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本篇英国essay代写-Trends in analytics展示了分析的三个关键趋势。互联网，互联网和移动互联网表明，人们正在呼吁更多的数据源作为分析的基础。其次，云存储和编码标准为信息访问创建了更强大的信息积累。本篇essay代写由51due代写平台整理，供大家参考阅读。

Summary 

This paper shows three critical trends in analytics. Internet, Internet of Things and mobile Internet demonstrates that people is calling for more data sources as the basic of analysis. Second, cloud storage and coding standard create a more powerful information accumulation for information access. Last, big data and cloud computing show the trend of excellent information for the greater ability to process the collected information. These trends help human being handle information more effectively and efficiently and promote the analytics into a new era. No need to say, lots of consequence make this situation, but the most important factors for this development are the market demand and the thinking changes, which unveils the development of analytics in some extent is innovation. Based on this, it is suggested for the decision makers to be innovative in two different ways according to their market position and heading direction to stay on the top of analytics.

1 Introduction

The technology is developing faster and faster, especially in the analytics field. However, the essence of analytics has never been changed for solution to certain problems in daily life or production and the process of analytics can be simply divided into four parts, information collection, information accumulation, data mining and present solution. The technology grown by human being helps to optimize one or several parts in the analytics process so as to output the better solution. Based on this, this paper aims to summarize and analyze the most critical trends in analytics and reason its statement as well as try to articulate these trends’ development history. Then strategies for staying on the top of these trends will be put forward and analysis of pros and cons of these strategies will be pointed out. 

2 The most critical trends in analytics

2.1 More data sources

The premise and basis of analytics is to collect enough information or facts from all parts we can reach and the data, the parts of information and facts is considered to be objective (TM Porter, 1996). Consequently, people are pursuing to get more and more information (data) to make the analytics more comprehensive. CE Shannon (1948) has pointed out that iInformation is something that is used to eliminate random uncertainty. But the information itself is uncertain with three features. First, if the greater the probability of occurrence of message, the smaller the amount of information, and vice versa. Second, when the probability of occurrence is 1, which means all people in the world know it, the amount of information is 0. Third, the message should be equal to the amount of information contained in each message when a message is composed of more than one independent message. These features mentioned above help to quantify the information and consequently make the trends of more data sources possible and feasible. 

2.1.1 Internet

The Internet began in 1969 in the United States as the arpanet constituted of network of different computers by the generic protocol (R&eacute, K Albert, H Jeong&Amp, Albert-L&aacute and szl&oacute). It means that the information from different computers can be conveyed by this net in the virtual word. Compared with the traditional communication mode of letters or word of mouth, it loads more data in an more effective and efficient way and makes contributions for other parts of analytics.

2.1.2 Internet of Things

Internet of Things, Just as its name implies is an Internet connecting things in the real world. There are two implications. First, the core and basis of Internet of Things is still the Internet, but it extends and develops itself on the Internet. Second, the user side in the Internet of Things reaches to every things in the real world and communicates and exchanges information with each other. It is accomplished through a series of cognitive techniques such as intellisense, recognition technology, pervasive computing, etc (M Turner and A Brown, 2013). 

2.1.3 Mobile Internet

When the mobile communication and Internet meets together, the Mobile Internet is born. Similar to the Internet, the Mobile Internet connects the mobile devices like smartphone used by waling citizens. Thus, it can be considered as the extension of Internet

In brief summary, the listed technology for more data sources indicates that the collection of information covers broader aspects of society with the technology development ranging from the fixed PC to fixed devices to mobile deices used by waling people. According to the third feature mentioned above, the amount of collection information is greater than ever before and the ability of data collection will be successively enhanced in the future since people tend to avoid risk, which is equal to uncertainty with the more information. 

2.2 More powerful information accumulation

It is proper to think about the issues of information accumulation after collecting them from all sources. Unlike the tangible things, the information has its own characteristic including timeliness and infinity, etc (SF Ding, ZZ Shi, Y Liang and FX Jin, 2005). Thus people is eager to develop a more desirable information accumulation, which covers two aspects, transition and room. The former means that the information collected should be made into a certain form and this action is usually called standardization. Since most of information is stored as data, the description of all different kinds of information should be translated as a unified coding. The different coding has its own efficient. The better coding formulated, the less pressure on the storage room. Though different industry has developed its own industry standard of information accumulation leading to a more efficient of communication among different companies in the same sector. However, it also brings barriers for different corporate from different area (D Acemoglu and F Zilibotti, 1999). Thus, the unified standards in the broader filed is expecting. 

