Introduction
Currently, many enterprises are investing in big data technologies because they are seen as a future of information analysis. It is a set of approaches, tools, and methods that are used to handle enormous amounts of various kinds of structured and unstructured data in a way that would be easy for people to understand. There are different methods and techniques of analysis such as crowdsourcing and data mining methods.
It has its benefits and limitations for individuals, companies, and governments. The goal of this research project is to analyze the implications of big data to propose recommendations on how enterprises should get ready for the changes.
Big Data and Individuals
Benefits of Big Data for Individuals
Big data can be extremely useful for every individual. New technologies are always being developed, and companies are looking for ways to utilize the available information. The current situation in the world can only be described as a knowledge revolution. Gadgets are capable of storing extraordinary amounts of information and can perform millions of tasks at once.
The first benefit for individuals is that such technologies could be extremely useful for a small business or family budget planning. Mobile phone applications that analyze vast amounts of data could be developed in the future and everyone would be able to make decisions based on collected and processed information.
Companies that develop the software and methods of analysis are slowly making small steps in that direction. Wu et al. (2013, p. 97) claim that ‘data collection has grown tremendously and is beyond the ability of commonly used software tools to capture, manage and process.’ This signifies that information processing it developing at an extremely fast pace.
However, it is still in need of development to meet the necessary requirements of its users. Another important aspect that should be noted is that big data allows every individual to influence the decision-making of companies in one way or another even if such influence is relatively not that significant.
Limitations of Big Data for Individuals
There are few limitations connected with usage of big data technologies for individuals. First of all, privacy may be breached. Some people say that some data should be openly available for general public but it is not so simple. It may contain the information about locations and phone calls that should be private.
The second limitation is that such enormous amounts of data are hard for some to understand without an external help. Cron, Nguyen, and Parameswaran (2012, p. 8) found that ‘in such massive data contexts, getting data into a form amenable to analysis and visualization is challenging’. In other words, companies should put a lot of effort into the development of tools that would help to visualize and process such massive amounts of information.
There are connections between all of the pieces of data. However, it is nearly impossible to detect correct correlations for most individuals. Most of them are utterly meaningless, and the goal of big data is to eliminate this problem. Another common issue is that current data processing programs use certain algorithms that can be abused if one has the knowledge of such weaknesses.
Current big data systems are not able to recognize such cases. The biggest problem is sentence structures because they are still extremely hard to analyze. Nevertheless, developers are trying to minimize possible errors that could be made by their systems.
Big Data and Companies
Benefits of Big Data for Companies
The first advantage of big data technologies is that they help with the decision-making process. Gathered data is stored and analyzed to develop new strategies. Also, it allows a firm to see the effectiveness of said strategies through a variety of financial metrics.
Nowadays, the use of the internet is essential for every company and data about web page visits, online purchases, web traffic, and other crucial statistics should be stored. The second benefit is that the new technologies are being developed that help with the visual representation. Pflugfelder (2013, p. 19) states that ‘in some cases, these collaborations may result in infographics, which have exploded in popularity in the last few years, or data visualizations of more traditional forms’.
This means that it is necessary for enterprises to collaborate because they may share technologies to provide a range of benefits to each other. Infographics are especially useful for companies because they are relatively easy to understand and can be demonstrated at meetings and conferences.
It is necessary to build an analytics team if an enterprise plans to implement a big data project (Gudivada, Baeza-Yates, & Raghavan 2015). Each member of such group has a set of tasks and responsibilities to ensure the success of operations.
Limitations of Big Data for Companies
Numerous restrictions are connected with the usage of big data technologies for enterprises. First of all, some barriers prevent companies in this industry from fully incorporating this approach. The knowledge gap is a critical issue. Many businesses think that these kinds of technologies are disruptive to the business and prefer more traditional methods.
Also, some firms are just too small and do not have sufficient funds to start using big data technologies. Another issue is that some valuable information may be lost when processed because it is just numbers, and human interference is always necessary. The second restriction is that the implementation of big data technologies that are still not fully developed requires extreme amounts of resources.
Xu, Cai, and Liang (2015, p. 205) claim that ‘new tools and techniques are obviously needed if the data are too large or too complex to surpass the capacity of existing methods to process and analyze’. This claim signifies that the development of new computer software is of utmost importance, and most enterprises are still not ready to use big data.
Big Data and Governments
Benefits of Big Data for Governments
There are numerous benefits of big data for authorities. The first benefit is that with the introduction of big data, governments are able to perform the same operations that they were doing but much more cheaply and efficiently. It may be helpful in such areas as cyber security, public safety and justice, and finance and operations.
The second benefit is its extreme usefulness when governments try to guarantee well-being of citizens and protect the country. Search engine queries can be analyzed to predict possible outbreaks of epidemic diseases and the governments would be able to stop such disasters from happening.
Phone call records can also be monitored to prevent any terrorist attacks. Currently, such companies as IBM offer analysis of data to governments. Very soon, close to 35 percents of all the collected data will be useful because of the introduction of new technologies (Ohlhorst 2013).
Limitations of Big Data for Governments
There are also numerous restrictions that come with the usage of big data. The most major issue that governments have to deal with is a possibility of information leakage. This is especially dangerous in the era of the Internet when information may be posted online, and millions of users are able to see it.
