The Growth And Availability Of Data

They used facts and arguments from various sources, such as studies and authors. As they are introducing the topic, they use the ideas of Lev Manovich to justify their argument that the name “Big Data” can be misleading. Manovich observed that Big Data has been used to refer to data sets large enough to require supercomputers, yet large amounts of data can now be analyzed on much simpler computers. Boyd and Crawford contend that the value of the industry does not simply come from the large data sets, but the “patterns that can be derived by making connections between pieces of data…” By relating Manovich’s idea, their argument made more sense. As computers become more advanced, bigger data sets look much simpler. But the connections Big Data makes are still valuable, no matter how advanced computers

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

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.

What does it mean to say “big data”? Big Data is more than just massive amounts of data stored together. It is more than just data delivered or analyzed fast. Meta Group’s Doug Laney described it as data that has volume, velocity, and variety (2001). This is the 3 V’s of Big Data and is widely used to define it. Additions to this definition include other V’s, such as veracity and value (XXX). What is volume? Volume could be 7 billion people speaking at once. It can be the data created by millions of Americans uploading photos, buying shoes online, or searching for the definition of Big Data. It is the volume of data being created by researchers at unprecedented amounts to chart the stars, to map the human genome, or to trend

Today, the data consumption rate is tremendously expanding, the amount of data generated and stored is nearly imperceivable and highly growing. Big data that is nothing but a large volume of unstructured or structured data that runs in and out in to a business on daily basis. This big data is analyzed in order to achieve prominent business growth and improved business strategies [1]. Every year there is at least 40% increase in the amount of data growth on global level, leading to which companies have started adopting new data analytic techniques and tools and also have stepped ahead moving their data towards the cloud for their big data analytic requirements and for better analysis.[3][2] In big data analysis it is not the amount of data that is essential but how efficiently we handle, process and analyze it is the key factor. Big data analysis doesn’t revolve around how much data we occupy, it deals with how well you make use

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.

Big data is an emerging term which has been noticed since it influences our daily life gradually. The big data is a large concept that is vague because different people look at big data phenomenon from a different perspectives, it is not easy to make a precise definition (Moorthy, et. al., 2015). The definition of big data is a matter of debate, however, a typical reference is to the collection, management, and analysis of massive amounts of data (McNeely & Hahm, 2014).According to George et. al. (2014), big data include Internet clicks, mobile transactions, user-generated content, and social media and content from sensor networks or business transactions, such as sales queries and purchase transactions. These procedures are significant to

Understanding what big data means is really simple.” It is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it” (Big Data Analytics).Big data is being produced by everyone and every day that finding ways way to manage this data is becoming a challenge. It arrives from multiple sources or touch points such as websites, social media or apps on smart phones at a high velocity, volume and variety. “All kinds of technologies or approaches including mobile devices, remote sensing technologies, software logs, wireless sensor networks, social media etc. are used by organizations to collect big data. (issue, 2013)” Now that the meaning of ‘big data’ is clear, it’s important to know that this information is useless unless it’s processed properly with the right tools. To extract meaningful value from big data companies spends fortune; it requires optimal processing power, analytics capabilities and skills.

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.

You may have heard the term “Big Data” often these days and the importance of analytics attached to it. The availability of good data set and analytics of it provide organizations ability to understand consumer behavior and future prospects. This has now become an essential part of every business operation. Data itself is not a new thing to humans, written records have been in existence since 4th millennium BC. What has changed recently is that with digital inventions and significant growth in internet access, most people are on the electronic grid for longer periods and spending lot of time on emails, messaging, entertainment and social media applications. This huge growth in data generation has given birth to the concept of

Big Data is changing the way business decisions are made. It is no longer a passing fad and if companies are not extracting insights from the data collected, they will be left behind by their competition.

Big Data: As the name proposes that it is "Enormous". Every one of us are acquainted with GB (Giga Bytes) and TB (Tera Bytes). Both of these terms are utilized regularly, regarding memory sizes. Not many know about Peta Bytes (PB) and Exa Bytes (EB). 1 EB is equal to 10^6 TBs (1 million TBs / 1 Billion of GBs). The measure of Big Data begins at the point, where a typical man 's reasoning stops. Every day, nearly 3 EBs of data is continuously produced and one of the main Market Research firm has affirmed that 1200 EB of information was created in 2012 alone. This information needs to be tamed from the viewpoint of Economics and need to be

Big Data embraces structured, semi-structured and unstructured information. It can be demographic or psychographic information about the customers, their opinions, product reviews etc gathered from variety of sources such as tweets, blogs, other social media content, technical devices like sensors and stream of data from mobile devices. Businesses have started using Big Data to get right information to identify right markets and right customers at the right time in order to make right strategic decisions. The Big Data market is estimated to grow to $32.1 bn by 2015 and $54.1 bn by 2017. According to the report “Be Careful or Big Data Could Bury Your Bank”, the world creates 2.5 quintillion bytes of data daily and last 2 years have contributed to almost 90% of the data which exists today. It empowers institutions to learn more, create more and do more using the data available with them.

The term Big Data is to a large extent vague and amorphous. Information technology professionals look at Big Data as large data sets that require supercomputers to collate, process and analyse to draw meaningful conclusions. A phenomenon defined by the rapid acceleration in the expanding volume of high velocity, complex, and diverse types of data. The new character added in this definition is