As for the storage space, the most outstanding technology is computer and cloud storage. The former’s feature is more excellent compared with the traditional methods like paper, film and tape.

2.2.1 Cloud storage

Cloud storage is a conception growing from the cloud computing, as a rising Internet storage technique. In plain terms, it allow users to put their information to cloud and they can download the information at any time everywhere through a device, which can surf the Internet. The advantages for Cloud storage is apparently providing a huge room for collected information and convenience to regain the information. 

2.3 Information excellence

Information excellence processes that are faster, more cost effective, higher quality, more flexible, more sustainable, or otherwise create differentiated value (J Weinman, 2015). In other words, it emphasizes the ability of information processing. The collected information is meaningless if its inner logic cannot be dug out. And the more data sources and greater information accumulation acquire a more powerful capacity to handle the information. Consequently, data mining and data analysis are extremely important in the analytics as the core for other technology service this part in some extent. In the future, there are two tasks for the information excellence. One is that it is supposed to be stronger to deal with more information in a faster speed. The other is people hope to use this technology with less costs. In some extent, the two requirements mentioned above are contradictory because the powerful ability to process the greater amount information means you should input more computer power - it is a concept to describe the computer ability especially in the calculation area - to grow the operation speed and ability when encountering jillion data, which implies costs rocket. However, the big data and cloud computing may help human being to achieve two dreams simultaneously. 

2.3.1 Big data

In the past, to process the data, Random analysis and sampling survey are usually chose to reduce the operational difficulty of data analysis since faith for people of that it is impossible to analyze all the data or it is too expense to analyze all the information for people at that technological level. However, with the development of computer and data processing, it is possible for people to handle all the data in a fast way without the compromise of analysis methods. Big data make us deal with the volume data in a crude and plain way (K Armstrong, 2014). Apparently, keynote for big data is to increase the computer power, but it does not mean that the super computer is called for. Instead, the technology of cluster help us to achieve. In a simple way to comprehend it, you can consider many computer work together. 

2.3.2 Cloud computing

Similarly to the big data, it also pay attention to deal with the volume data but it highlights the processing costs. Cloud computing is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet (JY Lee, JW Lee, WC Du and SD Kim). In other word, it allows people to use the service of cloud computing just as use the water or electric charged according to the usage amount, which means people can enjoy this function but no need to pay out the huge overheads. 

3.analysis of pros and cons for the trends

Undoubtedly, the trends mentioned above are extremely important in analytics for human being. With this new technology and innovation, people can handle the information from the world more easily in a deep way. First, the analysis containing more data and information can reduce the errors made by human operation. For example, though the Random Choice and Stratified Random Sampling are reckoned as scientist methods for research, it is a compromise way actually because human have no ways to deal with such huge quantity of information. Second, it also can bring benefits for prediction of future. With stronger analytics, the statistics will be more reliable and the math model is going to be closer to the real world. Last but bot the least, it speeds up other the development of science and humanities. Mathematical analysis can help these subjects break through their bottleneck with powerful data processing. However, the sharp sword has two sides and there is no deny that disadvantages for the trends are apparent as well as their pros. For one thing, the trends mentioned above pay attention to the data, which leads a tendency that people nearly take data as the whole facts when analyzing the real world. But, data just a part of information and it is impossible to make all information into data. Thus, once people rely on the data analysis too much, it may result in an ignorance of real world. For an other thing, the network security has been increasingly important for all the countries in the world. The flourishing data analysis development has created chances and excuse for the lawless man who merely goes after fame and money. 

4 How the trends come about 

Given that this paper is discussing the trends of analystics in view of management not the IT technology, the origin of this trends will be stated and explained in terms of market requirement and change management. 

4.1 Market requirement

It is a commonplace that consumers expect more and more nowadays. They want a the product with better performance but in a lower price. However, they will not tell you more detail about their requirement and more importantly, they do not know when they actually need this product. For example, people all want to deal with the greater amount of data or information in a faster way but they will not tell you they want to innovate a computer. Besides, the concept of big data and cloud computing has been put forward in the last century but it is payed attention in recent years. When the technology is created and the market recognize its value, can it develop itself.

