The Implications of Big Data - Essay Example

The Implications of Big Data Course Instructor Institution Date Introduction In the beginning of this millennium, terms like big data were beginning to be mentioned among individuals working in the information technology sector. The explosion of data is relatively new and as at the year 2000, only a quarter of world’s information was digitally stored. The rise of the amount of data in this millennium has swiftly been overturned. Currently, only less than two percent of all stored information is non-digital (Cukier and Schoenberger, 2013, p.1). Big data refers to the massive datasets that come from several sources of digital devices used in both the private and public sectors. Recent advancements in technologies have assisted in overcoming the previous limitations of storing, analyzing, managing, and capturing big data. In the modern world, organizations are collecting a wide range of data at a higher speed than before leading to a significant growth in data volume. Organizations in both the private and public sectors are capturing trillion of bytes of information about their suppliers, clients, and operations through digital systems and by any definition, this is big data. It is clear that over the years the world has accumulated huge amounts of data, and through computing power, this data can be analyzed, retrieved, stored at an affordable price. Big data comes with a wide range of benefits and limitations and the chance for greater innovations within an organization. Literature review The rise of big data and the recent technological developments represents a significant turning point that provided a clear opportunity to break from historical practices and transform data analytics. Today new technologies have developed in the market on how to use big data and utilize Big Analytics. Limitations and benefits of big data for Individuals Big data allows individuals to go past linear approximation models towards huge and complex models of greater intricacy. It is true to argue that bigger does not necessarily imply better, but small datasets often limit an individual from making accurate assessments and predictions. In the past, limitations in data and technology restricted people to certain statistical techniques especially in the form of linear approximation models. According to Norman H. Nie (2011), “Big data allows an individual to beyond linear approximation (Nie, 2011, p.3). Linear approximations models make causal predictions and inferences between two separate variables especially in the field of financial analysis and business environments. Traders and investors with access to big data can construct more complex models that are accurate and precise. With this information, an investor can make accurate decisions based on the trend and long-term movement of stock or GDP prices over an extended period. Nie continues to assert that, the availability of big data and the development of tools like Revolution R, individuals can analyze data allowing for greater precision and accuracy (Nie, 2011, p.3). The second benefit of big data to an individual is the challenge of statistical significance linked to big data. It allows one to move past interference and statistical significance and move towards correct and meaningful analyzes. According to Nie, “With first generation tools data analysis was based on experimental and inference. Statisticians and scientists sought to maximize statistical significance in their models” (Nie, 2011, p. 7). The idea of statistical significance developed from the experimental design with unsystematic assignment of fewer number of observations. This procedures involved several limits to analytical tools, hardware constraints, and difficulties in obtaining the entire universe of cases. By contrast, the main benefit of using big data is that it uses huge datasets that represent the population of interest. In this case, there is little need to focus on statistical significance allowing an individual to move towards making meaningful and accurate analyzes. Despite the advantages linked to the benefits of big data for an individual, big data also offers some challenges at an individual level. Big data requires special computer power to provide accurate and real-time analysis. This hardware tools are expensive to purchase and maintain, and one might be limited to this aspect. Secondly, in the field of marketing, user data can be fundamentally biased. According to Kokhi Yamaguhi, “The data that marketers access is only of individuals who have visited one’s owned digital properties or the data of people who have viewed one’s online advertisement, which is typically not an accurate representative of the total target consumer base” (Yamaguhi, 2015, p.1). This inaccuracy means that there is danger in assuming that the insights from this big data apply to one’s consumer base at large. Reflection The use of big data within organizations can significantly lead to several benefits. For instance, big data creates new categories of companies, and new opportunities to grow. Organization can use big data to improve their supply chain. Organizations that deal with fast moving consumer goods can utilize big data analytics to improve demand forecasting and supply chain planning. According to Bodgan Nedelcu, organizations in the manufacturing industry can improve on demand and supply planning by including the data from retailers, such as launch data, promotion data, and inventory data in their market analysis (Nedelcu, 3013, p. 16). By analyzing this data, manufacturers would be able to maintain a smooth order supply, deliver a higher level of service and use cash for more effective purposes Secondly, by using big data, organizations can be able to improve on sales and marketing and make solid development decisions. Nedelcu continues to assert that, with access to big data, organizations can obtain real-time input on emerging defects and make the necessary adjustments in the production process. Some limitations of using big data within organizations include the issue of leadership. According