Big Data for Individuals, Companies, and Governments - Literature review Example

BIG DATA Name College Course Date Table of Contents 1. Introduction 3 2. Big Data for Individuals 3 3. Big Data for Companies 4 4. Big Data for Governments 6 5. Big Data and the Electricity, gas and water supply Industry 8 6. Preparation for Big Data 9 7. References 13 Big Data Introduction There is no clear explanation of what big data is. There have been attempts to explain it, however. Arbesman (2013) says that one myth about big data is that there is a clear definition. One attempt at defining big data describes it as “non sampled data, characterized by the creation of databases from electronic sources whose primary purpose is something other than statistical inference” (Horrigan, 2013). Big data can therefore be defined as data that cannot be processed by traditional data processing methods because the sets are too large. They, therefore, require new methods of data processing. Big Data for Individuals Big data is still a relatively new concept. It is still in its trial and error phase. This means that it is still not foolproof, and has such been met with many challenges, but with equally as many advantages. There are some advantages of big data for individuals. One of this is that individuals are able to keep their data safe. With big data, individuals will be able to tell when sensitive information on their network is not adequately protected; which will allow them to make the necessary changes to protect their privacy better. According to the Data Science Series (2012), when enabled, Big Data can allow you to flag any 16 digit numbers when emailed or stored. This allows you to check the digits, and if they include credit card information, you can protect them appropriately. One other advantage of big data for an individual would be that people can customize their websites (Data Science Series, 2012) to the look and feel that they desire. This would apply to people whose lifestyles or jobs require them to have websites that people visit for information or interaction, such as blogs. Big Data allows such people to alter their sites to what they know would be liked by their target audience. In some cases where an individual is in tune with the tastes of specific members of the audience, he or she is able to customize the website to every different person’s taste; thus keeping the old audience and attracting new traffic as well. Big data also has its disadvantages to individuals. First, as mentioned earlier, big data is still a new concept. Therefore, many people are still new to it, and as such, are not well versed enough to handle it. For this reason, they still have a lot to learn about big data. Big data also requires different methods of data processing from the traditional methods. Individuals who were previously used to the traditional methods may have to learn the new methods anew, and this may take a while. In the meantime, the individuals might be inconvenienced as they try to work with things they do not yet understand. This may result in data losses in transmission, analysis and processing. Second, big data is not multivariate. This would especially be a problem for students. Students in this generation use the internet for their studies way more than any other generation in the past. For this reason, they need the very best when it comes to the internet as an information source and a learning forum. First, the students would have to learn how to analyze such data (Wagaman, 2013). Second, big data sources are not multivariate. That is, they do not have many different variables such as points of view, products and situations. This means, therefore, that the students do not get the absolute best; that they only get sub-standard data, and this beats their goal of getting the best resources and information. Big Data for Companies Big Data can be used in various ways by companies as well. The use of Big Data has its advantages for the companies that use it. First, Big Data allows dialogue with the consumers (Data Science Series, 2012). The world today offers a whole wide variety of goods and services of different kinds in the market; and customers and clients have a whole range of choices and alternatives to any one company. It is therefore important for every company to establish a connection with its customer base so as to inspire loyalty to the brand or the company. One of the most effective ways to achieve this is by maintaining constant correspondence with the customers/ clients. Big Data allows the company to have real time conversations with the customers. This way, the company gets direct feedback and is able to treat the customers as they would like to be treated, and to handle their demands and complaints appropriately. Big Data also allows companies to run frequent risk analysis. We live in an age that is extremely dynamic; and what worked or was popular yesterday may not work as well or be as popular in two weeks. Big data, in itself, is a perfect example of how fast a new concept can pick up. Companies therefore have to keep up with the market trends to avoid becoming irrelevant. Big Data allows companies to browse magazines and social media to establish the current trends constantly. It also allows them to predict the coming trends by some margin. This way, companies can tweak their goods and services appropriately to match what is popular; or, after predicting, to anticipate the needs of the customers in the coming days and prepare adequately. Unfortunately, Big Data also presents some challenges for companies. One such challenge is that Big Data