysis.


MCG offers IT Infrastructure Monitoring solutions to help customers in optimizing their IT infrastructure components that are within their data centers. ITIM tools devised by MCG are well-suited to deliver performance that can help the client in achieving their business goals. Additionally, for the clients with existing monitoring solutions, ITIM tools are tweaked to provide better utilization, and integration to the current system.


Furthermore, MCG offers Network Performance Monitoring and Diagnostic tools to help teams working in IT and network operations. The idea is to support the IT teams in understanding the current behaviors of the network and its elements regarding traffic demands and network application.


MCG offers two more monitoring solutions, Application Performance Monitoring and Digital Experience Monitoring, and both aim towards making client experience as smooth as possible. Such product solutions are designed to cater the needs of today’s technological arena, which is evolving rapidly and relying on data science more than ever.


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