What is Big Data? Simply put, big data is the new age of computing. It is the next evolutionary step forward in how data is processed and shared. Many believe that it will revolutionize how we do business and lead to significant advancements in science, technology, medicine, and government… but what is big data really?
Big data is a generic term that refers to a new class of data analysis applications that address challenges specific to the large unstructured data set that pervades modern day business activities. Examples include Apache Hadoop, Spark, and Pallets. They enable analysis and aggregation of the enormous amount of structured data currently available on servers, workstations, and even smart phones. By unrolling the network and leveraging available technologies, big data can be used to support all sorts of business applications, from sales and marketing to customer service and e-commerce. This new class of applications is referred to as big data due to its size and complexity.
In order to understand what is the big data analysis, one must first understand how it works. Unlike traditional data analysis, big data analysis does not discard one type of data, but rather targets several different types. For example, big data analytics can analyze text, images, video, and metadata (such as titles and descriptions) from social media sites, relational databases, and the cloud. While previously this information was stored literally in text files on web servers, thanks to the advances in technology, it can now be accessed from any modern computer.
In addition, big data analytics enables users to access and manipulate these advanced analytics tools. For example, Twitter recently announced plans to release a tool called Twitdeck that allows its users to analyze their friends’ tweets. Twitdeck offers both visual and textual analytics capabilities, which are very valuable to the Twitter user who wishes to understand what their friends are saying. It also offers links to the users’ profiles, which further enriches the social media site’s capabilities. Google has also recently announced an application called Google Trends that will allow users to see how popular certain keywords are becoming. The combination of the social media sites and the advanced analytics tools already available from Twitter, Google, and Yahoo! provides a richer picture of what people are searching for and communicating with on the Internet.
However, there is some debate about what is not big data. Some analysts argue that there is too much noise in the results of studies that analyze descriptive factors, such as content, instead of looking at quantitative features, such as frequency of use or sales volume. Proponents of big data analysis say that such techniques allow researchers to extract important information that can then be used to guide strategic decisions. Proponents of traditional statistical methods say that although there is some statistical noise, the underlying structure of the data still provides strong relationships between the variable and its correlated outcomes. Those who are against big data often point out that no empirical tests have ever proven the effectiveness of it and that the true test of the scientific method is not how you analyze a bunch of data, but how you interpret it once you’ve analyzed it. Those who support the idea of testing how effective the methods are point out that traditional statistical methods are extremely prone to statistical biases and mathematical errors, while big data has already been tested and is generally accurate.
Analyzing large amounts of data, especially real time data, poses unique challenges that weren’t necessarily present when analyzing smaller quantities. Traditional statistical methodologies were unable to provide insights into what is truly going on with a particular piece of data because of the sheer volume of the data available. The advent of big data analysis paved the way for a more robust scientific research and analysis system that continue to this day. There are many companies that have developed analytical technologies that can provide quantitative insights into the stock market. These technologies enable those researchers and developers to mine information to search for and identify patterns and trends. Often these trends and patterns can be used to generate predictions about future stock market activity.
With today’s emphasis on analytics in all aspects of business and society, it seems that there will be an increasing demand for individuals and companies who are able to tap into and utilize this new tool. While some companies have moved away from the traditional scientific model in order to focus more on modeling and leveraging complex data sets, there are still a lot of companies that have been using the traditional scientific methodologies for analyzing stock market data. One challenge with big data analytics has been the difficulty of collecting and interpreting empirical evidence that would support the validity of the conclusions drawn. Another major issue with the scientific model is the enormous time required to conduct and interpret the analysis. Without the need to hire a large team of scientists and programmers, there is no way for the typical company to realistically spend the resources necessary to effectively analyze stock market data.
On top of needing a sizable budget in order to hire a team of scientists and engineers to devote the necessary time to collecting and interpreting the empirical evidence, there is also a need to develop large amounts of real time analytics software in order to make sense of the large amount of data. In many cases, the company does not even have to invest in high end, proprietary software since it can simply use a commercial off the shelf solution that is designed to effectively handle big data analytics. There are many different types of solutions available to businesses and individuals seeking to analyze social media data. However, before investing large amounts of time and money into the analysis of social media, the company needs to determine what kind of data is important and what kinds of data will be useful.