A Research Proposal On Big Data

The analysis of big data is the process of organizing, collection, analyze and examining the large volume of data to find patterns, market trends and useful information. This analysis helps organizations to better understanding about the information within data, and helps analyst to make better

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

There is sudden increase in the way data is currently being collected. People use smart phones and smart tablets and connected to internet throughout the day. Most of the shopping has gone online. As a result, data is getting collected in different formats and from different applications. The rapid expansion of Big Data is further fueled by exponential increase in usage of internet and people ability to interact on social media and different networking applications including YouTube, Gmail, Facebook to name a few. But just how easy is the task to leverage the vast amount of data, which is called Big Data, our computer systems are collecting on a daily basis? What significant challenges are currently faced by the organizations

The emergence of big data has provided different avenues for organizations to use data to improve different aspects of their respective operations. Be it customer service, research and development, or market position, Big Data has the potential to be a significant driving force in all these areas. However, there’s still a significant gap between the ability of Big Data to produce insightful analytical information based on real-time data and the ability of organizations to capture and utilize this readily available tool. This is, in part, due to the fact that the systems and processes necessary to fully maximize the usefulness of Big Data is currently lacking in most organizations. This lack of a conducive habitat for Big Data is further magnified in new organizations without any knowledge of Big Data. For organizations that have that have little to no knowledge of Big Data, there must be a thorough assessment of the benefits of big data and how they could improve the organizations overall place in the market. There also needs to be steps taken towards the design of frameworks that will enable the organization to better capture and utilize Big Data.

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”).

According to a report from The International Business Machines Corporation, known as IBM, 90% of the data in the world has been generated in the last two years. Frank J. Ohlhorst (2013) explains how the concept of collecting data for use in business is not new, but the scale of data that has been collected recently is so large that it has been termed Big Data (p. 1). Company executives who choose to ignore Big Data are denying their companies an advantage over their competitors. Big Data analysis is fundamental for all fields of work; it provides an insight to large amounts of data that will answer questions and make discoveries to improve efficiency in all areas of the world.

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 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.

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

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.

Abstract— Big data is a very important subject in modern times with the rapid advancement of new technologies for example smartphones, pc/laptops, game consoles, that all in some way gather information that is stored. Big companies are needing a place to not only store all the data that is coming in but to also analyze it for specific purposes and at the fastest speed manageable. There are many different providers out there who provide this service, this paper will talk about one way the company Google handles data using their own special made platform.

With 3.2 billion internet users [1] and 6.4 billion internet connected devices by 2016 [2], unprecedented amount of data is being generated and process daily and increasing every year. The advent of web 2.0 has fueled the growth and creation of new and more complex types of data which creates a natural demand to analyze new data sources in order to gain knowledge. This new data volume and complexity of the data is being called Big Data, famously characterised by Volume, Variety and Velocity; has created data management and processing challenges due to technological limitations, efficiency or cost to store and process in a timely fashion. The large volume and complex data is unable to be handled and/or processed by most current information systems in a timely manner and the traditional data mining and analytics methods developed for a centralized data systems may not be practical for big data.

With 3.2 billion internet users [1] and 6.4 billion internet connected devices by 2016 [2], unprecedented amount of data is being generated and process daily and increasing every year. The advent of web 2.0 has fueled the growth and creation of new and more complex types of data which creates a natural demand to analyze new data sources in order to gain knowledge. This new data volume and complexity of the data is being called Big Data, famously characterised by Volume, Variety and Velocity; has created data management and processing challenges due to technological limitations, efficiency or cost to store and process in a timely fashion. The large volume and complex data is unable to be handled and/or processed by most current information systems in a timely manner and the traditional data mining and analytics methods developed for a centralized data systems may not be practical for big data.

‘Big Data’ is the application of specialized techniques and technologies to process very large sets of data. These data sets are often so large and complex that it becomes difficult to process using on-hand database management tools. There are several techniques which are widely used in implementation of Big Data.