Big Data - Benefits, Limitations and Effects - Literature review Example

Big Data: Benefits, Limitations and Effects Name Tutor Unit Code Date Introduction Just as the name suggests, ‘Big Data’ generally refers to large volumes of data. The volumes describe the number of transactions, files, or even time. It also extends to the variety of data (social media, streams, or logs) and the velocity (instantaneous speed) of the data. The volume, variety, and velocity and capture the 3 V’s of big data (Doug Laney, 2001). Armour, 2012) expanded the 3 V’s to 5 V’s by including the veracity (integrity) of the data and value (usefulness) of the data. He also introduces the involvedness (level of interconnection) of the data structures. Big data involves unique innovations and technologies used to capture, store, distribute, manage and analyse the data. Big data industry has grown exponentially. The Reuters (2010) indicated that this industry would grow from $3.2 billion in 2010 to over $25 billion by 2015. According to the MGI (2011), companies stored over 7 exabytes of data in disk form and individuals stored over 6 exabytes in their notebooks and personal computers. However, this is expected to grow by about 4300 per cent to over 35 billion zeta bytes by 2020 (Columbus, 2012). This begs for a basic question; what is the benefit of big data? In this report, I discuss the benefits of big data for people, enterprises and governments. I also discuss the various limitations that come along with the usage of big data on each party. Benefits and Limitations of Big Data for Individuals Individuals stand to gain a lot from using big data. The greatest benefit for individuals is based on using personal-based data. Today, virtually each one person owns or has access to a mobile device or data connection device. Today, individuals own over 7.4 billion mobile and connection devices of which over 88 per cent are smartphones. Moreover, the mobile traffic expanded from 1.5 exabytes per month in 2013 to 2.4 exabytes per month in 2015. Applications are being developed every day for personal use. For instance, individuals can use their smartphones to monitor their health. Healthcare professionals also use mobile applications to monitor their patients. The applications can be used in maintaining and accessing health records, administration, consulting and communication, as well as reference and information gathering. Patients can also use mobile devices to compare drug prices, physicians and treatment (McGuire et al., 2012). With the increasing traffic jams, people can use their mobile devices to get real-time traffic updates, through smart routing. McGuire et al. (2012) indicate that with the increasing number of smartphones, smart routing is expected to translate into fuel and time savings valued at over $500 billion by 2020. Moreover, people will have saved over 20 billion hours on the road translating to about 10 – 15 hours saved by every traveller in a year, representing around $150 billion in fuel savings. However, one great concern for individuals is their privacy. Individuals are fearing that their personal data may well be used inappropriately, especially when data is linked to many sources. For instance, architectures may require location-based data. This information may be collected by service providers, and if shared with other parties, like architectures, people’s privacy may well be breached. Malicious individuals, such as attackers, can hack into location-based servers and use the information stored there to determine people’s identity and follow them. It is technically challenging to manage privacy, which is also a sociological concern (Kayyali et al., 2013). Another related limitation is data security. Companies and governments find themselves in an awkward position in trying to manage how private data can be shared but still disclosure and sufficient utility are realised from the big data. Limited sharing of private data reduces the informational content to an extent that it may not be practically useful. This poses a great difficulty regarding the safety of information sharing. There are many online sites today, such as Facebook, Twitter, LinkedIn, dating sites, and many more that fundamentally require sharing of private data. It becomes challenging for individuals to know what to share, how this online companies can link the shared data and how individuals can finely control their sharing of private data (Kayyali et al., 2013). Benefits and Limitations of Big Data for Companies Companies find big data very useful in outperforming their competitors. The companies can use data-driven approaches to innovate, compete, and capture value. With the growing big data industry, companies can buy and analyse the data to help them design better products depending on the customer needs, tastes and preferences. For instance, companies can use sensors to monitor how their products are used. The data they gather will be used to improve the product designs and create new product/service offerings. MCI report (2011) indicates that a retailer taking up big data is disposed to increase its operating margin by over 60 per cent. More than that, big data creates new growth opportunities. Big data also helps to improve organisational operational efficiency. This is particularly crucial in the value chain and the supply chain. For instance, aircraft sensors can generate around 20 terabytes of data every hour. The data can be used by airline companies to