Big Data Is Not Without Its Own Problems

Since the 1970’s databases and report generators have been used to aid business decisions. In the 1990’s technology in this area improved. Now technology such as Hadoop has gone another step with the ability to store and process the data within the same system which sparked new buzz about “big data”. Big Data is roughly the collection of large amounts of data – sourced internally or externally - applied as a tool – stored, managed, and analyzed - for an organization to set or meet certain goals.

In Sutherland’s article, he suggested that criminality is a learned behavior and came up with the differential association theory to explain this. This theory basically says that who a person associates with is crucial in the development of deviant behavior. Through the groups the person is involved in, they learn how they feel about crime, motives to commit crimes, and how they could get away with crime if they choose to commit one. Sutherland also explained that crime isn’t directly related to poverty or anything associated with poverty, although the type of crime committed is usually based on social class. The upper class typically commits more crimes that deal with illegal use of money while the lower class typically commits more crimes

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.

Today, data is a growing asset that various businesses are having difficulty converting into a powerful strategic tool. Companies need help turning this data into valuable insight, which can diminish risk and enhance returns on investments. Companies are struggling to make sense and obtain value from their big data. Superior and reliable

Abstract—The important goal of this paper is to provide an overview on the concepts of big data analysis. Furthermore the growing emergence and importance of qualitative data analysis in the field of business intelligence and data science is broadly explained .It also marks out effective tools and techniques used to obtain prominent qualitative analytic results on the global level. Moreover we have concluded on the basis of comparison of the tools depending on various factors and parameters by representing it on a tabular manner.

The emergence of new technologies, applications and network systems makes it hard to run the current business models and huge data types, and thus emerged various types of analytic tools like Big Data, which make this work easier by way of proper organization of data. Big Data is all about analyzing different forms of data (Structured, Semi-structured and Un-structured) and it is not about the procedure, creation or consumption of data.

What is Big Data? Big Data is the mass collection of user data by mathematical algorithms, databases, data mining, and the use of datasets that were once believed to be static and unusable. Big Data’s history goes way back “…70 years to the first attempts to quantify the growth rate in the volume of data, or what has popularly been known as the “information explosion” (Press, Gil).” Researchers had predicted the massive growth of information and how our ability to collect and store it would need to continue to grow as well.

Even as organizations are embarking on big data initiatives, many still have several vision and

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.

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.

Big Data has seen exponential growth in recent years due to the large volumes of social-networking data that is now being produced. While it may be easier for large enterprises to adopt the use of Big Data it is unclear if many small and medium sized enterprises are firstly aware of Big Data and its uses and secondly whether it will actually be beneficial to the business. The project will look at the use, or lack thereof, of Big Data in SMEs and provide a comparison when looking at larger enterprises.

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.

In short, Big Data refers to various kinds of data sets whose volume, velocity and complexity make it hard and merely impossible to store and mange using current architectures and databases that exist in the market. These analytic tools are designed to enable users to rapidly analyze large amounts of data.

Innovations in computer processing, data storage, analytics software coupled with reduced costs have contributed to the emergence of "big data" as a mainstay in the world of Information Technology. It is no secret that businesses have been looking for ways to parse and effectively convert the vast amounts the data they 've been able to collect since technology has allowed them to do so. However, until recently, that has proven either too expensive or arduous of a process. Peter Sloot, in a call for papers referring to this very topic addresses what is known as the ‘Big Data ' problem,

Management of Big Data is useful with how one handles the information. Ways to use the stored information include, but not limited to, reduction of costs, time reductions, and making smart decisions based on data results. [1]