12
Nov 16

Wearables, big data and traffic regulation

Wearables and traffic. Photo and drawing by K.Tatakis, November 2016

By: K. Tatakis

Big data could help citizens in numerous ways and it’s up to our phantasy to invent new uses. In this short post I will work on the subject of wearables, big data and traffic regulation, something very useful globally as urbanization advances rapidly at a global scale. Wearables with GPS geo-localization that will count the number of citizens waiting in a metro station platform could be meaningful to reduce human stress and delays. Such delays are the reason for many missing meetings as employees (and even employers) arrive often late at work due to such disturbances. Big data could give traffic controllers the opportunity to augment the frequency of metros’ in a particular metro rail line until demand from passengers to enter wagons drops. But it is not only trains and subways where such an idea could be used.

It could be used to count passengers in a particular airport or a particular airport desk, to count drivers willing to take a turn towards a big highway and so on (especially when connected with route plans that people have picked in their digital traffic advisor). That will resolve many of today’s problems citizens daily have in large urban areas all over the world, from Shanghai to New York.

A particular scan of data could even calculate the number of social media messages about high traffic in a particular area and thus to help who organizes the traffic to send more buses, taxis or trains to relieve a problem. In such a case an alert level could be set, when for example messages about traffic are more than 50 in a small neighborhood.

In a less developed country where people don’t have enough money to buy these new gadgets, SMS could be traced in order to inform if particular products are available in a small local market of a poor African country. Linnet Taylor and Ralph Schroeder open up very interesting questions about big data in one of their recent articles. They inform about the MIT’s “Billion Prices Project (Cavallo 2013 in Taylor and Schroeder 2015, p.504) which is a similar idea but at a much larger scale.

Will citizens be more willing to provide such data if they can immediately use the outputs of such a survey? I think yes. Where positive results of an action are immediate and more tangible, people will feed such initiatives with more data for their own good.

Many companies that provide GPS orientation programs provide updated information about traffic in big cities. What if all these could be transformed in an all – included program which will combine road, train and air traffic and that will really inform about traffic jams in order to avoid delays. Buying tickets and the opportunity to see tickets availability would make such a program far more desirable.

A big fear of today’s citizen in a big metropolitan area is sudden traffic chaos. I think that people are ready to accept less privacy when outcomes really help them immediately towards a better life quality.  Αfter all improving our lives is  a form of social change.

 

 

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Social media, big data and development (Drawing by K. Tatakis, 2016)

A critical question is where data scan and elaboration will be done (Taylor & Schroeder, 2015, p.504). Let’s suppose that a heavy traffic jam hits Mombasa. Are local authorities ready to use such technologies or this will add new costs to the local economy, as technologies and scientists will have to be imported? The two scientists also inform about the dangers of a “top-down approach” ((Taylor & Schroeder, 2015, p.504), where questions (of data surveys) and needs are simply guided by officials both in national and international organizations and are not necessarily what people ask about.

Talking about the digital ethics and the digital concerns over big data was something very trendy during the last ten years. Can we talk about politicization of data in such a field, in a way similar to the one Taylor & Schroeder discuss about over other development issues (Taylor & Schroeder, 2015, p.506)?  My answer is yes, as decisions that will follow the use of these data (the decisions of traffic controllers for example will affect the lives of thousands of people). After all it is a political judgement what you consider high traffic in your polis (city) and the decision you make to inform others on social media. Speculation over the use of traffic control data could affect the elections in a large metropolitan area, if a candidate will blame the current mayor that she/he is not doing enough to press for more metro’s wagons in a particular line. But it will also affect private companies’ profits, positively or negatively.

Reviewing Taylor’s and Schroeder’s article, I can notice that one of the most important questions they pose is “what ‘development’ means to data scientists” (Taylor & Schroeder, 2015, p.508). How were they educated on development (both in class and from the environment they have lived) and how that will influence their choices?

They also pay attention to the fact that not all scientists gain allowance to use big data. They need prior reputation and good connections in order to gain such a permission. Thus only a small circle of scientists could end up on using big data (since big data is not always open data) and the rest will be constrained to do what is a small scale data research or a qualitative research(Taylor & Schroeder, 2015, p.510). The two scientists stay on “power and knowledge asymmetries” (Taylor & Schroeder, 2015, p.516) and insist on education of people about their rights (Taylor & Schroeder, 2015, p.516). When entering what I would call this new marvelous world, the world of big data, you have to take account of such complexities.

 

References

Taylor, Linnet & Schroeder, Ralph (2015). Is bigger better? The emergence of big data as a tool for international development policy, GeoJournal (2015) 80:503-518

 

 


10
Nov 16

Italy: Earthquakes create discussion and innovation

By: K. Tatakis

Earthquakes create notoriously big crises. When such natural phenomena suddenly strike communities causing fear and despair, humans try to find ways to be more helpful and effective. Earthquakes trigger solidarity and work as a stimulus for innovative ventures. Human energy comes out of the rumbles.

A big earthquake hit on August 24 the regions of Marche, Umbria and Abruzzo in Central Italy. Little townships like Amatrice and Accumoli were heavily devastated. Such regions were historically struck by earthquakes again and again during the last 5 centuries, but loss of lives was this time quiet high, compared to similar phenomena to other parts of Europe during 21st century, as many of the buildings were medieval and old stone structures.