alert the supply chain managers on when to order spare parts, and when to conduct maintenance. Insurance companies as well leverage on big data to analyse the speed of claims. This will help the companies to cut their operational costs and detect fraudulent claims that are predominant in the sector. The companies as well have the capacity to process claims automatically through the data collected. Another key development is the use of Radio Frequency Identification RFID technology in collecting and storing large volumes of data. The technology is particularly important in inventory management since it makes easy tracking and tracing products at every point through the supply chain in a more precise way and in real-time, thereby reducing processing time and labour. RFID also helps to improve product visibility, reduce stock errors, moderate out-of-stock stuffs, lower warehousing expenses, and reduce pilferage (Tajima, 2007). The companies will also be able to update their inventory and logistics records on a regular basis. Companies find it hard to deal with the large and increasing volumes and velocity of big data. Even though, processors have been developed to speedup up the processing of the data, big data is now growing exponentially and the systems are being overwhelmed. Computer resources and CPU speeds are not being developed at an equally faster rate. Since the volumes are increasing, it is also becoming a challenge analysing those volumes in good time. Some situations demand immediate data analysis. For instance, banking institutions may be faced by a situation where a fraudulent credit transaction is underway. Immediate action needs to be taken such that the transaction is flagged down before it is completed and therefore prevent losses for the company. However, if the transaction is not averted money will be lost through the fraud (Armour, 2012). Benefits and Limitations of Big Data for Governments Governments have embraced big data as a way of expanding their wealth of information so as to improve their efficiency. According to the Gartner’s report (2012), governments were the first to embrace big data. Governments have worked in partnerships to improve the transport systems within busy cities. For instance, the government in the United Kingdom has made great strides in improving transportation in the busy city of London using the Transport for London (TFL) network. The network is used to manage hundreds of footpaths, taxis, trains, cycle paths, as well as ferries. Millions of people use this government services to plan their day and prioritise their activities every day. The network collects a lot of data, such as ticketing, social media, surveys, and sensors, which is used to improve the transport system in the city. Governments can as well use big data to save on costs and enhance revenue collections. For instance, the government can use big data to enhance healthcare services to patients. Big data makes it possible to provide personalised, and more preventive medical attention. Healthcare can be provided through extensive, including home-based, constant monitoring. The MGI (2011) indicated that the U.S government alone saved over $300 billion by using big data. Just like companies, governments also find it hard to deal with the large and increasing volumes and velocity of big data. Furthermore, governments are faced with bureaucratic procedures. In analysing big data, different arms of the government have to come and work together so as to generate the desired result (Armour, 2012). Effects of Big Data for Companies in the Manufacturing Sector The manufacturing sector has played a major role in the growth of the Australian economy. However, in the past two decades, the sector has faced a myriad of challenges due to globalisation that has brought in stiff competition from emerging economies such as China. The contribution of the manufacturing sector to the overall Gross Domestic Product (GDP) of Australia has deteriorated from 29 per cent in the 1950’s to about 7.1 per cent in 2014. Many manufacturing firms have had to shift their operations overseas in a bid to cut on operational and production costs. Worse still quite a number of the manufacturing firms have had to shut down their operations and those that endured had to change. To return to its former glory, the Australian manufacturing sector needs to leverage on big data to achieve good efficiency, improve product design and quality. The manufacturing sector generates a lot of data from different sources including supply chain management systems and process controls. Data from the manufacturing industry is also projected to grow exponentially. The RFID play a vital role in collecting data within the sector. Nedelcu (2013) indicates that “the number of RFID tags sold worldwide is expected to rise from 12 million in 2011 to 209 billion in 2021”. The manufacturing sector also relies on different IT systems, such as computer-aided design, engineering, and manufacturing, digital manufacturing and product management. Manufacturers can combine data from these systems. According to the MGI (2011), there are up to seven different big data levers across the manufacturing value chain (as indicated in figure 1 below); In research and development, big data offers prospects for the manufacturers to improve their product development. In the supply chain, manufacturing enterprises, particularly in fast-moving consumer goods, have an opening to enhance supply chain