analysis takes certain new skills, as it is a new field. Therefore, companies will need employees who have these skills. They may have to hire new people, or train their staff. The courses the staff may need to take include programming for big data, statistics for big data, machine learning and cloud computing; time series data and big data analytics, as well as professional and research skills (Wilkinson, 2014). Whether the company has to train its employees or hire new ones in order to make this transition, it will be an inconvenience for them. Making the transition to Big Data would also require the company to restructure the entire company. The advantages of Big Data also come with disadvantages attached. As earlier mentioned, big data improves companies in a way that allows more traffic to their sites, and increases their customer bases effectively. An increase in the company’s customer base will require the company to make some adjustments (van Rijmenam, 2015). For instance, if a company was used to only a few orders a week, the increase in the number of orders would require them to either hire more employees, or review the job descriptions of the present employees. Therefore, inasmuch as the increase would be good for business, only good restructuring would help a company thrive under the new conditions. Big Data for Governments Big Data, when introduced to governments, also has advantages and disadvantaged, like for individuals and companies. First, big data is advantageous in that it enables the government to make its security protocols foolproof. Big data works in such a way that the government is able to detect fraud as soon as it happens (van Rijmenam, 2015). Governments are extremely complex administrative structures; and holes in the security system could have grave consequences for the government and the country in general. It is therefore beneficial to be able to detect fraud instantly, so that measures can be put in place to handle the damage caused, if any. Such fraud could include a mole in the government; and such could damage an administration completely. Second, big data is a very secure mode of data analysis once it is fully understood and its concepts and usage grasped. As with the companies, governments can use big data to detect loopholes in their structures. They could identify mistakes in their systems. They can also anticipate problems with their data systems, and be prepared for any issues that may arise. As mentioned earlier, big data also allows securing sensitive information, and this is important because governments have a lot of information that should only be accessed by authorized individuals. This would reduce the chances of treason happening. There are, definitely, disadvantages of using big data in administration. First, not all the countries in the world have reliable internet access in all their parts. This may present a challenge to administration in such countries. In such countries, it is probably not uncommon to come across leaders that are not well versed with the technology of the internet. Therefore, the first problem would be that not all the members of the government are able to use big data. Another issue would be how to reach the government officials in the areas without internet access. In an exercise held in South Africa, Kitner and de Wet (2015) stated that most participants did not have an email address, and nobody knew how to use email on a phone. When this applies to administration officials, it presents a communication breakdown. Another disadvantage would be the skill set that using big data requires. It requires a whole different set of infrastructure from what was previously used. Governments, therefore, have to find professionals to set up the needed infrastructure and train the affected personnel. A data program would need to be set up to analyze the data and make it available to the people who need to use it (Kitner and de Wet, 2015). Since big data is still a relatively new concept, there are not that many skilled personnel. The few skilled people may be extremely busy and on high demand, which means that getting to them may be difficult. This will be a problem and may take governments a while to set up, then get used to the new mode of working. Big Data and the Electricity, gas and water supply Industry The company that hired Mike Smith is part of the Electricity, gas and water supply industry, as dictated by the Australian and New Zealand Standard Industrial Classification (ANZSIC) (2006), because its prime focus is the production and distribution of electricity. Companies in this sector are also affected by the transition to Big Data. Companies in this industry, should they embrace big data, would have to restructure their modes of operation. This may take a while for them to establish completely. Unfortunately, in the time it takes to set up the new infrastructure, business may be interrupted. They may have to pull down the entire old infrastructure in order to replace it; or to modify it instead. Either way, the customers and staff may not have access to their websites for sometime while it is worked on. This will inevitably incur some losses throughout that duration. This, however, would be a necessary evil, as once the site is back up and operating with big data analysis methods, business will be higher than before. Companies in this industry also need to keep up with market trends. They need to know their competition and how to beat them and stay on top of the game. They also need to know of any changes in the industry as soon as they happen. This industry is also dynamic, especially