Prescriptive Analysis – Moving beyond Predictive Analysis 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. Approaches for data analysis After collecting the data, you will be faced with challenges to clean and analyze it with appropriate methods in order to derive meaningful conclusions from the data. For example, you will need to structure the individual data processing and analysis steps and to automate and eventually provide the results for implementation. Without the right data analysis methods, tools and other necessary resources, you will not be able to exploit the potential knowledge in the data. Data analysis in the enterprise can be roughly divided into three types: • Descriptive analysis – what has happened? • Predictive analysis – what could happen? • Prescriptive analysis – what should we do? These three types of analysis have one common interest - to enable a better understanding of the events, a business, to make the best
As defined by the magazine, CQ Researcher, big data is the collection and analysis of enormous amounts of information by supercomputers (CQ Researcher, 1). This collection and analysis has led to many great feats in the fields physics, medicine, social
Every day, we produce 2.5 quintillion bytes of data. 90% of all data in the world was produced in the past two years. Data has been around forever; we have always gathered information. Paleolithic cavemen recorded their activities by carving them in stone or notching them in sticks. Egyptians used hieroglyphics to record significant events in history. The Library of Alexandria was home to half-a-million scrolls of the ancient world. Less than hundred years ago, we used punch cards to record and store information. As technology continues to evolve, the amount of data we store continues to grow. We’ve come a long way since stone tablets, scrolls, and punch cards. It’s important to understand the concept of big data and the impact is has created. This paper will define the classifications of data, explain the challenges of big data, and describe how big data analytics is being used in today’s data driven world.
“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.” (SAS, 1)
Data exists everywhere around us. From your heart beats per minute, the current room temperature, or the number of tweets being posted about Miley Cyrus, nearly everything can be considered a data point. It is now being collected at an alarming rate and there are now opportunities to use that data in ways that were previously unimaginable. As technology advances data will only continue to grow, regardless of field of study, occupation or geography. As a user and firm believer of Data Science, I am excited by the possibilities of using that data and the trajectory of the field as a whole. After discovering the power of data I have developed a passion and appreciation for it, and realize that there is a lot left for me learn. Pursuing a graduate degree in Data Science is the stepping stone I need to take my career to the next level and compete with the best in the exciting, rapidly evolving and growing field.
Brown, B., Chiu, M., Manyika, J. (2011), Are you ready for the era of big data? Retrieved
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.
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
The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area
With big data seeming to boom so fast, it 's not surprising that problems in the processing of these enormous data sets were overlooked. With something so popular still in the experimental phase, there is a multitude of troubles that arise from the lack of rules or guides to limit how researchers manipulate the data in order to pull out the correlations that many big data scientists discover.
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.
In today 's mind boggling business environment, the field of data analytics is developing in acknowledgment and significance (Grant and Jordan, 2015). It is assuming a basic part as a basic leadership resource for officials, particularly those overseeing expansive organizations. Notwithstanding the development in significance of Planned/Analytical and its prospects for the future, other focal subjects emerged, incorporating the differed routes in which Planned/Analytical is organized and oversaw inside these ventures (Grant and Jordan, 2015). This flags the act of analytics, while advancing as a decision-making resource, stays in its initial advancement organizes and will proceed to develop and develop the length of it creates unmistakable budgetary advantages for the company.
Prescriptive analytics, on the other hand refers to the exploration of these business activities happening in the future. Predictive analytics is used in the exploration of business activities that are more likely to happen in the near future in order to find relationships in the data that couldn’t otherwise be found with the use of descriptive analytics. These different analytics mostly focus on larger data, but can also be used with smaller data as discussed in Chapter 17.
Data are raw materials that constitute an information system. When it comes to Big Data, the common perception of the ‘Big’ is in size, which can be elaborated as significant, complexity and challenge (Ward & Barker 2013). The magnitude is similarly addressed to volume, velocity and variety (Douglas 2001). Howie, one of the Microsoft engineers, succinctly discoursing Big Data as the expression progressively adopted to define the process of exercising serious computing power – the up-to-the-minute in artificial intelligence – to colossal and often highly intricate sets of information (Howie 2013). These diverse explanations present a perspective that Big Data appears as a more integrity and
The purpose of this paper is to give an insight of Big Data, its background and future opportunities.
Provide an approach for research efforts towards developing highly scalable and autonomic data management systems associated with programming models for processing Big Data. Aspects of such systems should address challenges related to data analysis algorithms, real-time processing and visualisation, context awareness, data management and performance and scalability, correlation and causality and to some extent, distributed storage [1]. Provide an approach for framework for evaluating big data initiatives [2]. Provide an approach for summarize opportunities and challenges with big data. Recent technological advances and novel applications, such as sensors, cyber-physical systems, smart mobile devices ,cloud systems, data analytics, and social networks, are making possible to capture, process, and share huge amounts of data – referred to as big data - and to extract useful knowledge, such as patterns, from this data and predict trends and events. Big data is making possible tasks that before were impossible, like preventing disease spreading and crime, personalizing healthcare, quickly identifying business opportunities, managing emergencies, protecting the homeland, and so on [3]. Provide an approach for sources of structured and unstructured big data. Unstructured data is everywhere. In fact, most individuals and organizations conduct their lives around unstructured data [4]. Successful decision-making will increasingly be driven by analytics-generated