The term Big Data appeared in 2008. For the first time it was used by the editor of the journal Nature – Clifford Lynch. He talked about the explosive growth of world information and noted that they should master their new tools and more advanced technologies.
What is Big Data?
To understand big data, you need to define the concept and its function in marketing. These days, they generate data on a regular basis: when they open an application, search on Google, shop online, or just travel with a smartphone in their pocket. The result is huge amounts of valuable information that companies collect, analyze and visualize.
What is big data and why do you need it?
Big Data is translated into Russian as “Big Data”. This term defines bodies of information that cannot be analyzed or analyzed using methods using human labor and desktop computers. The peculiarity of big data analytics solution is that the data array continues to grow exponentially over time; therefore, the processing power of supercomputers is required for the operational analysis of the collected materials.
Consequently, processing big data requires cost-effective, innovative methods of processing information and delivering inferences.
But why put so much effort into organizing and analyzing Big Data? Big data analytics is used to understand the attractiveness of goods and services, predict market demand and reaction to an advertising campaign. Working with Big Data helps firms attract more customers and increase revenues, use resources efficiently and build a competitive business strategy.
This means that analysts who can extract useful information from big data are now snapped up. You can learn this even if you have never worked in IT. For example, GeekBrains’ Big Data Analytics Faculty offers convenient online classes and a dozen portfolio cases. By the way, the first six months of training are free. Those who have successfully completed the course will definitely be employed – this is spelled out in the contract.
What tasks do Big Data technologies solve?
The area of my professional activity is the complex provision of information security of automated systems. Therefore, I will give several examples of solving urgent problems using Big Data technologies in the context of information protection and countering high-tech fraud:
- collection and analysis of information about anomalies in the internal network in near real time, based on the technical data of the equipment of the link and network layer of the OSI model;
- counteracting fraud against holders of plastic bank cards;
- monitoring and evaluating the effectiveness of the information security system using analytical models tested in related organizations;
- categorization of employees in accordance with the internal intruder model and dynamic redistribution based on additional information received;
- information security risk assessment (automatic filling of scorecards according to the methods of auditors or risk managers).
Analyzing and responding to customer reviews
A customer can leave feedback after contacting the customer support center or through a feedback form, but they are much more likely to share their opinion through social media. Big data tools can analyze this public information and collect all references to the bank’s brand in order to be able to quickly and adequately respond to it and get more info. When customers see that the bank hears and appreciates their opinion and makes those improvements that are required, their loyalty increases significantly.