The safety of the citizens is of utmost importance for all the governments, and necessary measures should be taken to protect the data to guarantee the privacy of all the individuals. The fact that big data is extremely helpful in fighting crime and terrorism is not questionable. The second limitation is the issue of trust.
Some people do not believe the authority most of the time with their information and often voice their opinions against laws and acts that are deemed as excessive. It has been a part of a famous scandal connected with Edward Snowden, who has leaked secret information.
Liang et al. (2015, p. 2385) state that ‘providing security services increases the computation and the occupation of system resources’. In other words, governments need to sacrifice some resources to protect the valuable information.
Effects of Big Data on Companies in Manufacturing Industry
The analysis of data is especially beneficial for the companies. First of all, various procedures are used by businesses to help with the decision-making process. They analyze, generate and store all kinds of data from various sources such as clients and distributors. Technologies can be bought from companies that are focused on these types of operations.
Certain types of data should be collected for decision-making purposes. This is especially true for firms that operate in the manufacturing sector because analytics are extremely crucial to assess the needs of customers. For example, information about customer profiles should be stored to see the effectiveness of the current strategy of a company.
Knowledge about orders is also crucial for every business. Customer service reports should be gathered because opinions of clients are essential, and every enterprise should be able to base their future strategies with those views in mind. Some metrics are commonly used by the companies in this industry to measure the quality and satisfaction levels of clients that help to identify if expenses are reasonable or not.
Big data is extremely helpful when a company is trying to develop a servitization strategy. Opresnik and Taisch (2015, p. 175) claim that ‘servitization will be used even more by manufacturing enterprises to create additional and more secure revenue streams, since global competition is increasing while margins are lowering’. This means that big data is capable of analyzing if produced products should be delivered to customers with various services as an additional value.
Recommendations
It is highly necessary for corporations to be prepared for big data. After the analysis of peer-reviewed journal articles I was able to get a better understanding of the topic and would like to make recommendations for companies that are still hesitant about the implementation of big data systems.
The first recommendation is that the help of big data analytics companies should be requested. They should help on each step of the process thanks to their experience that they have earned during many years in the business. These companies should help with the analysis of information that would not have been recognized by most of the systems.
Analysis of data that has different values is much harder than analysis of simple data. This happens because the information is collected from various sources and in many forms. These companies also offer other kinds of services such as data management that are very useful for the firms. My second recommendation is that a company should carefully develop its privacy policy to maximize the benefits from the collected information while protecting the privacy of the customers.
Obtained data may be shared with business partners, law enforcement, service providers, and affiliates. However, it should be encrypted and should not reveal any private information. Also, there is a need to publish a remedy if a client’s security is breached. However, a company should do it in case of an emergency to minimize possible damage to the image of a corporation.
My final recommendation is that companies should consider the development of their own big data systems. Alexander, Hoisie, and Szalay (2011, p. 11) found that ‘in addition to the hardware investments required, there is a pressing need to invest in research and development of analysis algorithms’.
Differently put, a company could develop their own computer software or analysis methods to get ahead of competitors in the market. If it is not possible, some employees should be trained to work with big data systems because they are extremely complex and require vast knowledge.
Conclusion
In conclusion, big data is becoming a huge part of the modern world. Companies that still not have invested in this method of information processing should revise their opinions on this subject. For me, the most significant part of this research was to get a better understanding of the state of big data technologies, and what could possibly happen to them in the future.
Initially, I would not have been able to formulate a definition of big data because this term is not widely known. Having analyzed the peer-reviewed articles I came to the conclusion that the technologies are still being in relatively early stage of development, and a lot needs to be done. Most importantly, I have learned that big data currently has its limitations for all the sectors.
This information is helpful for me as a consultant because I am able to develop my own opinion on this subject. However, I have not sufficiently improved my understanding of the way big data functions because I do not have the sufficient knowledge of the subject. As a next step, I need to research the literature on this topic to develop as a business consultant.
Overall, big data has its benefits and limitations. Most of the companies gather various data, but most of it is not processed. Xhafa (2015, p. 1) found that ‘companies can adopt data-driven knowledge for their decision-making by finding meaningful patterns in their business data’.
Differently put, enterprises should use available information to the full potential. Nevertheless, technologies are always developing, and they should be widely used by both companies and governments. It is the future of analytics, but other methods should not be disregarded right away because they have proven their efficiency.
Reference List
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Cron, A, Nguyen, H, & Parameswaran, A 2012, ‘Big data’, XRDS: Crossroads, The ACM Magazine for Students, vol. 19, no. 1, pp. 7-8.
Gudivada, V, Baeza-Yates, R, & Raghavan, V 2015, ‘Big data: promises and problems’, Computer, vol. 48 no. 3, pp. 20-23.
Liang, Q, Ren, J, Liang, J, Zhang, B, Pi, Y, & Zhao, C 2015, ‘Security in big data’, Security and Communication Networks, vol. 8, no. 14, pp. 2383-2385.
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Opresnik, D, & Taisch, M 2015, ‘The value of big data in servitization’, International Journal of Production Economics, vol. 165, no.1, pp. 174-184.
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Wu, X, Zhu, X, Wu, G, & Ding, W 2013, ‘Data mining with big data’, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107.