since there is currently research into new methods of electricity production. Big data will enable the companies to browse the blogs and tabloids and stay updated on the developments in order to provide their customers with the best available service at any one time. The transition to big data would also mean, for companies in the Electricity, gas and water supply industry, that the companies increase the amount of data they handle daily. This includes communication with the customers. It would also improve the levels of security in the company. It would also secure the market presence of the companies since they will be able to anticipate market trends and be prepared well in advance. Preparation for Big Data Companies that have not embraced big data yet will probably have to do so in the near future, since it has taken over the field of Information Technology (IT) (Mayer-Schönberger and Cukier, 2013). It is, therefore, very vital that those companies that have not made the transition prepare adequately for what is coming (Boyd & Crawford, 2012) Source: http://www.scidev.net/global/data/feature/big-data-for-development-facts-and-figures.html First, companies need to start sending their staff to the relevant training courses. The transition, at this point, is inevitable. As they wait for the change to catch up with them, they could begin preparing by training their personnel. This way, by the time the company makes the transition to big data, the employees are qualified to handle the new infrastructure. This is better than waiting till the change is made so they can train their staff. Second, companies need to begin preparing for the influx of customers they anticipate to get once they make the transition. They could do this by hiring more members of staff (and training them too). Alternatively, they could make sure they only hire people with knowledge and skills in big data analysis, so that they do not have to waste time and resources training the new ones as well. They could also prepare their staff psychologically for the influx. Their training could include bumping up their efficiency, such that they can handle multiple customers in the shortest time possible, without downplaying the quality of service. This also gives them a chance to increase their product production in preparation. Companies may also need to review their current data analysis systems in preparation. They may have to find reliable ways to store their data. They should also find effective methods of backing up their data. As mentioned earlier, this transition is expected to present a challenge in adjustment. Therefore, companies need to allow themselves a long enough period of adjustment. As the companies adjust to the new systems, a lot of data is expected to be accidentally erased. Some data could also be lost in transmission. If their data is adequately backed up in advance, these mistakes will not be deadly to them. It would be some sort of insurance policy for the company. Companies could also invent enough time and resources in finding the IT experts that will set up the new infrastructure for big data. As said earlier, these personnel will be on high demand in the coming days. It is, therefore, important, that when the time comes that a company is ready to make this transition; they will not have to start looking for professionals to handle the set up. Similarly, companies should also prepare their customers for the transition. Customers are a very vital part of business in any company. The transition will include a period of work when the site will be down or under construction. Customers may try to access the company during this period and fail; then seek an alternative, causing the company to lose business. However, if the customers are informed in advance (and alternative protocol established for contacting the company), they will be in a position to understand the situation. This increases their chances of staying loyal to their favourite companies. References Arbesman, S. (2013). Five Myths about Big Data. Washington Post, 16(08). Australian and New Zealand Standard Industrial Classification (ANZSIC) (2006), Detailed Category Descriptions, http://www.dpc.sa.gov.au/sites/default/files/pubimages/documents/Office-of-the-Industry- Advocate/Australian%20and%20New%20Zealand%20Standard%20Industrial%20Classification%20-%20Detailed%20descriptions%20for%20IPP.pdf Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679. Data Science Series: Where Big Data Happens 2012. Ten Practical Big Data Benefits. Examples of what you can accomplish with big data. http://datascienceseries.com/stories/ten-practical-big-data-benefits Horrigan, M. W. (2013). Big Data: a perspective from the BLS. AMSTAT news: the membership magazine of the American Statistical Association, (427), 25-27. Kitner, K. R., & de Wet, T. (2015). Big city, big data. interactions, 22(4), 70-73, http://interactions.acm.org/archive/view/july-august-2015/big-city-big-data Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt. Van Rijmenam, M 2015, ‘The Advantages and Disadvantages Of Real-Time Big Data Analytics’, Datafloq: Connecting Data and People, https://datafloq.com/read/the-power-of-real-time-big-data/225 Wagaman, A. S. (2013). Meeting Student Needs for Multivariate Data Analysis: A Case Study in Teaching a Multivariate Data Analysis Course with No Pre-requisites. arXiv preprint arXiv:1310.7141. Wilkinson, D 2014, ‘Statistics for Big Data: Doctoral programme in cloud computing for big data’, Darren Wilkinson's research blog, 11 November 2014, https://darrenjw.wordpress.com/2014/11/22/statistics-for-big-data/ Read More