The Benefits and Limitations of Big Data on Individuals and Businesses - Term Paper Example

The Impacts of Big Data (Name) (University Affiliation) (Date) Executive summary Recent studies indicates that more than 60% percent of businesses have already employed big data projects while 29% are in the process of implement the same by 2016 (McAfee, 2012). Therefore, this report aims at delineating the significance of big data and its current and future state. Moreover, the paper will highlight the various benefits and limitations for big data for, individuals, companies and governments. In addition, the report will also give a reflection on the effects of big data in the manufacturing industry. Finally, the paper will provide three practical recommendations that companies can employ when engaging in bid data more so, when using outsourced information. Table of Contents Executive summary 3 Recent studies indicates that more than 60% percent of businesses have already employed big data projects while 29% are in the process of implement the same by 2016 (McAfee, 2012). Therefore, this report aims at delineating the significance of big data and its current and future state. Moreover, the paper will highlight the various benefits and limitations for big data for, individuals, companies and governments. In addition, the report will also give a reflection on the effects of big data in the manufacturing industry. Finally, the paper will provide three practical recommendations that companies can employ when engaging in bid data more so, when using outsourced information. 3 Table of Contents 4 1.0 Introduction In today’s global market, suppliers, customers and businesses are creating as well as consuming enormous amounts of data. As Gartner (2014) predicts, enterprise data in all forms could grow over 98% in five years to come. According to Gartner (2014), the world’s volume of data often triples every eighteen months. This kind of data, often referred to as “big data,” “information-overload” and “data deluge” comes along with big benefits and challenges both to the businesses and individuals. Key findings from recent studies, for instance, indicate that big data is associated with the economy since it can be used as a catalyst for innovation. It is a catalyst for innovation because novel business models need development to strategies that are often derived from the analyzing the various efficiencies found in big data across various sectors (McAfee, 2012). On the other hand, studies indicate that big data is often a distraction to employees even though executives always feel overwhelmed by data deluge. As such this report aims at highlighting the various benefits and demerits of “information overload” for an individual, companies as well as governments. Below are some of the drivers of big data for companies. Figure 1.0 Drivers for big data incorporation Source: Ca.com, (2015). Figure 2.0 Benefits of Big Data projects 40% 38% 41% 41% 44% 46% 49% 48% 49% 51% 44% 50% 14% 11% 10% 10% 8% 6% New Improved Increased Faster time-to- Increased quality More effective products/services competitive revenue market targeted that would positioning marketing and otherwise not be selling campaigns possible Source: Ca.com, (2015). 2.0 The Benefits and Limitations of Big Data for Individuals 2.1 Advantages Before the 21st century, one needed to be a data scientist collected, sift and analyze any kind of data. With today’s big data however, statistics indicate that a personal user is able to go through huge potions of data without complicated technical steps. Secondly, through the various sources of data that can be extracted from the internet and data bases in the workplace and libraries, an individual is able to learn various learn by themselves various aspects of their lives and their environment. For instance, an individual can quickly investigate the various ways of staying healthy, how to access various place through the Google maps. Further, the granularity associated with big data allows individuals to personalize services as service providers today customize services according to the customer’s needs (Manyika et al., 2011). An individual can always enter his personal data in the system and order customized products and services as he or she will already have prior information gathered from big data regarding the same. 2.2 Limitations The first disadvantage of big data is that the vastness nature associated with various types of information can mislead an individual more so, issues to do healthcare. For instance, an individual could be seeking a solution towards a medical problem he or she has and in the event lands on deceptive information thus impacting him or her negatively. Secondly, whereas it is important to enter personal data in various data sites in the web and company databases when applying for employment, the same information can be used by a malicious individual for personal gains thus negatively affecting the owner of the data. Accordingly, individuals are at a high risk of suffering from privacy violation as it is easy to hack databases and extract important information such as credit information. 