Big Data Processing Capacity Of Conventional Database Systems Data

Gathering of values and variables which are related in some sense and differing in other sense is called as “DATA”. In recent days it is observed that size of data has been increasing. The quantity of data that is increasing for very two days is equal to the amount of data that has been produced until 2003. The year 2007 was the first year in which we were unable to store the data that we produced. This increase in size of data is proportional to the increase in the size of database. This lead to a

“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.” (SAS, 1)

New organizations can leverage Big Data in such a way that it provides benefits that can prove valuable especially to an organization seeking to establish itself and grow. One of these benefits is faster and better decision making. Particularly with the analytics of Big Data, organizations have access to real-time data

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.

The amount of data in our world has been rapidly increasing and analyzing these large data sets, or big data, has become crucial for businesses in increasing their success. Many businesses use big data to model their business structures, control processes, and run the business. The availability of this data leads to a more accurate analysis of the target market. More accurate analyses lead to more confident decision making and better decisions means greater operational efficiencies, cost reductions and reduced risk. There are many ways in which big data can be successfully implemented in an organization. Big data allows businesses to segment their target market, creating more precisely tailored products and services. Big data is also used to conduct controlled experiments to make better management decisions. Finally, big data can unlock value by making the captured information transparent and usable at much higher frequency (Manyika, “Big data: The next frontier for innovation, competition, and productivity”).

Big Data is defined as data sets that are so large that they defy conventional applications, frameworks and methods for analyzing them. The proliferation of Big Data is attributable to the amount of data companies across all industries are capturing on transactions with suppliers, customers, distribution channels and services organizations over years of activity. Big Data, by its very nature of spanning a multitude of databases and conventional data storage platforms within organizations, becomes difficult to capture, store, search and complete analytics on. For the manager in an organization who has these conventional methods of data search, analytics and visualization available to them, Big Data can quickly become overwhelming given the limited scope of tools available and the sheer amount of data available (Jacobs, 2009). For the manager attempting to gain greater insights into their organization's processes, strategies and overall performance, Big Data can quickly becoming overwhelming. The intent of this analysis is to provide guidance to managers on how they can better manage Big Data to provide the maximum analytical insight and intelligence about their organizations.

In today’s world, data is being amassed at an unprecedented scale. Large amounts of data generated by and about people and their interactions are being collected, analyzed, and stored for future use. Organizations are able to gain access to a variety of data sources including call logs, text messages, emails, client chats, social media pictures, videos, and posts, RFID, Geographic Information Systems (GIS), and much more. The reception of Big Data is described by boyd and Crawford (2012) as being “seen as a powerful tool to address various societal ills, offering the potential of new insights into areas as diverse as cancer research, terrorism, and climate change” as well as being “seen as

Additionally, social networking website Facebook, stores approximately 40 billion photos in total. (“Data, data everywhere”, 2010) Besides enormous data that generated from daily operational company transactions and social networks, the price drop of the data storage is also a strong factor triggering the fever of “Big Data”. For example, Google Drive - a cloud based data storage service – had a price drop of approximately 80% from March 2014. This price drop is considered a marketing approach to attract more computer users to adopt Google’s cloud service, which provides a more convenient and efficient way to access and store daily-used files. Although emerge of enormous data provides us opportunities to conduct further investigation and benchmarking, valuable information are not fully extracted and the potential power of using “Big Data” is undermined. In order to achieve thoroughly extraction of useful information from databases, many professionals in the academic field devoted into the study of data analysis and identified two of the most important drawbacks of traditional data analysis, which lacks of predictability and is less flexible in scalability.

The aim is to review the current ways of storing and obtaining data and compare them and determine the methodology used. Look into future methodologies and new developments in the industry. It is also crucial to assess how big data is already used and implemented into certain organisations. How the organisations improve their own businesses with this data and how it could help their clients with similar interests.

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’ is a new innovation. Processing big data and converting it into useful information can result in many benefits to a company such as increase its profitability, improve their business practice. Companies do spend lots of resources to harness this new technology. “Google Spent $7.3 Billion on its Data Centers in 2013. (Miller, 2013)” Big Data is regularly being generated by our activities such as surfing internet, using social media, blogs etc. Every digital process leaves a trail of information about consumers and their behaviour. These consumer behaviour and purchasing patterns can be used to visualize information. The big companies use big data to improve their customer’s

Big Data has gained massive importance in IT and Business today. A report recently published state that use of big data by a retailer could increase its operating margin by more than 60 percent and it also states that US health care sector could make more than $300 billion profit with the use of big data. There are many other sectors that could profit largely by proper analysis and usage of big data.

Volume is often regarded as the primary attribute of big data. With that in mind, a large number of people define big data in terabytes—sometimes petabytes, but big data can also be quantified by counting records, transactions, tables, or files (Russom, 2011). Volume refers to the mass quantities of data that organizations are trying to harness to improve decision-making across the enterprise (Schroeck et al., 2012). The volumes of data have continued to increase at an unprecedented rate over the last couple of years. The sheer volume of data that is stored or available for storage today is exploding, it is expected that by the year 2020 40 zetabytes (ZB) of data will be stored (Zikopoulos et al. 2012) which

Additionally, the objective here is to inform the reader about the technical makeup of big data and breaking it down through data analysis, for the purpose of realizing that this can be advantageous not just for big enterprises

Data-mining and the concept of 'Big Data ' are ideas that have grown in popularity over the last three decades as technology has grown and expanded so rapidly. Iqbal Pittawala explains that “Big Data refers to a technology phenomenon that has arisen since the mid-1980s. As computers [improve], growing storage and processing capacities [provide] new and powerful ways to gain insight into the world by sifting through the infinite quantities of data available” (Pittalwala, 1). With the increase of the popularity of Big Data, we 've been able to discover trends in our society we had not noticed before. However, there is a danger in Big Data. While information is being collected on us, do we really know what is being done with it? Big Data can be used to discover valuable trends, but it can also become a violation of one 's personal privacy.