A Research On Big Data

I arrived on scene at about 0025 hours. I made contact with Deputy Williams and he advised the female who was reported to have left was in the residence and she had a severe injury to her face.

Many health care providers have recognized that big data analytics can provide chances for predicting and discovering tentative needs. It can also help in reducing risks, as well as in providing personalized services more appropriately. Ryu and Song (2014) said that many healthcare organizations and countries have done tried and made many successful cases of big data analysis to solve regular problems in healthcare, like reducing readmissions, rising the effectiveness and efficiency of healthcare, providing better quality of care, and predicting demands for future healthcare services.

The research proposal can be developed from the topic of big data to match up the demands of enormous data flow in the dynamic world. Data visualization tools at significant cost can help up in analytics of big data and can form an innovative research proposal for analysis and extensive research. The real time data and the use of techniques of big data in this domain can form an excellent topic of research calling for formulating methodologies and strategies to tap the untapped potential of this field and to experiment more in the field of research.

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

The application of big data is not limited to the aforementioned. Patient data is collected in volume that can encompass lengthy medical history for any given person. That data serves as a tremendous force in supporting hospitals, pharmaceutical companies, medical research labs and insurance companies. Reason being, the data could have connection to diseases, treatments, and even supply and equipment requirements or expenses. Analysis have shown that information about any genetic factors, the probability of genetics affecting patient health, validation of whether medications are legitimately improving patient health status after prognosis, or discovery that there are implications for an ethnic group can be discovered (Weber, 2016).

The rise of big data analytics has affected the 21st century American economy and businesses in many positive ways. One area where it is lagging, however, is the healthcare industry. For years, America has paid more for healthcare than any other country on Earth. This can be attributed to a number of reasons, but a large factor among these is the inefficiency of the current healthcare system and its failure to adapt to cost-saving analytics like other industries have. That is where big data analytics can step in and serve a great purpose. Big data is the process of taking mass amount of information across different, but interrelated areas in order to derive deeper meanings, insights, trends, and analysis through the usage of high-speed, high-capacity algorithms. This can be huge when one considers that as of 2014, there are 44 petabytes of information on patients in the electronic health records system. (Raghupathi) This can include medical history, imagery from patient scans, lab results, and a vast array of other information. Couple this information with the push to integrate individual’s social media posts, personal DNA sequencing, and vital data collected by smartphones and wearables, just to name a few, and it becomes evident that we as a species will be generating exuberant amounts of medical data. There are some people, however, who feel that having this information integrated into any kind of database poses a risk to the privacy of their most personal,

In a fast paced, business ordinated technological world the overall welfare of a company is tied to the success or failure to make the tough decisions. On one instance a company’s CEO might be able to make the choices based on experience, advice, or simple gut instinct. However, this is not the only skill one needs. There is a great deal of information to be found in being able to see investments in data and analytics. These decisions are based off of big data. Big data is a catch-phrase, used to describe a massive volume of both structured and unstructured data that is too large to process using traditional database and software techniques. The volume of data is in most cases is too big, moves too fast or it exceed the processing capacity the company has. Despite these potential drawbacks, big data contains the potential to help companies by improving operations and making faster, more intelligent decisions. This can be broken into three key parts, knowledge, data, and information.

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.

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.

Data is the backbone of business today and has always played a critical role in business. Today in the era of “Big Data” and Digital Business, data has become the primary driver of decision making, growth and innovation. The big data today is radically different from the data of yesterday. The Big Data age has brought with it a tremendous increase in the amount of data and types of data available to businesses. New data is produced every day, generated by social networking sites, mobile phones, location, third party, business transactions, etc. We are in an era which is characterized by the 5 V’s of Big Data: Volume, Velocity, Variety, Veracity and Value. The Big Data opportunities are enormous, as are its challenges. In this context it is especially important to understand the Opportunities and Problems that business faces to extract value from Big Data Analytics.

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 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.

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,

Through this paper, we will attempt to understand what constitutes ‘Big Data’. We will explore some of its sources and discuss some of the barriers faced by organizations looking to benefit from this phenomenon. We will also examine the various management tools and statistical techniques that can be used to extract information from big data.

As we have discussed in class, the big data revolution is hitting the healthcare sector hard. Hospitals, managed care organizations, and other providers are creating significant value in analyzing data from a multitude of sources with much of the information not previously thought of as healthcare data. What exactly does this mean? Well, healthcare companies are gathering data such as gym membership lapses, food shopping habits, clothing shopping, income, marital status, and even the number of cars (Bloomberg.com, 2014; Singer, 2014).