What is Big Data? If you are a business owner, then I’m sure you must have heard about it at least once. It has been in the news a lot recently because of the great potential of this technology for businesses and has been talked about by the top economists and venture capitalists. But what exactly is it?
“Big Data” is really a term that refers to unstructured or geographically-rich data sets that are obtained through different technologies. Examples include telecommunications companies’ usage of the mobile phones’ GPS technology (which can be considered as structured as well), retail sales and purchase histories, consumer shopping patterns and preferences over the internet and so on. The term has also been used to refer to stock market trading databases. In general, the Big Data concepts are those that stem from statistical principles and machine-learning techniques – the latter of which often utilizes Artificial Intelligence techniques.
How can we best utilize the big data? There are two ways on how we can utilize the large volumes of unstructured or semi-structured data. The first way is to make use of it for analytical purposes and for the other, we can make use of it for business decisions and strategies. In order to have a clear understanding of what is big data, let us look at its characteristics. Big Data Properties and Characteristics
The key characteristics of big data visualization/data mining are: large data volume, high degree of unpredictability, and relatively low degrees of connectivity. The second characteristic is actually the key attraction of this form of analysis or mining – the fact that it allows the extraction of qualitative or quantitative characteristics. Data mining and big data visualization is thus a two-way street where the objectives and the methodologies overlap.
However, data mining and big data visualization is not the same as data transformation/interpreting. Big data visualization primarily deals with visualizing and extracting information from large structured data sets. It usually involves a fairly complex set of algorithms, the main one being the inverseskull function. It also involves using some statistical or algorithmic techniques. However, data mining involves the extraction of structured information, not necessarily the same kind of information, but the same sort of information. It involves statistical methods such as geometric or logistic regression or fuzzy logic.
When we talk about growing exponentially, we are actually talking about the characteristics of increasing volume or the increase in the total number of records over time. One of the characteristics of big data is its volume – it grows exponentially in both directions (up and down) without the need for further extrapolations. This characteristic is rather unique among all the characteristics of big data. Most of the traditional methods of data analysis do not allow for this. Some of them even consider volume as a proxy for quality (i.e., a higher quality result can be obtained by having fewer records).
The second characteristic that makes big data unique is its flexibility and the ability to fit into various existing formats. Data visualization tools such as Visual Studio C# and Power BI can convert unstructured or semi-structured data into both structured and visual information. Moreover, most of these tools are open source, and they make exploitation of the new data much easier. Since these tools are easily available on the Internet, their use in the enterprise is rapidly increasing.
The final characteristic that makes big data unique is its speed. Traditional data analysis methods take a lot of time to perform – they may even consume more time when transformations are involved. However, the availability of new data and the ability of computers to process and visualize them have made the task much simpler. Computers are now able to achieve levels of accuracy and processing speeds that were previously reserved for the most seasoned IT professionals. This has allowed the transformation of unstructured or semi-structured data into structured data in a matter of hours, instead of weeks or months.