to Jean Yan, leadership on big data creates some challenges for some organizations. Taking the Initiative on big data has been a challenge to some leaders within major organizations (Yan, 2013, p. 13). A large number of leaders of organizations reported that they were learning about big data, and it would take some time to take full advantage of the initiative (Yan, 2013, p.13). The second limitation of big data is budget. Most organizations use traditional servers that are not designed to process big data. According to Yan, big data analytics requires high-performance computers and applications that require new information technology investments (Yan, 2013, p. 15). The government can use big data analytics to provide several benefits to the citizens. The use of big data in the government is unique as compared to the uses at an individual and organizational level. The government uses big data technologies in institutions like the National Institute of Health to facilitate a wide use of biomedical data, enhance training for biomedical data, develop and disseminate analysis software and methods for biomedical big data as well as establishing centers of excellence for biomedical data. This government organization utilizes this data to research and track the movement of diseases from one region to another among other research activities. Secondly, the government uses big data analytics to identify wrongdoers before they commit crimes. According to Kenneth Cukier and Viktor Mayer-Schoenbeger, “In 2007 the government through the Department of Homeland Security launched a research project aimed at identifying potential terrorist by analyzing data about the individuals body language, vital signs and other physiological patterns” (Cukier and Schoenberger, 2013, p.1). Other cities in the US have also adopted to a similar software that analyzes big data on previous crimes to identify when and where the next crimes might be committed. Despite the advantages linked to the utilization of big data within the government, government users of big data face several challenges and limitations. One of the most problematic limitation of use big data within the government is the issue of cyber-security. This involves the processes, technologies, and practices designed to protect programs, computers, networks and data from unauthorized access, damage, and attack. Recent developments in the data management technologies and diversity of data analytics have always created a problem in the management of big data. Since the government receives large amounts of real-time data from different sources, the risks of system invasions increase every day. In addition to cyber security, the government faces other problems related to big data use such as federal policies on privacy and cyber security that prevent the government from benefiting from big data. According to Paul Ohm, some big data project within the government can lead to bad outcomes like an invasion of privacy and discrimination (Ohm, 2013, p. 340). Since the government has the access to one’s personal information, some individuals within the government can use this data to prey on individuals. Additionally, data protection policies, security guidelines, and information sharing standards vary widely as the government's missions do. Many government agencies react to the big data wave with outdated policies which impends their growth and capability to cater for the needs of the big data revolution. According to Yan, this eventually reduces their financial gains and insights since the most organizations do not take proactive positions in adjusting their policies (Yan, 2013, 17). The second limitation linked to the use of big data within the government is the over-reliance on data. Yan argues that over dependence on big data within the government can be risky because something important may be overlooked, and wrong judgments may be made. Data alone should not be used to make decisions and solve problems, especially within government agencies. Reflection On The Impacts That Big Data Has/Might Have For Companies That Belong To The Retail Trade. The retail trade industry plays a significant role in the global economy as well as a bellwether of consumer prosperity and confidence. Secondly, retail trade acts as an intermediary industry linking consumers with other economy sectors. It also connects the producers and manufacturers with the customers and influences demand and supply of products within these sectors. More like other sectors of an economy, the retail sector is facing several changes that are altering the way the sector must prioritize the resources in response to risk. In the modern world, the risk profiles of retail trade industry evolve quickly. As a result, retail trade is incorporating ICT to shape the industry according to the demands of the millennium. According to Dr. Mulligan and Dr. Gurguc, “The big data is becoming a key determinant of business strategy in the retail trade and it assist in creating smarter shopping experience that allows retailers become more efficient and responsive to consumers” (Mulligan and Gurguc, 2014 p.21). Big data allows the companies within this sector to streamline and improve the already existing business processes by using analytics to locate useful insights and patterns. The authors continue to assert that with the use of big data within the retail industry, retailers can achieve four forms of efficiency. First, with the retailers can gather customer knowledge. Customer knowledge assists in designing the shops and web pages in ways that are appealing to the customers. Big data technologies have assisted international retailer companies such as Walmart to understand their customers. For instance, Walmart has particularly targeted mobile technologies to track consumers and market products. According to Center for Media Justice (2013), data brokers assist in fueling advertisements on the Internet by assembling the profiles of consumers and tracking their actions. This information is used by Walmart to gather consumer information and use this information to