Big Data seems to be the new buzzword of the moment and the solution to all of society’s problems. Often we hear people coming up with studies involving a great amount of data aggregated from Twitter, Facebook and so on. I truly believe these studies are good; they take snapshots of scenes, let us know of interesting moments in a specific time and give us an overall idea of the problem.
boyd and Crawford (2012) define big data as “a cultural, technological, and scholarly phenomenon that rests on the interplay of: (1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets. (2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims. (3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.” (p. 663)
Big Data is usually thought as big numbers, the big N approached quantitatively. These numbers are generated based on people’s produced data; people that are online and constantly talking, sharing, posting, tweeting and “liking” things. But what about the people that are not doing that frequently, or even, not doing these activities at all? If we take Big Data and extend it to the ones experiencing digital inequalities, we would be imposing a colonial practice in which the voice of those constantly online will be obscuring the voice of those who are not. These voices are often clashing in different of contexts since they are rooted in social tensions and differences of power.
So, how can Big Data tell us the story of the people that are on the “wrong” side of the digital divide?
Mary L. Gray (2011) makes the case that Critical Ethnography is a practice of Big Data. She invites us to think of Big Data not solely as numbers and quantitative approaches, but also as a practice that is able to balance the value of ethnographic significance and statistical significance. Big Data is usually deeply concerned in mashing as much number as possible to be able to have some sort of reliability and statistics strength. The more you can get, the more reliable the information is.
Qualitative work is often seen as being too specific and doesn’t tell us anything, but Gray argues the opposite, qualitative approaches tell us something different, they give us a different perspective of the story. Ethnographic significance should be integrated as a complement in collaboration with statistical significance, so we are able to get something transformatively different.
I agree with Gray; at an earlier post here on the Social Informatics Blog (Digital Divide Research: one myth, problem and challenge) I make the case that the Digital Divide Research should move on from the statistical charts, census and Big Data, and go in the field to tell us about the context of those who are not on the internet, or not as often due to digital inequalities.
Big Data was the reason why I ended up going to the slum of Gurigica in Vitoria, Brazil. According to the census, the locals have a very low access to the LAN Houses and Telecentros that are inside the community. But if it wasn’t for my ethnographic research, I would have never known that this was happening due to the activities of the drug cartel that didn’t allow them to circulate freely on the streets. Therefore, Critical Ethnography is a powerful tool to approach the issues of the Digital Divide and contextualize the notions that Big Data gives us.
References (I highly recommend Gray’s video):
danah boyd, & Crawford, K. (2012). CRITICAL QUESTIONS FOR BIG DATA.Information, Communication & Society, 15(5), 662-679.
Gray, M. L. (2011). Anthropology as BIG DATA: Making the case for ethnography as a critical dimension in media and technology studies.
The Myth: Digital Divide has a small literature. Pretty much, almost every book or paper on the topic will say this. I used to believe that not enough work has been done on Digital Divide, until I started studying for my qualifying exam. Fortunately or unfortunately I found out that the literature is actually very large. The problem is that the digital divide research is spread throughout all kinds of disciplines, such as: ICT4D, Community Informatics, HCI, Social Informatics, Sociology and Communication studies. In fact, the literature is not new, because it goes way back when academics were studying the diffusion of telephones and televisions.
The Problem: Quantitative approaches are addressed to answer the wrong questions. A lot of the research done on digital divide is done quantitatively. They rely on the data collected by International Telecommunication Union, World Bank and other agencies. And what these researches do is to identify a digital gap and try to correlate that gap with some sort of social, economic or political issue. For example, there is a cross country study done by Luis Andres, he says that, based on his quantitative analysis, in order to bridge the digital gap we need to liberalize the telecommunication market to promote internet provider competition. I agree, but Brazil has had this free market for about 15 years, and we still have a vast digital divide. So, obviously, this is not an issue for Brazil, something must be happening that is keeping the divide wide. What I’m trying to say here is that in order to fully understand and propose meaningful solutions, the digital divide research requires local and context based research. It doesn’t matter if it’s quantitative or qualitative, I don’t want to get into this argument, but we need to understand that each country has its own set of policies, people have different cultural backgrounds, so solutions need to be tailored and not based on general analysis.