planning and improve on demand forecasting. Demand can vary at any time and there is also the effect of constantly changing consumer preferences. Moreover, data generated from customer interactions can be used to improve sales and marketing strategies. Understanding customer behaviours helps to deliver in a ‘timely and profitable manner’. The manufacturing companies can as well reduce the defect levels and boost quality through improved detection of defects. The companies can use the data to shift to lean manufacturing by doing away with the products and processes that are not productive. Simulations of new manufacturing processes will also help in increasing efficiency within the sector (Nedelcu, 2013). However, the companies will have to build sufficient capacity to handle big data. A lot of data is likely to be generated from the value chain and supply chain processes that requires enhanced storage. Sufficient computing capacity will also be needed as well as expert data analytic people to get the necessary understanding of the data. Recommendations The demand for big data is rising and more companies are expected to adopt new technologies in collecting and analysing data from different sources. Adopting big data is not a cheap nor is it a simple exercise. Companies new to this area must understand that today data is key in surviving the current aggressive competitive globally. Consumers have access to data such that they can compare prices and treatment and will therefore choose the most informed competitor (Zikopoulos et al., 2012). It is also important to find the correct experts in data analytics. This field is very much complicated and only a few individuals can handle to intricacies it brings along. The experts will also provide the necessary information and will enable the company to get real time information and greater insight regarding the consumer behaviours and patterns. Moreover, the analytics should be able to make correct predictions of the likely trends into the future. Another crucial part of big data is privacy and data security. Today there are many cybercrimes and hacking cases. The company must be able to invest in appropriate technology to guarantee safety of the consumer’s information and private data. In the current information age people can abuse online systems to let out personal data that can be used by malicious people, such as terrorists and attackers, to harm others (Hopkins, 2011). Conclusion Since the emergence of data, it has come to be an integral part of society. The industry has experienced exponential growth over the past few years and is expected to grow at a faster rate into the future. Big data has created opportunities for individuals, companies and governments. Individuals have got the opportunity to use their mobile devices to lead better lives. Big data provides the opportunity for people to monitor their health and monitor the traffic situation and therefore are able to save huge sums of money. However, their use of big data is limited by privacy and data security concerns. Companies have leveraged on big data to gain competitive advantage and improve operational efficiency. Governments have leveraged on the data to improve service offering in health and transport. However, companies and governments have to deal with huge volumes of data and high data veracity. In Australia, the manufacturing sector can leverage on big data to cut costs and improve efficiencies through the value chain and the supply chain. References Armour, F. (2012). Introduction to big data, presentation at the symposium Big Data and Business Analytics: Defining a Framework, Centre for IT and Global Economy, Kogod School of Business, American University, Washington, DC. Columbus, L. (2012). Roundup of Big Data Forecasts and Market Estimates, 2012, Forbes. http://www.google.com/url?q=http://www.forbes.com/sites/louiscolumbus/20/12/08/16/roundup-of-big-data-forecasts-and-market-estimates, 2012/&sa=U&ved=OCB8QFjAAahUKEwip3PSos-XHAhWGtxQKHeYDBJk&sig2=H_KCQ7zl68se1wZagFqM6A&usg=AFQjCNE8DChHRQnQ_HzDBplvltB1qPgbDA. Gartner, Inc. (2012). Press Release. Gartner Says Big Data Creates Big Jobs: 4.4 Million IT Jobs Globally to Support Big Data By 2015. http://www.gartner.com/newsroom/id/2207915. Hopkins, B. (2011). Big opportunities in big data. Forrest Research, Inc. http://www.asterdata.com/newsletter-images/30-04-2012/resources/Forrester_Expand_Your_Digital_Horiz.pdf Kayyali, B., Knott, D. and Van Kuiken, S. (2013). The big-data revolution in US health care: Accelerating value and innovation, McKinsey & Company. McKinsey Global Institute (MGI). (2011). Big data: The next frontier for innovation, competition, and productivity. http://www.mckinsey.com/~/media/mckinsey/dotcom/insights%20and%20pubs/mgi/research/technology%20and%20innovation/big%20data/mgi_big_data_full_report.ashx McGuire, T. Manyika, J, and Chui, M. (2012). Why Big Data is the New Competitive Advantage. Ivey Business Journal. http://iveybusinessjournal.com/publication/why-big-data-is-the-new-competitive-advantage/ Nedelcu, B. (2013). About Big Data and its Challenges and Benefits in Manufacturing. Database Systems Journal, Vol. 5, no. 3, pp. 10-19. http://www.dbjournal.ro/archive/13/13_2.pdf Tajima, M. 2007, Strategic Value of RFID in Supply Chain Management, Journal of Purchasing and Supply Management, 13, 261-273. Zikopoulos, P., Eaton, C., DeRoos, D., Deutsch, T. and Lapis, G. (2012). Understanding Big Data. The McGraw Hill Companies. Read More