4.2 Change management

As the trends and exact master work for the trends respectively, it is obviously that not all the innovation is unique. The innovation of computer and Internet is of course unparalleled, but the big data and cloud computing increasing the computer power actually are not so innovative compared with the former. It points out that the driving force for analytics development can be simple and plain when thinking model is changed. For example, as for the question of how to enhance the ability1 of dealing with more and more data, people can input more resources into education to output mathematician, or create a machine like computer to handle it, which methods attach importance to the individual power. However, the thinking of share can also help with more computer together connected by Internet to increase the computer power. In a word, the thinking of methods has great influence in the analytics.

5 Strategy for staying on the top of analystics filed

Like other filed or industry, the driving force for development of analystics is the innovation. The one knows how to be innovative at the right time catering for the market demand can stay on the top. So, the strategy to stay on the top pf analystics filed suggested in this paper is keeping innovating constantly. The trends mentioned above including the Internet and Internet of Things for more data sources, computer and cloud storage for greater information accumulation as well as big data and cloud computing for information excellence show the strength of innovation. In some extent, the technology and the thinking mode promote the analytics. The advantages of innovation in analystics are obvious. Once someone obtains innovation in one or several parts of analytics such like the more excellent information processing, he can be second to none in this field undoubtedly. However, there are also disadvantages and risks for innovation. First, the occasion to output the innovation is hard to prey because history has demonstrates that the market and public need time to discover and receive the new things. If you output too early, no can admire it. If you are too late to do so, you have been lag behind. Besides, the exact tactics of innovation can be different for various groups. In general, the ways of innovation can be divided into two kinds. One is the sustaining innovation, which aims to improve the original products’ feature to cater for mainstream users. The other is the disruptive innovation, which is usually equipped with more simple performance but lower prices(CM Christensen, 1997).   

Based on the statement above, it is suggested for the market leader to choose the former strategy, which means the ripe corporate in the market can improve its existing technology to maintain its market position with advantages in first-mover advantages. As for the coming men who wants to enter this field, it is advisable for them to pick up the disruptive innovation with changed thinking mode to occupy the niche market or even redefine the market because there is no competitive edge for the entrant to compete with the existing giant in the same field (CM Christensen and D Leslie, 2003). But the strategy is not invariable. The entrant will become the giant one day and the existing market leader may enter other field when the growth retardation happens. Thus, the decision makers should be sober of their position and heading direction so as to adjust the strategy. 

Conclusion

Prediction for the future development is a gamble, but the trends of analytics are obvious and the coping approaches have been put forward. The most important things for the decision makers to stay on the top of analytics is not only know what to do, but also recognize how to operate as well as adjust their strategy to cater for the different situation in the market. The statement and analysis mentioned above have show that innovation does not mean that one should input a huge resource to develop new technology but to observe what the market and public and the thinking mode timely. 

Reference

Theodore M. Porter. (1996). Trust in Numbers:The Pursuit of Objectivity in Science and Public Life. Princeton University Press.

C. E. A. Shannon. (1948). A mathematical theory of communication. Bell Labs Technical Journal, 5(4):3-55.

Reka Albert, Hawoong Jeong and Albert-Laszlo Barabasi. (1999) Internet: Diameter of the World-Wide Web. Nature, (6749):130-131.

Turner, Michael and Brown, Alistair. (2013). Introduction to the Internet of Things. Drug & Alcohol Dependence, 154(3):1-10. 

Shifei Ding, Zhong-Zhi Shi, Yong Liang and Feng-Xiang Jin. (2005). Information feature analysis and improved algorithm of PCA. International Conference on Machine Learning & Cybernetics, 3:1756-1761 Vol. 3. 

Daron Acemoglu and Fabrizio Zilibotti. (1999). Information Accumulation in Development. Journal of Economic Growth,  4(1):5-38.

J Weinman. (2015). Digital Disciplines, Strategic Supremacy. John Wiley & Sons, Inc, 1-20.

K Armstrong. (2014). Big data: a revolution that will transform how we live, work, and think. American Journal of Epidemiology, 17(9):181-183.

Jae Yoo Lee, Jung Woo Lee, Du Wan Cheun and Soo Dong Kim. (2009). A Quality Model for Evaluating Software-as-a-Service in Cloud Computing. Acis International Conference on Software Engineering Research, 261-266.

Clayton M. Christensen. (1997). The Innovator's Dilemma. Harvard Business School Press, 8(Spring):382.

Clayton M. Christensen and Michael E. Raynor. (2003). The Innovator's Solution. Harvard Business School Press. 

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