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The term Big Data is quite young, it appeared in 2008. However, the phenomenon itself, which Big Data describes, appeared much earlier. Big data implies huge amounts of information with a complex definite and / or indefinite structure. Big data previously was not of great value, since its processing and analysis were quite complicated thing to do – it required significant computing power, long time and large financial costs. Everything changed when the technology of processing multi-gigabyte arrays of information in fast RAM appeared, and then the profession – big data analyst.
Table of Contents
Sources of big data
There are three sources of big data. Interesting to know, that every source is becoming more and more popular.
- Social Media Data. Social networks are confidently gaining number of users. Now Facebook is used by more than 1 billion people every day. And it is not a limit. Every minute, this gigantic number of people produces tons of information that consist of likes, comments, tweets and other similar actions.
- Transactional data refers to businessmen and different business actions, like payments, storage records or transactions that are committing both offline and online.
- Machine data refers to information from GPS navigators, industrial equipment and similar devices.
Why do we need Big Data Analytics
First of all, big data analytics is used mostly in TV and financial markets. Big data analytics allows to process extremely useful information from structured and unstructured data. For example, with big data’s help, business can define trends, optimize budget etc.
Today, common volume of information on the internet equals 40 zettabytes (1 zb = 1 billion gigabytes. And by 2025 this number will increase to 400 zettabytes. Now, try to imagine, how much useful information does Big data have for every sphere in business.
How does it work
This is a bit obvious, but Big Data Analytics is not a one-stage process. To receive the information that is always so useful for us, data has to go through multiple levels, starting from its natural condition and ending with the needed state.
- Everything starts with the data specification. This stage is responsible for sorting out the important information and its source.
- After that goes data acquisition, that collects all the important data. For example, about the user.
- When the data is collected, it is necessary to sort the data into different categories.
- Of course, it is also important to clean the data, so there will be only qualitative parts without any defects or redundant elements.
- Then, comes analysing, which helps to fully understand all the characteristics of data and its nature and determine key features that will be needed for the next stage.
- Data modeling takes these key features and use them to generate formulas and algorithms.
- On the final stages, system creates a test model that acts like a business problem, so that they can try to predict its result, outcome.
- Finally, after all these stages, data will be delivered in various forms.