What Is Big Data Analysis?

What is big data? Big data is a discipline which deals with methods to analyze, statistically manipulate, or otherwise exploit large amounts of real-time data sets which are too complex or large to be dealt with effectively by conventional data processing software. These techniques enable the extraction of insights from massive amounts of real time data. In simple terms, these techniques allow an analyst to make sense of the complex dynamics of complex and often unstructured data sets.

Big Data has the potential to transform our business and give us insight into completely new areas. In short, it lets us utilize the power of the web in a way that was unimaginable just a few years ago. Some of the most exciting applications in this area include creating new product concepts from large and complex data sources, analyzing consumer behavior, and extracting insights from financial markets. This is just the beginning. Although companies like Apache Hadoop and JBoss have done quite a lot to popularize the idea of big data, there are other areas for which it can be applied.

For example, several studies have demonstrated that human beings can be very efficient when it comes to analyzing data from Twitter, Facebook, and other popular social media sites. When participants took the time to analyze this new data, they were able to discern the relationships between people, groups, and institutions all through their favorite mediums. They were also able to make sense of natural phenomena such as changing temperatures, weather patterns, and even political unrest. While each of these studies used relatively the same data sources, the key thing that helped them to draw the conclusion that there is a relationship between temperature and politics.

What is big data analysis is not only about the practicality of what it can do for your company. It is also about what it can do for you. In order to take advantage of all the potential uses for the internet, whether you are a small business with just one or two machines, a medium sized corporation with thousands of machines, or a government agency with billions of dollars in data in its database, you need to have predictive analytics. Predictive analytics refers to the use of big data analysis for the purpose of making a better decision regarding the use case for your product or service.

For many businesses and corporations, the utilization of big data analysis is a way to turn raw, manual decision making into something more effective and perhaps even lucrative. To this end, the analysis of customer trends and their interactions with your product or service is essential. Without a thorough understanding of what is big data and how it can help you make decisions, you will have a very difficult time trying to capitalize on trends, which are inherent in all markets. For that reason, the use of tools to aid in the analysis of customer and market trends helps you make informed decisions that are economically and strategically sound.

An additional application of what is big data analysis is the utilization of tools to facilitate analysis of social media data. Many companies are currently leveraging large amounts of data to make better sense of what consumers are saying about their products or services, as well as what they are buying. Using social media sites such as Facebook and Twitter to gain insights into how consumers within those markets are interacting with one another is advantageous because these sites allow marketers to create profiles that allow them to interact with users and follow their comments or messages on a near real time basis. Tools that analyze this social media data and what is popular are what allows businesses to get a clearer picture of consumer behavior, allowing them to take steps that are most likely to yield the best results in terms of revenue and market penetration.

The final application of what is big data analysis is the creation of what is called a case study. A case study is essentially an analysis of what is popular, what is not popular, and what is working for other companies that have been studied in a similar situation. These case studies allow the company to learn from their competition and from their mistakes. The main reason why using a case study is important is because it forces the company to ask hard questions of their internal team and of external stakeholders, which gives insight into how the company should be run. In essence, this type of analysis is the process of asking a series of questions to find out what is working and what is not working and then analyzing whether those assumptions could be realized through some kind of change, whether by improving processes, changing business models, or by implementing some kind of initiative.

Data analysis applications that utilize supervised learning methods, commonly known as Mapreduce, are particularly well suited for use in large data sets where supervised data is needed. Mapreduce is a framework that is very easy to apply, using just one or two types of data cleansing techniques and one or two types of greedy, supervised learning strategies. The concept behind Mapreduce is that the user creates a supervised data set, such as a Twitter list, in which each member of the group submits tweets about the topics chosen, with these tweets being stored in a Map-reduce session. The Mapreduce application allows the user to define a greedy function, which when applied on the tweets in the Mapreduce session will cause the software to create a fold over the tweets, resulting in the creation of a new informative topic in the final stage of the program.