Understanding the digital divide in the era of big data

Understanding the digital divide in the era of big data

Big data has been a hot topic in recent years. The advantages of big data and the new trends of thought brought about by big data have formed a research boom. From random sampling to full samples, from requiring accuracy to dealing with confounds, from pursuing cause and effect to discovering correlations, the era of big data is changing our information environment and information processing thinking mode. However, not everyone can enter the era of big data at the same time. Just like every innovation and diffusion of media technology, sensitive enterprises and organizations are the pioneers and practitioners of big data, and are also the earliest beneficiaries of big data; while ordinary people Individuals show differences when facing big data. Some are slow to follow up on time, some are deficient in data analysis capabilities, some do not know how to find open data, and some are overwhelmed by data noise. The digital divide in the traditional Internet era has not been completely filled, but in the big data era, a new digital divide is forming and continues to affect and change people's political and economic status.

When discussing the digital divide in the era of big data, it is necessary to clearly distinguish between "digital difference" and "digital divide". From an etymological point of view, the two have similar meanings, and both are translated from "Digital Divide". However, in terms of communication effect or emotional color, the digital divide is more warning to people than the digital difference. In the era of big data, people create data and are surrounded by data. Limited by human vision and energy, there will inevitably be differences when people face data and make choices. For example, the personalized search engines and personalized favorites provided by the Internet will lead to the personalization of information browsing, and digital differences in the era of big data are inevitable. The digital divide arouses people's vigilance more than digital differences. The digital divide emphasizes differences in awareness and opportunities. The digital difference is knowing that there are opportunities but not taking action. The digital divide is wanting to take action but not having the ability or opportunity. Under the same background of big data, the digital divide may exist at three levels: data ownership, data analysis, and data thinking.

Three different analysis dimensions

(1) Digital divide with data

In the era of big data, “new”, “revolutionary” and “subversive” Terms such as "Big Data" appear frequently, but the issues referred to under the label "Big Data" have a long history. With the rise of the Internet, exponential growth of data, information overload and data processing problems have always been problems that people have to face. In the era of big data, technologies for data mining, storage, processing and application have developed rapidly. However, on the most basic issue of who owns the data, which is the cause of the digital divide, the current discussion about big data does not give people a clear answer. Satisfactory answer.

1. Data openness

For enterprises and governments, big data is a valuable asset. "The mastery of big data can be transformed into a source of economic value." Understand and manage society from a more accurate perspective. Therefore, enterprises and governments need to collect data from the general public. The dissemination of data is a bottom-up process. The first to own and control big data are the "digital pioneers" from enterprises and governments. However, bridging the digital divide What is needed is another form of data flow, that is, open data - allowing data to move from being owned by businesses and governments to being shared by the public. This is a top-down process. In real life, this top-down information flow faces resistance everywhere: on the one hand, companies regard data as their core competitiveness or core secrets, and spend a lot of manpower, material and financial resources on data analysis, so it is difficult to Achieve maximum sharing of data; on the other hand, the pace of government data disclosure is still relatively slow, and it is still difficult for the public to obtain valuable information.

The digital differences caused by open data need to be solved by open data. Which data can be opened to the public, in what form, who is the specific implementer, and who can pay for the "free rider" behavior in the process of data opening are all issues that need to be considered.

Big data can not only generate commercial value, but also has the characteristics of fairness. In this process, data closely related to public interests need to be open. As early as January 17, 2007, our country passed the "People's "Regulations on Information Disclosure of the People's Republic of China" clearly stipulates the principles, scope, methods, procedures and supervision and guarantee system. In the era of big data, the government should further increase its efforts to open data, and at the same time educate the public on how to obtain data, so as to realize data ownership and enjoyment by the people. As a public resource, the fairness of data distribution, like the fairness of wealth distribution, will have a very large impact on the social structure. Governments and enterprises can rely on the development of data storage and analysis technology to do "data banking" business. , giving every citizen the opportunity to store and retrieve the data they want in the "data bank". In the book "Big Data", domestic scholar Tu Zipei thinks about open data from the perspective of data democracy, pointing out that the open data movement will promote "a series of movements and slogans such as open politics, open government, open media, open cities, etc." .This provides a feasible way to eliminate the digital divide formed by data ownership and build a brave new world of data fairness.

