Big Data and Analytics

Data produced around the world increased exponentially especially after mobile communication and sensor technologies came into our lives. This amount is so large that every two years, more data is created than all the past years combined. This new data created is different than the usual. The usual data is more character based and the information stored is structural where the new type of data is more visual and audio themed and is not structural. New type of data also stands out from the usual data due to its large size and fast production speed.

On the other hand, this data is very important. It stores information about either a system or a person at the place it was created. Processing and making this data useful is an important asset that will get companies ahead of our their competitors.a

Processing this data using our current systems is not possible. That's why, new technologies are developed to process this new kind of data and determine useful different patterns and tendencies among different data. All of this new technology combined is called big data.

Big Data technology is based on Hadoop which collects all data from different sources in one place. Tools necessary to gather data into Hadoop, process, analyze and make data meaningful are all parts of big data.

An important new feature big data brings is the fact that data to be processed doesn't have to be user created. Of course, the user can use the data they created about their job but big data aims for the user to gather data from other sources (social media, GPS, etc.) and mix all of these up to make it meaningful.

Every company's method of using big data is different because every company's job, goal and operation method is different. Examples of several applications of big data in different industries include;

Health industry Comparison of information about various patients and determination of positive responses to various treatments by various patients, increasing the chance of healing and saving time by trying other treatments on patients with negative responses. Prediction of accidents and seasonal diseases due to mid season climate change and weather conditions, which leads to preparation for these situations and reduction of effects with warnings in time.

Insurance and finance The more information insurance companies know about their clients, the easier they come up with personalized solutions. Correct analysis of risks the client takes is the most important factor to determine the most suitable solution. Each client's risks can be analyzed separately and the best solutions can be offered using big data. The finance sector is no different. In order to offer the right products to their clients at the right times, financial institutions have to know their clients' goals well and merge this information with their economic state and the institution's goals. This can only be possible and maintained by big data applications.

Manufacturing industry Improvements in the protective maintenance processes using data from sensors, saving labor and money due to the evaluation of early warnings before malfunction, more precise demand predictions, more efficient planning of raw material and manufacturing processes.