The Challenge: “How to talk to policymakers?”. Policymakers of the digital divide tend to have a technological deterministic perspective. They focus on single factors, such as “access”, because they are convenient since they are easy to measure. These simple measures can be used to influence public opinion since lay people can relate to them. Policymakers also need to justify allocation of resources, which is easier to do when they can create benchmarks (Barzilai-Nahon, 2006). So policymakers are strung up on numbers, and how can we show them that subjective factors such as education and training can be of much better value to promote the digital inclusion than pure access? I don’t want to blame policymakers for approaching the digital divide quantitatively, but I’d like to leave this challenge for us, digital divide scholars, to realize a way to start conversations with people that can only see numbers.
Barzilai-Nahon, Karine. 2006. “Gaps and Bits: Conceptualizing Measurements for Digital Divide/s.” Information Society 22:269-278.
Brazil is currently the world’s fifth largest country, both by geographical area and by population. It is the world’s eighth largest economy by nominal GDP with one of the world’s fastest growing major economies (World Bank, 2011). With such outstanding macro indexes, it is a shame to look into a close up reality of the Brazilian society, which is characterized by its abysmal gap between the rich and the poor. The marginalized poor people are not only deprived from decent services to their basic needs, but also to the access of technology. About 47% of the Brazilian population never used a computer, and 66% of the population never had access to the Internet. 64% of the people that had/have some sort of access to the Internet, never had a formal training on how to use the internet (CGI, 2006), which highlights the need of critical education and consciousness of its use.
The Brazilian government has been trying to fight such digital divide by introducing digital inclusion programs in order to socially include the marginalized population. Before moving on, I would like to revisit such terms since they have different meanings but often times are used as the if they were the same. Digital divide refers to inequalities between any groups in terms of access and use of digital technologies. Digital divide is usually concerned with statistics of access and can help us by acknowledging where the problem is situated. Digital inclusion refers to the process of democratizing the access to digital technologies in a way that the digitally marginalized is inserted in the information society. For digital inclusion, access is not enough; the process should be worried about empowering the marginalized and teach them how to appropriate the digital technologies.
Digital Inclusion policies in Brazil have a technological deterministic approach, in which policymakers are mainly concerned about giving access to technology to the poor classes. Issues such as empowerment and appropriation of technology don’t seem to be on their priorities. In 2005 the Brazilian government invested over $400 million in various programs, equipment, infra-structure and tools to afford the poor population to access to technology. The Brazilian government was mostly concerned about lowering the price of computers and pushing them into the people’s homes instead of providing social programs that would involve technology. (Rebelo, 2005; “Info Plantao” 2007).
Currently, the Brazilian government has two main strategies to promote digital inclusion: LAN houses and Telecentros. LAN houses are establishments where, like a cyber cafe, people can pay to use a computer with Internet access and a local network (LAN). According to the Internet Steering Committee in Brazil, LAN houses are responsible for almost 50% of Internet access in Brazil and in poor areas it is responsible for 82% of the accesses (“O GLOBO”, 2009). Although LAN houses are privately owned business, the government provides several credit lines and loans with low interest rate in order to spread the number of facilities, especially in poor areas. Telecentros are facilities where the general public can access the computers for free. The computers are equipped with a variety of software and connected to the Internet. Several computer lectures are offered to the population throughout the year in order to fight the digital divide. Some Telecentro programs are owned by the government and some others by the private sector. Telecentros are usually implemented in areas where the populations with low income reside.
Because of the relative nuance of the Digital Inclusion programs in Brazil and even in the rest of the world, little substantive research/theory literature exists on the effective ways to measure change brought about by providing access to ICTs (O’Neil, 2002). The reason for such inefficiency is due to the erroneous methodological approach by policymakers whom are mostly strung up on hard numbers and statistics. The “problematique” of Digital Inclusion should be approached by qualitative methods which work well for exploratory studies in new fields as monitoring their progress and offers a holistic view of a dynamic situation (Patton, 1990). In this way, Digital inclusion research can build on Social Informatics research that considers social factors influencing ICT use. Social Informatics provides theoretical tools that can assist researchers in considering and understanding the social factors influencing ICT utilization (Kling, 2000).
The topic of digital inclusion hasn’t been fully explored in the eyes of Social Informatics. A lot of analyses have been done on policies regarding the topic, but a proper study that researches the users’ behavior, culture and attitude towards digital technology is almost nonexistent. No one can argue whether digital inclusion leads to social inclusion or not, because the previous studies try to tackle such question in terms of numbers, and as I already mentioned, it can’t be answered quantitatively. Digital Inclusion has been my main research interest, and as a Social Informatics PhD student, my goal is to ethnographically explore the actual digital inclusion units (LAN houses and telecentros), talk to people and understand their culture in order to properly answer some questions.