shape the company’s marketing strategies (Center for Media Justice, 2013, p.4). The second efficiency of big data on the retail industry is targeted advertising. This allows the retailers to place the correct advertisements in the right locations to create real-time personalized offers that increase demand. Through big data, retailers can target the right customers and configure the right emails to send to specific clients. Thirdly, big data has assisted the players in the retail industry to optimize the prices for their products and services. According to Dr. Mulligan, retailers can optimize prices by using the customer demand, shareholder value, and competitor activity by synchronizing prices with competitor and inventory data (Mulligan and Gurguc, 2014 p.21). Through big data, the retailers can personalize the shopping experience for their customers. Retailers prepare real-time in-store posters by that attract the customers into their store and online platforms. Recommendations on how companies should prepare for big data There is a lot of hype about the benefits of using big data in the modern world. However, before investing in this technology organization, need to consider several factors and recommendations. I would recommend organization first to consider the big questions about how it would save more money and increase revenues not the big data. According to Foley and Lardner LLP, the first step for any organization considering investing in big data is to identify the vital questions they want to answer using the big data. Company executives need to identify questions like- how the organization will save money and increase revenues? Although big data can be used to handle such questions and drive much broader range of decision making across the organizations, business owners need to consider the big questions. It is recommended a company beginning a big data initiative to consider a pilot question and construct a model that can be advanced and personalized over time (Foley and Lardner LLP, 2014, p. 1). The second recommendation would be considering whether the organization is ready to manage the cost and load of big data. According to Ramon Chen, “Mobile traffic alone is expected to surpass 6 exabytes a month in 2015” (Chen, 2012, p.1). This implies that organizations looking into the big data initiative need to consider the expenses they would incur in investing in the technology and how the these expenses would push the company in terms of finance. The sheer speed and volume with which big data era has grown has become a business and IT challenge. This could be a costly and risky move for companies looking into the big data initiative. The third recommendation is the consideration whether the organization is ready to rely on big data driven decision. According to Foley and Lardner LLP, big data-driven decisions are one of the most challenging decisions and many organizations are not ready to act on the recommendations that conflict with the leadership perception. Before investing in the big data initiative, organizations need to analyze whether they can handle the pressure driven by data approach to making significant decisions within the organization. The company executives need to be ready to invest in big data analytics as well as making the changes needed to implement the alterations suggested by the big data analysis. Conclusion In conclusion, big data comes with a wide range of benefits and limitations and the chance for greater innovations within an organization. Access to a large set of data with powerful tools can lead to greater accuracy and precision in making decisions. With big data, individuals can discover new opportunities, and improve performance in forecasting. Businesses can make the correct decisions about the market and plan their production based on consumer data. The government, on the other hand, can enhance security and focus on biomedical research with the use of big data. Despite all these positive impacts big data has its limitations and any organization investing or looking into investing in big data, initiative needs strategic plan to implement the technology. Since big data will affect everyone in the society, there is need to collaborate and form partnerships to make big data successful. References Center for Media Justice, (2013). Consumers, Big Data, and Online Tracking in the Retail Industry: A Case Study of Walmart. 1st ed. [ebook] Center for Media Justice, pp.1-22. Available at: https://saveballston.files.wordpress.com/2014/08/walmart_privacy_.pdf [Accessed 5 Sep. 2015]. Chen, R. (2011). Companies Need To Prepare Themeslves For The Big Data Challenge. [online] Business Insider. Available at: http://www.businessinsider.com/companies-need-to- prepare-themeslves-for-the-big-data-challenge-2011-10 [Accessed 5 Sep. 2015]. Cukier, K. and Schoenberger, V. (2013). The Rise of Big Data. [online] Foreign Affairs. Available at: https://www.foreignaffairs.com/articles/2013-04-03/rise-big-data [Accessed 5 Sep. 2015]. Foley &Lardner LPP, (2014). Big Data: Is Your Company Prepared? | Emerging Company Exchange. [online] Emerging Company Exchange. Available at: http://www.emergingcompanyexchange.com/2014/01/09/big-data-is-your-company- prepared/ [Accessed 5 Sep. 2015]. Mulligan, D. and Gurguc, D. (2014). ICT & THE FUTURE OF RETAIL. 1st ed. [ebook] London: Ericsson, pp.1-39. Available at: http://www.ericsson.com/res/docs/2015/ict-and-the- future-of-retail.pdf [Accessed 5 Sep. 2015]. Nedelcu, B. (2013). About Big Data and its Challenges and Benefits in Manufacturing. Database Systems Journal, 4(3), 10-19. Nie, N. H. (2011). The rise of big data spurs a revolution in big analytics. Revolution Analytics Executive Briefing, 1-8. Ohm, P. (2012). Underwhelming Benefits of Big Data, The. U. Pa. L. Rev. PENNumbra, 161, 339. Yamaguchi, K. (2015). 7 Limitations Of Big Data In Marketing Analytics. [online] Marketing Land. Available at: http://marketingland.com/7-limitations-big-data-marketing-analytics- 117998 [Accessed 5 Sep. 2015]. Yan, J. (2013). Big Data, Bigger Opportunities. White Paper, April, 9. Read More