The Big Data

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 is the act of compiling large sets of data based on a single individual or groups. Everyone encounters data in their daily life--you are experiencing it when you log onto a social media account, when you stream entertainment online, or even when you are online shopping. When you do any of these things you are leaving behind a digital trace that can be accessed by just about anyone. In “Six Provocations for Big Data,” danah boyd and Kate Crawford raise questions regarding the nature of Big Data. What is considered public information? What is the ethical way to go about retrieving data from online sources? Is Big Data more harmful or helpful? How often do you encounter Big Data, or data in general? What is the relationship between data

Like Luna et al., Kuo, Sahama, Kushniruk, Borycki, & Grunwell (2014) note the inconsistency of the definition of big data in research. They, however, say this inconsistency is because big data is an evolving concept. They describe big data as data that is so large, so complex, and growing so fast that traditional data management methods cannot hold or organize it. Kuo et al.’s and Luna et al.’s definitions varied among syntax, but both stressed the complex nature of big data. This variation seemed to be a trend among several of the researchers. While each researcher’s definitions somewhat differed, each seemed to agree that big data is data that is so complicated that traditional methods cannot handle it. The researchers continued to agree that the complex health data available today falls under the definition of big data.

What is Big Data? Big Data is the mass collection of user data by mathematical algorithms, databases, data mining, and the use of datasets that were once believed to be static and unusable. Big Data’s history goes way back “…70 years to the first attempts to quantify the growth rate in the volume of data, or what has popularly been known as the “information explosion” (Press, Gil).” Researchers had predicted the massive growth of information and how our ability to collect and store it would need to continue to grow as well.

What is big data? Big data is structured and unstructured data that is difficult to process using traditional database and software techniques. This is because of its extensive size. Big data ranges “from a few dozen terabytes to many petabytes of data in a single data set – and are constantly growing” (Hopp). A terabyte is equal to 1,024 gigabytes, while a petabyte is equal to 1,024 terabytes. A regular iPhone has 16 gigabytes, so a terabyte contains the same amount of digital storage as 64 iPhones, while a petabyte contains the same amount of digital storage as 65,536 iPhones! Structured data is in a fixed field within a record or file (usually databases or spreadsheets). Unstructured data is unorganized and hard to interpret by traditional databases or data models (like photos, webpages and emails). Structured data is a lot easier to work with and can be easily classified, so it is preferred in big data over unstructured data.

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 can mean different things to different people/organisations – one organisation’s big data of a few terabytes may seem small compared to another organisation’s big datasets in petabytes or exabytes. Big Data is typically described by the following characteristics, known as the “5 Vs”:

Big data is an element that allows companies to leverage high volume data effectively and not in isolation. Big data needs to be quickly accessible and have the ability to be analyzed. Data stores or warehouses are one way data is managed that is persistent, protected and available as long as the data is needed. The forefather to data stores is relational data bases, relational data bases put in place decades ago are still in use today

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.

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 defined as “large data sets or to systems and solutions developed to manage such large accumulations of data, as well as for the branch of computing devoted to this development.” (“Big Data”) This definition of big data was not added to the dictionary until 2014. The next big thing in business analytics is a relatively new, yet, explosive business practice known as data mining: the collection and analysis of big data. (Fayyad) These large, seemingly random, sets of data are condensed and analyzed for patterns and trends by people with a very broad set of skills. These people are known as data scientists and are considered unicorns in today’s job market.

Presence of big data is a very common phenomenon now days, specially when talking about medium to large size corporation. Manyika et al., in their article (James Manyika, 2011) defined the term big data as “large pools of data that can be captured, communicated, aggregated, stored, and analyzed”. To clarify they suggested that big data refers to data, whose size makes it impossible to be processed by the typical software used for database management. Gartner (Gartner, 2012)defined big data in terms of its characteristics of high volume, high velocity and high variety. By volume, he referred to the size of the data, by velocity he referred to the speed at which the data is created and by variety he referred to the range of types of data.

Big Data is the term which is used for data sets to make a very large application for processing the data. The Processing may involve analysis, search, sharing, storage, visualization and privacy information. Big data is a type of predictive analysis which is used for the purpose of extract the value from data. The data sets are used for the purpose of analysis to find the new correlation of business trends, for the prevention of diseases and so on.

The massive amount of data available is where the term “big” data comes from. The processing power of a typical server is not vast enough to store this data; new technologies help to ease this burden. To put into perspective how much data is out there, six billion people today have cell phones that are transmitting data. Most United States company currently store a minimum of one-hundred terabytes. It is estimated that by the year 2020, there will be forty zettabytes of data available. This is three-hundred times the amount available in twelve years ago.

“Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.” (Dumbill, "What is big data?", 2012).