They all are eager to take advantages that these solutions may bring. This is true for retail sector as well. Big data analytics has the ability to provide actionable insights retail sector has been looking for long time. These insights can be utilized as a guide for internal decision-making in a wide variety of capacities. Big data analytics’ influence is both growing and expanding into various sectors.
1.1. DATA MINING Data mining refers to extracting or mining knowledge from large amounts of data. Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the forthcoming need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration. Data mining can be viewed as a result of the natural evolution of information technology.
On the other hand we can also see all the good technology can do. Big Data There are many different definitions for Big Data. SAS (n.d.) an analytical software company describes it as, “a popular term used to describe the exponential growth and availability of data, both structured and unstructured.” Many think Big Data just came into existence but it has been around for years. Banks, retail, advertisers have been using big data for marketing purposes. Tracking consumers’ habits in many different aspects of their life has allowed them to gear specific products in a specific manner.
Through Big Data, developers can now access and tap into varied sources, which can even come from far-flung areas. Something that developers and statisticians from before the Big Data revolution dare to only dream about. According to Jonathan King and Neil Richards, authors of “What’s up with Big Data Ethics?”, man’s ability to discover new patterns and knowledge from data is moving faster than what the current legal and ethical guidelines can manage. As technicalities in the realm of Big Data continue to expand, debates have already ensued as to how Big Data challenges pre-existing policies, as well as the public understanding. According to a study conducted by the Royal Statistical Society (UK), there are 2 primary reasons of questioning big data: · Trust in the findings · Truth in the methods The leading concern about the uses of
The Information technology is the process of planning, developing, implementing or managing computer or electronic based applications. Particularly, computer hardware and software applications which are helpful in storing, converting, protect and securely retrieve information. Since, it becomes unprecedented rate of development in technology during the last two decades. New technology inventions created a lot more opportunities to the IT companies efficient in solving complex problems and using collected information for future referral. On the contrast, current change models plays crucial role in implementing change in an organization.
Knowledge discovery also known as data mining is the processes involve penetration into tremendous amount of data with the support from computer and web technology for examining the data. Data mining is a process of discovering interesting knowledge by extracting or mining the data fromlarge amount of data and the process of finding correlations or patterns among dozens of fields in large relational databases [3, 4]. Privacy Preserving in Data Publishing (PPDP) is very important in data mining when publishing individual information on web . The improvements are toward producing more effective methods that preserve the privacy and also reduces information loss to the researchers. There are also researches related to improvements of the algorithm that avoids some attacks on data.
This policy has resulted in increasing operational costs which would apparently have a detrimental impact on profit and EPS. Investment in research and development (R&D) is extremely necessary to thrive in the technology industry. However, this too would be extremely costly. Although AT&T has a high number of patents compared to its rivals, not every patent could be turned into a commercially viable product. AT&T has lost some of its market share and customer base to cheap rivals such as Sprint and T-Mobile.
With the help of the literature it is clear that due to high uncertainty avoidance the level of stress in an individual increases, not only the stress but also the aggressive behaviour of an individual, Which has a negative effect on the creativity of an employee and employee can’t be able to think of innovatively. High uncertainty avoidance also reflects a tight culture, and because of the tight culture employee cannot perform task other than the defined norms. Another setback of the tight culture is that employee cannot think of ideas which are not fit in the norms. Based on the above discussion I proposed the following
Fearing technology is foolish and should not be taken seriously. Nowadays, scientific and technological discoveries happen too quickly, and it is often the case that people who do not belong to the science field can feel threatened by the results that Research produces. Biotechnology, cloning, and artificial intelligence are just a few terms that arouse fear in people’s mind and that divide the public opinion in two different positions: on one side, the one in favor of scientific research without any restriction, and, on the other side, the one that want to imposes severe controls to it in order to limit the possible and dangerous consequences. Despite the concern about Science can be understood, if we look closely to the history of human
Data Science vs Statistics Data science is one of the rapidly emerging trends in computing and is a vast multi-disciplinary area. Data science combines the application of subjects namely computer science, software engineering, mathematics and statistics, programming, economics, and business management. Data science is based on the collection, preparation, analysis, management, visualization and storage of large volumes of information. Data science in simple terms can be understood as having strong connections with databases including big data and computer science. A data scientist is an individual with adequate domain knowledge relevant to the question addressed.