Essay Sample: How Big Data Analysis Can Play a Role in Development of Africa

"Big data" is a popular word in recent years. It usually refers to the network era to collect and analyze the work of huge amounts of data. However, since inception of the concept, economics has been using it to support and test a variety of theories and models. The earliest large-scale socio-economic data collection in the West can be brought back to Britain by 1700. Since then, with the evolution of economic research methods, data collection and analysis are more systematic and refined. People from the data centers extract one after another model and theory, which in turn guide the data collection process. Economist, Princeton University professor Angus Deaton (Angus Deaton) is in the collection and interpretation of economic data in the field of an important pioneer. Because of these contributions, he won the 2015 Nobel Prize in Economics. In 2014, Dhar published a study that pointed out that the correlation between income and lifestyle was $ 75,000. Beyond this income, lifestyle status is no longer increased.

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Dydon's Nobel Prize is not because of his job, but because of his contribution to his entire career. Nobel's official job presentation cited his 37 papers, of which 20 were published by him alone. According to the Nobel Committee's declaration, Dhar is mainly due to the recognition of the three aspects of the work. This work highlights a subset of the elements combined to define an integrated model of decision making using big data, business intelligence, decision support systems, and organizational learning all working together to provide the decision maker with a reliable visualization of the decision-related opportunities. The main objective of this work is to perform a theoretical analysis and discussion about these elements, thus providing an understanding of why and how they work together. At the time of (Dhar, 2014), proposed the so-called "almost perfect demand system" model in 1980. This model examines the issue of consumer priority. That is, how much is the distribution of the total amount of consumer goods when the total consumption (amount) is fixed. The understanding of the overall distribution of society is clearly important for economic policies and programs. The prevailing approach was to equate the behavior of the whole economy with a "representative. "He" based on the situation (the price of various items, their total consumption, etc.) choose the amount of consumption distribution, the pursuit of the utility function to maximize. The utility function is an independent variable in the consumer's choice and the function value on behalf of the consumer's ultimate "happiness" level. Besides, it determines the form of the utility function, is to develop the correct model of the key. If the consumer is rational, then the utility function will meet certain mathematical properties. The problem is that the utility function derived from the actual data does not satisfy these properties. In other words, the basis of economics "rational people" in this issue has been challenged.

Dumbill, (2013) points out that the problem is that the overall economic behavior is different from a rational person, although it is a collection of thousands of rational people. Hendler, (2014) then proposed the "almost perfect demand system" model. This model contains a set of parameters that are easily determined by data, which can describe rational and irrational utility functions and can easily determine whether a set of parameters is rational. This model can describe the individual's consumer behavior, but also can describe the overall social consumption distribution. Dumbill, (2013) points out that even in a very simple case, (all individual consumers use the same rational utility function), and the utility function of the overall consumption distribution is not necessarily rational. This is not a complex model but can reveal some of the essential problems in the distribution of consumption. Also, has a good mathematical nature, it is still widely used. Dydon's paper has been applied more than 4500 times (Asongu, Anyanwu, & Tchamyou, 2017). This is the first major contribution of Dayton: the establishment of a both faithful to the data, but also flexible enough demand model.

After the initial victory, Diedon in the consumption and savings model has made a comprehensive contribution. The question here is how the distribution of consumption and savings changes as a person changes in income. The mid-twentieth century popular global Keynesianism, which is based on the proportion of individuals used for consumption is the same. Therefore, when the government runs the deficit to temporarily increase the people's income, the people's spending will increase proportionately, thus stimulating demand and revitalizing the economy. Later, the liberal economist Fredman, who raised objections, raised the theory that argues that the level of human consumption is not determined by the income at that time but based on estimates of long-term income (Asongu, Anyanwu, & Tchamyou, 2017). Therefore, the level of consumption is stable, not with the income fluctuations. This theory is called "permanent income" theory. If that is right, Cairns's economic stimulus will have no effect.

Dumbill, (2013) conducted a detailed model study of consumer behavior. He found that the "permanent income" theory, although intuitively very reasonable, but careful analysis requires a lot of amendments. For example, people's lending capacity is not infinite. Moreover, in reality, most people live in the limits of lending capacity. They have no savings; all income is used for consumption. Also, the expectations of future income are not the same but based on the current income. Therefore, the high income, people will think that the future income is high, so instead of saving, used to increase consumption. Besides, vice versa to reduce the income, it would feel the bitter day is long, need to increase savings a rainy day. For these two reasons, human consumption fluctuations should be higher than income fluctuations. However, the total social consumption data show that consumption levels are more stable than income levels. This contradiction is called Dumbill paradox," so far there is no definite answer.

