2.1.1.1 The volume of data
Volume is often regarded as the primary attribute of big data. With that in mind, a large number of people define big data in terabytes—sometimes petabytes, but big data can also be quantified by counting records, transactions, tables, or files (Russom, 2011). Volume refers to the mass quantities of data that organizations are trying to harness to improve decision-making across the enterprise (Schroeck et al., 2012). The volumes of data have continued to increase at an unprecedented rate over the last couple of years. The sheer volume of data that is stored or available for storage today is exploding, it is expected that by the year 2020 40 zetabytes (ZB) of data will be stored (Zikopoulos et al. 2012) which according to Schaeffer & Olson (2013) is fifty times more than what is generated today. IBM (2012) has estimated that 2.5 quintrilllion bytes of data are created daily.
The volume of data being stored on a daily basis by businesses these days are immense with IBM (2012) estimating that most companies in the UK have about 100 Terabytes(100,000 gigabytes) of data stored. With this number steadily increasing, the estimation of 40 zetabytes by 2020 could well be surpassed. This gives rise to the introduction of the right technologies to help identify the most useful of the available data. This will ensure that enterprises are well equipped to have a better understanding of their business, customers and the market place they belong to.
2.1.1.2 The Variety of data.
Big data is inherently high variety; The Variety of data represents all types of data weather its unstructured, semi structured (which makes up about 80% of the worlds data) or structured in parts of the decision making and insight process. T...
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...of data and despite this uncertainty, the data still contains valuable information. (IBM, 2012). SAS (2012) indentifies the forth V as Variability which is the daily, seasonal and event-triggered peaks in data, an example of this will be a trending topic on twitter which is difficult if not impossible to manage. It is important to note for enterprises that high data quality is an important big data requirement and also a big challenge, but even top notch data cleansing methods and solutions cannot remove the innate unpredictability of some data (IBM,2012), as earlier mentioned - twitter trends, the economy, the weather or even the future buying decisions of a customer needs to be acknowledged and embraced ,IBM(2012) believe that embracing this uncertainty is a hallmark of big data and executives will need to learn to use these uncertainties to their advantage.
Chet Craig is the Central Plant Manager of the Norris Company. He started as an expediter in the company's eastern plant and was quickly promoted to Production Supervisor in three years. After two years, he was promoted to Assistant to the Manager of the Eastern Plant. Five years later, Chet was transferred to the central plant as an Assistant, and after one month, was promoted to his current position.
Big Data is characterized by four key components, volume, velocity, variety, and value. Furthermore, Big Data can come from an array sources such as Facebook, Twitter, call
The Human and Ethical Aspects of Big Data, quickly outlines an array of ethical problems that big data has had and will continue to develop in the upcoming years in our society. The author begins by giving an example of the earliest use of big data, the census. Census data can provide a plethora of benefits to society, representation in government, civil planning, such as road and water treatment, as these take years to construct and having the population and these necessities, Time align is crucial. As the author, uses the example of the internment of Japanese-Americans in 1943. Here is big-data supposed to help the citizens of the Unites States, the big data that is supposed to be private. Now in 1943, big data being used to discriminate
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Big Data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools. However, Big Data contains a lot of valuable information which if extracted successfully, it will help a lot for business, scientific research, to predict the upcoming epidemic and even determining traffic conditions in real time. Therefore, these data must be collected, organized, storage, search, sharing in a different way than usual. In this article, invite you and learn about Big Data, methods people use to exploit it and how it helps our life.
Currently the world has a wealth of data, stored all over the planet (the Internet and Web are prime examples), but it is needed to be understand that data. It has been stated that the amount of data doubles approximately
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.
...ch Reips. ““Big Data”: Big Gaps of Knowledge in the Field of Internet Science.” International Journal of Internet Science 7.1 (2012): n. pag. Web. 16 Mar. 2014.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
The technology was innovated, upgraded and extended. (Mapreduce, Google work queue, Google files systems, Ad Words (an auction-based advertising program that enables advertisers to deliver relevant ads targeted to search results or web content.), CPC, Google map, Google images, Google Apps, Google desktop search, Froogle, Google talk, Gmail, Google check out, Google video etc.), Google Audio Ads (an automated online media platform that schedules and places advertising into radio programs). Google Print Ads (a web-based marketplace for placing ads in print media), Google Video Ads (user-initiated click-to-play video ads that run on sites that are part of the Google Network).
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources necessary to properly analyze the data, and as a result, companies often made business d...
The last decade can be marked as a period of significant changes in the business world. Being accustomed to utilize computers as a powerful tool with its office applications such as Microsoft Word and Excel. In the 1990s office workers first faced the opportunity to share information using the Internet (McNurlin, 2009). However, the situation became even more different with the transition to the third millennium. With a further development of information technologies, the majority of big enterprises had to reconstitute their business processes and to make the transition to the Internet economy. Enterprise resource planning (ERP), supply-chain management (SCM), customer relationship management (CRM) software and the variety of other information systems became essential components of the new economy. It can be expected, that all these complex solutions were designed to bring great benefits for different sides of the corporate activity, in particular, decisions made by top-managers are expected to become nearer to the ideal, customer service is to be improved and collaboration more prolific. Nevertheless, to ensure the desired results it should be taken into account that the key concept of these reorganizations is an information or a data, dealing with which can be a serious issue, and wide utilizing of the data warehouses in contemporary organizations confirms this fact.
Adopting big data can also help the banking industry by saving them from lots of embarrassment resulting from increase in the number of customer which in turn requires banks to improve on their performance. As stated earlier banks are entrusted with lots of information and this information must be safe will be required to be accessed ready and in a timely fashion. The use a normal small database will not be enough to perform this operation and if banks don’t embrace the use of big data they might start to experience failure in there system.