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

Savvas (2015) stated that the reasons why Marks and Spencers uses big data because it helps them to gain a better understanding of the customers of multichannels. It also helps them to understand more about the shopping habits of their customers, which in turn helps them to understand about inventory turnover. The higher the inventory turnover, the better their sales are. Marks and Spencer also uses big data because it helps them to answer key questions about IT support, even if they do not have any IT backgrounds, and at the same time, it helps empower their

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

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 refers to the data sets whose size is bigger than traditional data base tools and contains the ability to acquire, store, manage and analyze data (Watson, H.J., & O. Marjanovic, 2013). Big data often has the following four characteristics, that is to say, it has a vast volume of data, fast transferring and dynamic system of data, a variety types of data and huge value of data. As the development of big data is faster and faster, the use of it also becomes broader and boarder, like researching on customers’ preference, taking it on military use and so on. This essay will mainly discuss the influences big data has on consumers.

Several explanations were given about the buzzword “Big Data”. Big Data describes a process in which “serious computing power” is applied to “seriously massive and often highly complex sets of information” (Microsoft Research, 2013). Big data is also defined as a cultural, technological, and scholarly phenomenon” based on three elements such as Technology, Analysis and Mythology (Boyd and Crawford, 2012). The first reason behind the quick expansion of Big Data is the extensive degree to which data are created, shared and utilized across the organizations and virtual networks formally and informally in the recent times. Digitization, that is, the transformation of analogue signals into digital ones, reached massive popularity in the early 1990s. The data-information-knowledge-wisdom hierarchy offers an alternative view, according to which information appears as data that are structured in a way to be useful and relevant to a specific purpose (Rowley,

Big Data is all around us, from creating our anniversary videos on Facebook and giving Netflix recommendations to utilizing all cellphones as weather stations to improve predictions (source 4) and combining it with machine learning for interesting insights. (source 5) All this data needs to be processed, creating a multitude of jobs in the data science field. Companies with products relying heavily in internet are investing heavily in Big Data, companies such as Uber, Spotify, Google and IBM to name a

Big Data has developed over the years new and more complex methods that allows them to see market evolution, their position on the market, the efficiency of offering their services. For being able to accomplish that, a huge volume of data is needed in order to be mined so that it can generate valuable insights.

Five years ago, few people had heard the phrase ‘Big Data.’ Today, it’s hard to go an hour without seeing it implemented practically in our daily life. The promise of a highly accurate data-driven decision-making tool is an attractive lure for any organization in any industry. However, big data is not without its own problems.

Big Data has taken the business world by storm. By 2020, it is expected that the amount of digital information in existence will have grown from 3.2 zettabytes in 2014 to 40 zettabytes. Companies are doing all they can to capture this digital information and turn it into actionable insights. Currently, the total amount of data being captured and stored by industry is doubling every 1.2 years. Therefore, companies must find increasingly efficient solutions to store and analyze this incredible amount of data.

The primary vision of Big Data is that organizations will make use full volume of each and every bit of data to make their best decisions. So, the Big Data not only the ability of collecting huge data but also takes the advantage of it for the business transformation.

The variety, volume and velocity (Lohr, 2012) of data have evolved in the present contemporary times with various companies and business embracing the benefits and advantages leveraged out of Big Data and its plethora of real life applications. This concept have taken a huge leap ahead in time and have find some innovative and creative ways to collect, store and analyze data which his enormous these days. The realm of e business and its applications in this industry are conquered by the means of big data.

Firstly, the main problem is deciding which data should be selected. The data, explaining customers’ desire and need, is important to be collected while most of the businesses are confusing what data they should concentrate on. A recent Gartner report (2014) stresses that 64% of firms raced to plan or launch a Big Data project, though they didn’t have enough professional knowledge yet. To understand what customers need through Big Data possibly turns into the core of companies’ target. The large data volumes and different varieties of data lead to complexity.