Data Mining And The Concept Of Big 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

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

With the buzz around many big data applications, privacy concerns regarding their uses have also grown. With the personal data has been mined and published every day, the battle to reclaim the privacy starts vigorously. E-commerce websites harvests information about all the online searches of customers. Social Media exposes the likes and preferences of people, their photos and all their daily activities. Video surveillance monitors the movement of people. The data gets published from health care, censes and other government agencies. With such potential of harvesting, mining and publishing data, the risks of using big data is more than anything [29].

In 2001, the MIT Technology Review listed data mining as one of the top 10 technologies that will change the world.[i] So, what is data mining? For many people, the simple answer is that data mining is the collecting of people’s information when logged onto the Internet. But Webopedia emphasizes that data mining is not the collection of data itself, but the statistical interpretation of it – allowing people to obtain new information or find hidden patterns within that collected data.[ii] It is the combination of these, collection and analysis, which are cause for concern. People want to know: What information is being collected about me? Who has access to that

Now that we have figured out how to harvest the free and ubiquitous big data, the next huge challenge is to figure out how to analyze and display the information in a useful and meaningful way. The big question today is how you present big data in a way that human beings can quickly understand and make decision. Most big corporations and government entities are drowning in a pool of their own data, because they lack the corresponding manpower to understand the data and extract meaningful knowledge out of it (Bizer, Boncz, Brodie, & Erling, 2012).

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 is becoming more meaningful with the ever more powerful data technologies, which enable us to derive insights from the data and help us make decisions. Big Data also creates new courses and professional fields such as the data science and data scientist, which are aimed at analyzing the ever growing volume of data. Some might think this exaggerated because data analysis, after all, not a new invention. However, we might all agree that the progress of digitization associated with the generation of ever larger amounts of data have totally changed the ways we deal with data.

When you hear the word “big data” what it is that first comes to your mind, a straight forward answer would be huge amount of data ranging between tens or hundreds of peta bytes to few zeta bytes of data. In a way, Big Data is not just about the amount or volume of data but one way it is about deriving business value from a range of new and emerging data sources, including social media data, location data generated by smart phones and other roaming devices, public information available online and data from sensors embedded in cars, buildings and other objects — and much more besides. Another way to define big data would be a 4V model wherein Vs stand for Volume, Velocity, Variety and Veracity. The next question that pops in would be what led to this huge pile of data, well certainly days or months or years is

Big Data is a term for very large amounts of formal and informal information that can be analyzed to find trends and patterns. The information can be about anything, but it needs to be processed in a way that will give it value and relevance. It can come in multiple formats and from different sources such as large databases, electronic records, social media, mobile phones, apps, wearable devices such as pedometers, and others. Different data sets are combined and contrasted in different ways to give perspectives and insights about a topic. It can be used in a seemly endless number of ways and people are discovering new ways to use it all the time, some of them entertaining. The largest areas of use include those relating to consumer behavior and choices, business procedures, healthcare, science and research, and law enforcement. People are also discovering its use in their personal lives as well for things like buying a home, dating, fitness routines and travel.

Big Data refers to the large amounts of data that businesses deal with on a day-to-day basis. Big Data can be defined in many ways, but this is one of the most general definitions. One of the primary uses businesses have for Big Data is to provide assistance for decision making. In order for Big Data to be effective, organizations must utilize the data to their advantage. The quality of information is what matters, not the quantity. Organizations may have virtually unlimited amounts of data to work with, but they must locate the helpful portions of data in order for it to be effective and serve its purpose. In addition, some data may not be helpful at the present moment in time, but will be needed for analysis in the future (“What Is Big Data?”,

A very simplified way of looking at big data refers to, “the sheer mass of data produced daily by and within global computer networks at a pace that far exceeds the capacity of current databases and software programs to organize and process” (Dewey, "Big Data"). The world of big data has evolved primarily from the business intelligence and analytics field of information technology. Big data and big data analytics involve big data sets. The information that is stored requires unique ways of holding and organizing the data in order to process it correctly. Common methods of data storage are just not possible. The internet has added to the amount of data that can be captured. With the ability of advertisers to utilize technologies to capture user information through web interfaces, the sheer magnitude of information that can be kept is staggering. All of this information can be put to use by any number of businesses and governments (Chen, Chiang, and Storey, "Business Intelligence and Analytics: From Big Data to Big Impact."). The ability to direct or channel all of that information opens unbelievable doors to virtually any organization. All kinds of organizations would benefit including businesses, governments, schools and hospitals (Dewey, "Big Data"). “Big data may be as important to business –

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. More accurate analyses may lead to more confident decision making. And better decisions can mean greater operational efficiencies.

A more layman definition of big data is found in the Oxford English Dictionary, which states that, “Big data is data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges.”