3.0 Benefits and limitations of Big Data to Companies 3.1 Advantages One of the major benefits associated with big data is that businesses are able to detect defects in products, improve supply better thus boosting quality. As (Russom, 2011) indicates business executes often give credit to big data as it helps their companies to analyze customers’ responses to products, which in turn impacts the sales trends. Understanding the customers’ repeat behavior is often critical as it enables the company to deliver its products and services in profitable and timely manner. Moreover, through the big data the company can evaluate a wide range of information from various databases thus settling on good contracts. Thus, big data is important in products’ defects detection, improving the supply and boosting quality. Secondly, companies today also benefit from high capacity, speed as well as scalability provided by the cloud storage IT companies. Accordingly, companies can obtain information regarding various business statics related to trends in time and at limited costs. The storage of data in cloud storage companies has two merits. Firstly, it allows companies to do an evaluation of huge datasets without necessarily spending considerable costs in hardware installation in to keep the data internally. Secondly, outsourcing data is beneficial as a company would need special skills attached to the IT department, which is costly. Therefore, data hosting companies are essential as they abstract the complexity related to big data storage thus enabling companies to access big data technology from a distance. 3.2 Limitations The first challenge that companies often face in regard to big data usage is privacy issues. Companies often need customers’ and end users’ personal information in order to serve them appropriately. However, this information may at times leak and fall into the hands of malicious individuals with the intention of stealing money from their credit cards. Therefore, in such situations companies are often put in legal difficulties which often cost their credibility and reputation as well. The other problem related to big data is that companies would have to build capabilities required to manage data. Big data projects are costing firms more than 20% of their IT budget annually and this is expected to increase to 30% in three years to come (Ca.com, 2015). This means that big data projects are costly and cannot be ignored. The increasing amounts of data that comes from novel sources everyday along the supply chain needs a new level of computing power, deep analytical skills as well as storage capability. Therefore, big data has come along with a major challenge that will need new strategies in the coming years 4.0 Benefits and Limitations of big data for governments 4.1 Benefits More often than not, big data is used by the government to administer its citizen in various sectors of the economy (Unicomgov.com, 2015). Accordingly, through the existence of big data, governments through the health ministries or departments are in a position to improve as well as care for its patients and protect its citizens from predictable and prevented diseases. The various sources of data helps the government to create a platform for sharing healthcare programs thus optimizing the use of scarce health service assets through integrating and analyzing huge datasets. Accordingly, the government advances risk models and personalize health advice thus reducing wastage. For instance through Google Flu Trends, governments are in a position to identify influenza drifts based on the changes in the search action. Consequently, centers for disease control are often informed in advance thus responding promptly. The other advantage is that today under their national aeronautic and space departments or ministries, governments are able to reduce the size, cost and risk associated with the development of ground ad space-based information systems through the use of big data. The advanced information systems often use big data in increasing the utility and accessibility of science data (Russom, 2011). 4.2 Limitations According to (Lohr, 2012) violation of privacy is one of the major challenges that governments face with regard to big data as it holds vast personal data of almost all its citizens. For instance, whereas the medical data is usually significant for predicting future outcomes of peoples’ health, it is unethical and legally inappropriate for such information to be leaked to third-parties without the owner’s consent. On many occasions, governments are often forced to compensate its citizens, whose information has leaked as it violates their privacy and they are prone to discriminatory practices. A good example is when HIV AIDS patient’s data is leaked. The second challenge is the misuse and manipulation of big data by individuals privy to government’s databases. Previous reports have indicated that government officials often use their power to blackmail individuals who oppose them. For instance, they often use extensive tax information to threaten their perceived enemies. Moreover, the information gathered from the government surveillance cameras as Becker and Posner (2013) indicate can be unethically be used for political advantage. 5.0 Impacts of Big Data in the Manufacturing Industry Ideally, the manufacturing industry in Australia has been an intensive of data in more than three decades (Lee, et al., 2013). Data is in the manufacturing industry is typically used to drive efficiency and quality, build, adopt information technology as well as distribute product since the inception of the computer era. As (Lee, et al., 2013) indicate, during the early 90s manufacturing firms in Australia racked up remarkable annual productivity gains due to improvements in the quality of products manufactured and improved operational activities. Accordingly, the operational improvements in turn increased efficiency in the manufacturing processes. The manufacturing sector in Australia and globally has been a huge success because of big data. However, the augmenting global as well as fragmented manufacturing value chains has come along with various challenges, which manufactures need to address. Due to the presence of big data, the Austrian manufacturing industry is in a position to engage in the specialization of particular stages associated with the production process. To continue attaining increased levels of productivity growth, manufactures will have to leverage huge datasets in order to sustain efficiency across the protracted enterprise, as well as design and market high-quality products. The manufacturing sector keeps more data than all sectors of the Australian economy (Russom, 2011). According to Russom (2011) more than three exabytes of new data was accumulated in 2014. This is because the manufacturing sector sources data from a vast multitude of sources from the supply chain management systems, performance monitoring systems and process control systems (instrumented production machinery). Consequently, big data has several impacts in the manufacturing industry as it will be indicated in the subsequent sections. To start with, the big data has positively impacted research and development as well as product design. Notably, the use of big data give manufacturing companies further opportunities to hasten product development and help designers to incorporate significant features that come as a result of concrete customer inputs. Furthermore, designers are able to develop strategies that can minimize production costs through innovation. The manufacturers have the capability of capturing the considerable amount of data opportunity to develop more value by incorporating product life cycles, which are able to integrate datasets from various systems for consistent collaboration. Second, big data also helps in “design to value.” Whereas capturing customer input through market research has been the norm with regard to product process, many manufacturing companies are yet to systematically obtain significant insights from the burgeoning amounts of patrons’ data. The manufactures are yet to use customers’ data to refine the current designs as well as help to design specifications for new frameworks and variants. The other impact of big data on the manufacturing industry is associated with the supply chain. Apparently, the volatility related to demand has been a dire issue to manufactures as their customers are often pushing for increased responsiveness and flexibleness from the suppler due to the ever-changing and diverging consumers’ preferences. Thus, through big data, manufactures have an added advantage as they able to improve supply chain planning and demand forecasting as well. Moreover, manufactures can access more value from integrated data sources include information gathered from retailers such as launch, promotion and inventory data. Big data also often has a significant impact as far as marketing, sales and after-sales support is concerned. Apparently, manufacturing firms are using data collected from vast customer interactions to enhance their marketing and sales activities as well as to make informed product development decisions. Unlike in previous decades whereby, companies had to call homes in order to generate data related to the actual product and performance, today the manufacturers are using big data to obtain real-time information thus adjusting in the production process accordingly and promptly. 6.0 Recommendations In the preparation of big data, companies need to identify first the source, the produce and the content of various data it may need use in its operations. For instance, the company should come up with significant security measures related to information analysis and virus detection. This is because not all information that seem impressive from the outside is good for the company’s system as it might come with virus and malwares set to gather and steal information from the company and send it back to the originator. With the coming of electronic communications adversaries, companies are susceptible to information loss as well as customers’ data information. Thus, the company should prioritize antivirus software that is equally updated regularly. Secondly, the companies should often filter the outsourced information in order to determine the important and insignificant information. To attain this objective, the company should provide the staff with data management policies and tools that can help them to effectively filter data quickly for relevance. Further, the companies must identify what filters to employ, establish processes, and categorize data in order for the producers to be accountable for the information created. Last but not least, is to distribute and apply the data analysis. Various information are often intended for different locations, levels as well as business units therefore companies must incorporate a distribution framework that is intelligent and automated. The right data must be applied in the right case for perfect performance to be exhibited. An organization can even look for opportunities to monetize information in unique ways and enhance competitive differentiation. 7.0 Conclusion In summation, the business report has delineated significance of big data and its current as well as future state. Moreover, the paper has highlighted the various benefits and limitations for big data for, individuals, companies and governments. In addition, the report has also given a reflection on the effects of big data in the manufacturing industry. Finally, the paper gave three practical recommendations that companies can employ when engaging in bid data more so, when using outsourced information. References Becker, G. and Posner, R. (2013). The Internet, Surveillance Cameras, and Misuse of Big Data Becker. [online] The Becker-Posner Blog. Available at: http://www.becker-posner blog.com/2013/05/the-internet-surveillance-cameras-and-misyse-of-big-data-becker surveillance-cameras-tax-reporting-internet-based-data.html [Accessed 2 Sep. 2015]. Ca.com, (2015). The State of Big Data Infrastructure Management Benchmarking Global Big Data Users to Drive Future Performance - CA Technologies. [online] Available at: https://www.ca.com/us/register/forms/collateral/the-state-of-big-data-infrastructure.aspx [Accessed 2 Sep. 2015]. Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38-41. Lohr, S. (2012). The age of big data. New York Times, 11. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data. The management revolution. Harvard Bus Rev, 90(10), 61-67. Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. 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