The World 's Data Pool

Big Data is an expansive phrase for data sets so called big, large or complex that they are very difficult to process using traditional data processing applications. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. In common usage, the term big data has largely come to refer simply to the use of predictive analytics. Big data is a set of techniques and technologies that need or require new forms of integration to expose large invisible values from large datasets that are diverse, complex, and of a massive scale. When big data is effectively and efficiently captured, processed, and analyzed, companies

Big data and its definition has changed over the years. In a 2011 research project by MGI and Mckinsey’s Business’ defined big data as

Big data is nothing but collecting of datasets. Organizations in current world demands data to be broken down which can used to get more high effectiveness and benefit. Big data refers to the large amounts of data which collected from various devices such as mobiles, sensors and social media etc. Generally, large amount of data have been regenerating by IT industry such as satellite data, mobile devices and etc. This data is being growing rapidly day by day and it would be referred as Big Data.

In a fast paced, business ordinated technological world the overall welfare of a company is tied to the success or failure to make the tough decisions. On one instance a company’s CEO might be able to make the choices based on experience, advice, or simple gut instinct. However, this is not the only skill one needs. There is a great deal of information to be found in being able to see investments in data and analytics. These decisions are based off of big data. Big data is a catch-phrase, used to describe a massive volume of both structured and unstructured data that is too large to process using traditional database and software techniques. The volume of data is in most cases is too big, moves too fast or it exceed the processing capacity the company has. Despite these potential drawbacks, big data contains the potential to help companies by improving operations and making faster, more intelligent decisions. This can be broken into three key parts, knowledge, data, and information.

The industry is inundated with articles on big data. Big data news is no longer confined to the technical web pages. You can read about big data in the mainstream business publications such as Forbes and The Economist. Each week the media reports on breakthroughs, startups, funding and customer use cases. No matter your source for information on big data, one thing they all have in common is that the amount of information an organization will manage is only going to increase; this is what’s driving the ‘big data’ movement.

Big data is not as new as many people believe it to be. It is actually a concept that has been around for almost a century. It is just the “same old data marketers have always used, and it’s not all that big, and it’s something we should be embracing, not fearing” (Arthur). In 1944, Fremont Rider “predicted that the amount of data in the world would increase exponentially” (Hopp). Rider was right on target with his prediction seventy years ago. Data has grown much greater than he probably could have ever imagined back then.

Big data: What is considered “big data” varies depending on the capabilities of the users and their tools, and continuous data generation make Big Data a moving target. Thus, what currently is considered to be "Big Data" doesn’t seem to be same in the coming days.

What is the definition of big data? What the difference between big data and data? Figuring out these two questions can help us to comprehend big data in depth. Big data has been defined as the data that is too big to be analyzed by traditional methods. Generally speaking, there are five characteristics for big data: data volume, data variety, data velocity, data variability, and data complexity, which were also known as the five dimensions for big data.3 Data volume means that the measurement of the units of data storage. Data variety indicates the diverse forms of data. Data velocity has been defined as the velocity of data producing and processing. Data variability means that the data flow could be inconsistent. Data complexity indicates that the data is hard to analyze. Also, the five dimensions of big data can be seen as the differences between big data and data. Figure 1.13 shows the dimension diagram for big data.

Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business – and society – as the Internet has become. Why? More data may lead to more accurate analyses. More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies.

Big data is an extremely important topic for future developments, growth trends and similarities between certain things. From a Microsoft blog published in 2013 big data is “the process of applying serious computing power” (HowieT, 2013). Another article describes big data as data that “exceeds the processing capacity of conventional database systems” (Dumbill, 2012). Based on these definitions and many more alike, big data refers to or can be described as recorded information that exceeds capacity. As brief as this is, data can be recorded using many instruments and even through observation. This topic is interesting to research and develop as new technologies are more capable at storing and reading mass data. With technology advancements, a method that took half a day, more than ten years ago, would only take a couple of minutes using present technologies. As big data is getting more widely used more businesses and enterprises will be interested in the trends shown.

The world is changing with respect to the growth in big data and to the way in which it is used. Growth in big data brings with it many challenges, but it also presents new opportunities. Figure 1, helps understand some of the big data related activities that are taking place in the world with respect to volume of data that is being consumed by these activities over the next 5 years.

Big data refers to the growing data a company experiences but this data has to be managed and processed to convert it into useful information. For example, a company XYZ just launched an e-commerce website and its receiving a

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

Big data is not just about having large amounts of data, but it also refers to the complexity of data sets fetched from multiple sources, where traditional data processing methods cannot be sufficient to process the data, and thus requires advanced computing tools and technologies which can be acquired by using distributed processing and cloud technology. In general, Big data excels in three areas: Volume, Variety and Velocity. Volume and Variety is when you are dealing with a huge quantity of data, in all types of forms and all types of sources. Velocity is about additional capabilities offered by distributed processing, which drastically accelerates the computing speed. But, when it comes to logistics, Big data is more than just 3Vs. It goes beyond and presents real-world use cases, revealing what is happening now and what could possibly happen in the future. Big data is often unstructured, so you definitely need advance mechanism for interpreting the data. The major challenges faced are data analysing, capturing, curating, storing, searching, querying, updating, sharing, transferring, visualising and security. As of 2012, the size of Big data ranges from a few terabytes to dozens of petabytes.

Big data analytics is the use of advanced analytic techniques against very large and diverse data sets. Data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency are termed as big data. Big data comes