2. Data collection

The foundation of the big data era lies in massive data. How big is big data? The latest report of the "McKinsey Global Institute" defines big data: "Big data refers to data groups whose size exceeds the capture, storage, management and analysis capabilities of traditional database software tools." Moreover, the standards of big data have changed over time. The exponential growth of data is also changing. Today, when we talk about big data, we often use petabytes as the unit. Massive data provides more detailed information, but there are also some hidden concerns, that is, the value density of data is too small, so collecting data and finding valuable information in massive data The cost is too high. In an exclusive interview with China Economic Weekly reporter Xie Wei, Schonberger said: "In many ways, we are still living in an era of 'small data', in which collecting data is very time-consuming, expensive and difficult." Big data The era of data collection is a huge project, and big data is far from reaching the stage where ordinary people can afford it.

The digital divide in collecting data does not seem to be reducing in the era of big data. Instead, it is gradually expanding with the development of big data processing technology. For the media and companies, collecting and processing data is not easy. The famous Harvard Business Review magazine conducted a scientific survey on the application of big data by the world's Fortune 1000 companies and found that "most companies We are still in the introductory stage of big data and have not yet developed the ability to truly mine big data.” Furthermore, “only 21% of the respondents believe that the data accessibility of their companies is good enough or world-class.” They believe that the analytical capabilities of their company are good enough or have reached the world level." Obviously, for the general public, collecting and mining data is more difficult and the differences are greater. In an era when search engines dominate the flow of information, the public has created a digital divide because of the use of different search engines. There is a difference between using ordinary search engines and using more professional search engines and databases. In the era of big data, the public not only needs to know how to use professional search engines, but also needs to quickly find the most valuable information among massive amounts of information. Due to differences in public capabilities, the digital divide created during the collection stage will be unavoidable. Moreover, data under the Internet is in a state of constant updating, and timeliness is very important and critical. In their research on the “knowledge gap”, Western scholars J.S. Atima and F.G. Klein once mentioned the “ceiling effect”, which refers to the gradual reduction of the knowledge gap over time. However, in the Internet era, the value of information is closely related to its timeliness. Even if the "gap" in data collection among the public gradually narrows over time, the value of data owned by latecomers will be greatly reduced.

Levinson's discussion of information overload, a representative figure of the media environment school, may help alleviate the differences caused by data collection in the era of big data. He believes that establishing information classification rules can solve the problem of information overload, such as building books on books. Classification rules and operating according to this rule can solve the problem of information overload in libraries. This idea has universal enlightenment significance in solving the information overload that has plagued mankind for a long time.

(2) Analyzing the digital divide in data

There will be differences in who owns the data, and in the case of equal ownership of data, differences will also arise in the public's ability to use data. Big data includes both structured data based on quantitative relationships and unstructured data based on qualitative descriptions. Moreover, unstructured data often accounts for a large proportion. Therefore, in the era of big data, having data does not mean that we can also use the data. The digital gap in analyzing data and extracting value still needs to arouse our vigilance.

1. Data deletion

The era of big data is an era of highly fragmented information, duplication, noise, redundancy and human factors in information (cyber trolls) etc., all affect people's analysis and utilization of data. At this time, deleting data is as important as collecting data. In addition to "The Era of Big Data: Big Changes in Life, Work and Thinking", Schoenberg also has a profoundly influential book - "Deletion: The Choice of Big Data". In this work, Schonberger reminds people that in the era of big data, "memory has become the norm, and forgetting has become the exception." Therefore, we must pay attention to the way to choose information; in this "world without forgetting," forgetting has become a problem. This precious information processing method and rights data deletion is a humane issue. As the "computer native generation" grows up, everyone has their own feelings of youthfulness, embarrassment, and even shame in looking back. In the past, before the Internet, people would try to forget these small happy pasts, but the memory of the Internet makes everyone small and facing the reality that people may pay for the mistakes they made ten years ago.

Deletion is also a technical issue. In the Internet era, data with a long history will gradually become "data garbage", which not only takes up a lot of storage resources, but also affects the analysis and evaluation of current data. Deleting data has become an essential data processing method in the era of big data. But when it comes to individuals, a problem arises. People cannot evaluate and process information like machines. They can only process information based on past experience. Another foreign scholar, Ticino, when analyzing the reasons for the formation of the "knowledge gap" mentioned that personal information reserves can also create a "knowledge gap", that is, "formal education and information obtained from mass media will help educate people." "People with higher education levels provide the background for understanding knowledge." The era of big data has not changed people's habits of receiving information. Therefore, in the era of big data, people with higher education levels still learn to accept and delete information first. Deletion also has a philosophical meaning. In the era of big data, selection means deletion. People's acceptance of data has a zero-sum effect. "Moving towards a set of data means giving up other data. This is also deletion in another sense. Processing low-quality outdated data is the prerequisite for discovering the meaning of big data. Well-known "Streamlining: The Rules for Business Winning in the Big Data Era" written by scholar Matthew E. May also mentions the problem of information deletion and streamlining in the big data era. In the big data era, it is possible to quickly obtain the most valuable information in the first time. Companies that value data will gradually develop, while companies that do not understand big data or are addicted to big data will gradually fall behind.