Nevertheless, it seems that the problem lies in the individual behavior and the overall behavior of the inconsistency. Also, Hendler, (2014) has a significant discovery for data collection. Ideally, to study changes in consumer behavior over time, we need to track individual consumers and collect their data at different times, called "panel data" in the industry. The United States Census Bureau and the Bureau of Labor Statistics collected and published very detailed panel data from the eighties of the last century, covering many aspects of population composition, income, and consumption. However, in other countries in the world, panel data due to the high cost, the application is not very common (Asongu, Anyanwu, & Tchamyou, 2017). Diedon found that, in fact, another "cross-sectional data" (cross-sectional data), that is, a crowd at a time of investigation (rather than tracking individuals), can also provide the same information to achieve the same accuracy. This technique of turning cross-sectional data into "pseudo-panel data" has played a significant role in promoting the use of data statistics in third world countries. Chavula (2012) combined with data and theory, individual and macro-proof of the style, for the consumption and savings research, has laid a solid empirical basis. This is his second major contribution.

Dumbill attaches great importance to data, model, and theoretical analysis in economic research, but he has not lost the fundamental goal of economics: to enhance people's quality of life. Therefore, in addition to data collection and analysis, he also focuses on a more basic question: what data best reflects the quality of life of the people? Dumbill, (2013) found that income is not the most reliable character of life quality. In developing countries, consumption and nutrition are a measure of the quality of life better. He also pioneered the use of the "poor price index" to reflect the poor level of consumption, with the subjective interviews to add "hard data, and the use of health to reflect the quality of life and other methods, the study of developing countries, the economic situation of the tools and means to make a great contribution.

Dhar, (2014) is also good at using creative data. For example, to investigate whether there is a patriarchal behavior in the crowd, he needs to know whether the household is equal to spending on boys and girls. However, the family survey only knows the total cost, cannot be accurate to the specific person (Asongu, Anyanwu, & Tchamyou, 2017). Therefore, he used a comparison of children before and after the level of changes in the level of luxury goods to calculate the family budget spent on children's money. For Daidon, the data is very important and valuable, but after all, just a tool. Chavula (2012) has a discerning eye, good at digging information from the data to quarantine, the development of a series of data analysis practice. This is his third great contribution to recognition.

The essence of big data in development of Africa

In the history of human development, the text generated before, fragmented knowledge is easy to annihilate with the tribal changes, more difficult to develop a higher level of wisdom. Therefore, the possession and use of knowledge have been an important means of human progress. Throughout the past, the development of each revolutionary technology, have brought a leap in production efficiency (Chavula, 2012). The invention of the wheel improves the efficiency of human transport and transportation, and the invention of telegraphy improves the efficiency of human communication. The essence of large data technology is to improve the accuracy of human activities, reduce the traditional way of "trial and error" costs, thereby enhancing the overall efficiency of society.

How can large data improve the accuracy of human activities in Africa? This needs to be analyzed from the information model. From the hierarchical model of information, information is divided into four levels from the bottom: data, information, knowledge, and wisdom. Where the data is only the number of words, sounds, and images, the information is composed of data plus the definition of content, knowledge is composed of information and rules, the highest level of wisdom is learned by knowledge and experience. The work of improving the accuracy of human activities should be located at the highest level of information, that is, the wisdom layer to complete. Whether it is for human beings or computer systems, it is easier to deal with data, information, knowledge of the three levels of information, its essence is the data storage and retrieval, but human processing efficiency and accuracy is lower than the computer. However, for how to get wisdom from knowledge, whether it is for humans or computers, is a very difficult thing.

Big Data Future Technology Trends

Large data technology in Africa should be like metallurgy, printing, and other technologies, in the near future to penetrate into all occupations, and comprehensively improve social productivity. The future of large data technology should show three trends: data generation and collection level, "crowd sourcing" model trends; data storage level, centralized storage, and unified modeling trends; data analysis mining level, to generate wisdom as the goal trend.

The Generating and Collecting Trend of "Universal Package."At the data generation and acquisition level, it is not feasible to rely on a single force to collect data in the context of future massive data, both at...