World Bank (2011, April 15). World Development Indicators database. Retrieved from http://go.worldbank.org/I358WVLTT0
CGI (2006, May 30). Survey on the Use of Information and Communication Technologies in Brazil: e-Government Indicators – Households and Enterprises. Retrieved from
Info Plantao. Retrieved December 7, 2011, from
Kling, R. (2000). Learning about information technologies and social change: the contribution of social informatics. The Information Society. 16(3), 217-232.
O Globo. Retrieved December 7, 2011, from
O’Neil, D. (2002). Assessing community informatics: a review of methodological approaches for evaluating community networks and community technology centers. Internet Research, 12(1), 76-102.
Patton, M. (1990). Qualitative evaluation and research methods. Beverly Hills, CA: Sage.
Rebelo, P. (2005, May 12). Inclusão digital: o que é e a quem se destina? Webinsider. Retrieved from
From: Social Informatics: Principles, Theory, and Practices
(Sawyer and Tyworth)
We see integrated criminal justice systems (ICJS) as one area that presents a significant opportunity for social informaticists to both develop theory and contribute to practice. E-Government, or digital governance, is both an emerging area of scholarship and a fast evolving phenomenon in society. This is particularly true for issues of law enforcement and national defense where there is increasing pressure to computerize or modernize existing information and communication technology (ICT) given the recent attention to international terrorism (National Commission on Terrorist Attacks upon the United States, 2004). And, for at least the United States, it may be that there is no other area where the consequences of adhering to the deterministic view of ICT are as potentially catastrophic. In spite of these risks, the deterministic model continues to be advocated.
For example, in his article on improving intelligence analyzing systems Strickland (Strickland, 2004) focused exclusively on technological change as the solution to the problems of information sharing among agencies. Strickland identifies data disintegration, problems in analytical methodology, and technological obsolescence as the primary areas of concern. Yet, as Richard Shelby noted in his addendum to the Senate Select Committee investigating pre- and post-9/11 intelligence (Shelby, 2002):
The CIA’s chronic failure, before September 11, to share with other agencies the names of known Al-Qa’ida terrorists who it knew to be in the country allowed at least two such terrorists the opportunity to live, move, and prepare for the attacks without hindrance from the very federal officials whose job it is to find them. Sadly, the CIA seems to have concluded that the maintenance of its information monopoly was more important that stopping terrorists from entering or operating within the United States.
Though Senator Shelby’s language is polemic, the message is clear: without significant changes to the organizational cultures, simply implementing new technological systems or updating existing ones will in many instances fail to achieve policy goals. It is exactly this type of problem for which social informatics theory is particularly applicable. An e-Government policy area directly related to the issue of intelligence sharing is the problem of integrating information systems among law enforcement and criminal justice agencies. Prior to, but especially after 9/11, there has been a significant movement within government to integrate ICT across law enforcement and criminal justice agency boundaries in order to facilitate cross-agency communication and information sharing. See for example (General Accountability Office, 2003).
Criminal justice information systems have historically been developed in an ad hoc manner, tailored to the needs of the particular agency, and with minimal support resources (either fiscal or expertise) (Dunworth, 2000, 2005; Sawyer, Tapia, Pesheck, & Davenport, 2004). As a result federal and state governments have begun the process of trying to develop and implement integrated criminal justice systems that allow agencies to share information across organizational boundaries. Examples of such systems are Pennsylvania’s Justice Network (JNet), the Washington D.C. metro area’s Capital Wireless Integration Network (CapWIN), and the San Diego region’s Automated Regional Justice Information System (ARJIS) among others.
We find ICES s to be ideal opportunities to conduct social informatics research for three reasons. First, law enforcement is a socially complex domain comprised of and embedded in multiple social institutions (Sawyer, Tapia, Pesheck, & Davenport, 2004). Such institutions include organizational practice and culture, societal norms and values, and regulatory requirements. Second, law enforcement agencies have long been adopters of ICT to the point where ICT are now so ubiquitous that they are viewed as integral to policing (Hoey, 1998). This remains true in spite of a decidedly mixed record of success (Baird & Barksdale, 2003; Bureau of Justice Assistance, 2002). Third, the historical practice of ad hoc and siloed systems development suggests that law enforcement is an area where new systems development approaches are needed.