2. Data is available

The era of big data provides a diverse environment. , detailed and complex data environment, in the era of big data, all reality can be quantified into data. However, if you use big data to create value, you need to find valuable data from massive data and restore the data to reality. ”, having a data set, no matter how big or small they are, doesn’t bring any value on its own. “The ultimate value of big data is still reflected in the “availability” of the data.

At the same time, issues about the digital divide also arise in the "availability" of data. Big data is like providing a delicious nut, which is difficult to open without the help of tools, and the "cloud storage and cloud computing" used by big data It’s not something that anyone in the public can easily grasp. A small number of people have mastered the ability to analyze and apply data, while a considerable number of people are at a loss when faced with the vast sea of ??big data, and eventually fall into the anxiety of information overload.

Bridging the "digital divide" in data availability requires making data intuitive and visible, which remains a topic involving fairness. Reducing data to reality requires both artificial intelligence technology for data analysis and sharp human analysis and judgment capabilities. More importantly, it is necessary to truly convey the environment prompted by the data to the public. The government and the media still have a lot to do. First of all, they need to popularize data processing technology and regard interpreting big data on public affairs as a public undertaking. For example, in the 1960s, it was called "artificial intelligence". Father John McCarthy once predicted that "one day, computing may become a public facility." Secondly, the media must play the role of "ferrer" between data and reality, and not only use big data to analyze the audience to obtain benefits. , but also to reflect the fairness of the media, so that the audience can understand and benefit from big data. For example, when reporting on tornadoes, American reporters "overlay the damage data of houses damaged by tornadoes with maps to create a big data map." In this way, the audience can not only understand the general area of ??disaster caused by tornadoes more accurately, but also accurately understand Specifics of damage caused by tornadoes in a certain area.

(3) Digital Divide in Data Thinking

The important change brought about by the big data craze is the change in data thinking. There are many discussions about big data, but not all. With the concept of "big data", our information environment has naturally undergone qualitative changes. But today, as the Internet gradually moves towards massive data, the big data thinking from "digital survival" to "digital survival" has given people an extra A perspective on understanding the world. The digital divide outside of big data technology comes from people's thinking, that is, there are differences in how people treat data.

1. Beyond big data

One of the thinking in the big data era is to transcend the "data myth" and regard data as a tool rather than a data hegemony. In the book "Big Data Era", Schonberger pointed out three changes brought about by big data: not random samples, but all data; not accuracy, but confusion; not causality, but correlation. These changes have a great impact on traditional quantitative research methods. However, improvements in quantitative methods cannot replace qualitative research. You must go beyond the data to discover the meaning and value behind the data. Therefore, big data thinking includes three levels. The first level is to discover massive data and understand the potential value of massive data, but cannot make good use of the data; the second level is to be able to make good use of data, but often falls into data worship and cannot solve the problem of meaning; The third level is to be able to utilize data, but also be able to transcend data and discover value at the same time. These three levels are both a diachronic process and a cognitive process in the development process of big data. It will take time for the concept of big data to rise and spread, so the three-level "digital divide" in data thinking will still exist for a long time.

2. Big data literacy

Reducing the digital divide also requires efforts in both hardware and software directions, and this is still the case in the era of big data. Judging from China's Internet statistical reports in recent years, the digital divide in hardware is gradually narrowing, while the digital divide in software is still expanding. Bridging the digital divide requires governments, enterprises, etc. to open up public data and provide ways to use public data. It also needs to improve the big data literacy of all citizens and realize the ownership and enjoyment of big data by the people. Data literacy is also called data information literacy, which mainly refers to people's abilities in the collection, organization and management, processing and analysis, sharing and collaborative innovation and utilization of scientific data, as well as in the process of data production, management and release. ethics and behavioral norms.

Only by comprehensively improving the data literacy of all people can we confidently welcome the arrival of the big data era and use big data to create new benefits for mankind.