Italy’s earthquake happened approximately at 3 AM local time and Twitter was a source of information particularly helpful to night- shift journalists. From the very first moment appeals to free Wi-Fi networks and help who suddenly stayed homeless in the middle of the road or had to search for missing persons were made in Italian social media.

Some insisted traditional during crisis

But initial calls for rescue generally happened the more traditional way.  Calling the emergency numbers of the Italian Firefighters, State Police or the Carabinieri. Calling for rescue via e-mails or digital apps was not the norm in the Italian case.  Despite this country is a member of the G8 and technologically far more advanced than Haiti, people preferred to make their calls mostly by phone and not via social media. Writing short messages like Meier suggested in the case of 2010 earthquake in Haiti was not very easy-going for someone desperately urging out of home, in the middle of the night. In most cases cellular phones and other more valuable items where simply left behind in an agonizing attempt of people to save their lives into the dark. An exception could be the case of a nun living at the Religious Institute Don Minozzi in Amatrice. She appeared on RAI News 24 TV Channel the days immediately after August 24 explaining how she sent SMS when living under the rumbles. But her case was not a call for help, rather than a “talk” of a person ready to die with her most beloved, sending ad Dio (to God) messages.

Source: Ministero dell’ Interno / Vigili del Fuoco (Italian Ministry of Interior / Italian Firefighters) https://www.youtube.com/watch?v=A_pH6nbQMFY

On the other hand people turned massively on Social media to inform and discuss about the earthquake and to talk about reconstruction and solidarity initiatives. Social media (mainly Twitter and Facebook) were the absolute protagonists in that sense. Citizen’s involvement in social media, this time locals and not mostly foreigners as Lillie Houliaraki (2013, p.273) noted for the case of Haiti’s 2010 earthquake was much more evident in the Italian case. Campaigns for collecting money and blood but also to inform people on what to do during an earthquake started in a few hours. Facebook also activated Safety Check in order to find missing persons.

In his recent book Patrick Meier informs of a network that he and his peers gradually built in Boston in order to help Haiti hit by the 2010’s earthquake They were based on some of the most innovative technologies and the help of volunteers all over the world(Meier, 2015,p.5).

Meier’s positivistic view over big data is not at all unjustifiable. Big data can help in emergencies situations, but more importantly on monitoring health issues. In his book Meier’s suggests to enable GPS localization in our digital devices in order to be located during an emergency (Meier, 2015, p.175). In the Italian example, at least when looking at the initial earthquake of August 24, it is difficult to find cases that people where traced under the rumbles thanks to their GPS signals. There are two main explanations for that. The time that the earthquake happened (too early to carry a mobile phone with you, unless you slept with one in your pocket), but also the desire that often people have in more advanced societies to live under anonymity. In other words reluctance to share such personal data in big networks.

Meier supports the idea of “microtasking” (Meier, 2015, p.65) sharing small missions with volunteers in order to act faster and to save more people during a disaster. Such a task may sometimes require educated people with basic technological knowledges, but it is a meaningful idea over social change. According to Meier such a microtasking helps to enable better validation of sources, which is a real headache for people working on the social media. More eyes and more brains can check better what is originally published on the web (Meier, 2015, p.65-66). Similar projects are heavily based on “consensus” (Meier, 2015, p.68). People have to believe that big data is the next big thing and to share more data, in order to make the stats work for social change.

Transparency and big data

Transparency is another hot topic. All should contribute on giving data. But the next point is why not to make these data available to all? In one of his recent books about communication Manuel Castells underlines as an exchange, a reciprocal flow which is based to “interaction” (Castells, 2011, p. 55). How much of these data should be open and where it is advisable to be protected for reasons of personal privacy or security? That’s an ongoing discussion that will last for the whole century in my opinion, as it is a very complex matter.

Who will check the use of huge databases, once data often collected from private companies for profit? Will be any regulators to control such a use or the whole sector we will be left to the self-regulation of large private (or even state) entities for the common good?

Thoughts over data use transparency are in this sense a little limited in Meier’s book. The author prefers instead to develop more the concept of ‘democratization” of data (Meier, 2015, p.187) which is similar to data transparency, but not identical. Transparency over the use of data, but mainly over the control of data is what will be a cardinal point of conflict in the 21st century. Who will get control of these data will have a new soft but enormous tool of power, whether private or state entity… May be that’s inevitable, once you need huge amounts of money, resources and persons to carry over such a digital revolution. But politics over the use of these data should be clean and clear and citizens must have their say. Education over big data will also be cardinal in order to take the most positive things out of them. Citizens, companies, researchers and states should decide together about the future.

Overall Meier’s book is a must read for someone wanting to work with big data for humanitarian purposes and opens up many new windows of thought that will ferment new ideas in the future.  Stats and mathematics are better and more reliable than emotions as guide to who really needs help in an emergency situation (Meier, 2015, p.47). And it’s up to who will take decisions to make the best use of them.

 

References

 

Castells, Manuel (2011). Communication power, Oxford – New York: Oxford University Press.

Houliaraki, Lillie (2013). RE-MEDIATION, INTER-MEDIATION, TRANSMEDIATION, Journalism Studies, 14:2, 267-283

Meier, Patrick (2015). Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response, Boca Raton: CRC Press.

NOTE: Product or corporate names may be registered trademarks or trademarks and are used only for identification and explanation without any intention to infringe

 

 


08
Nov 16

Big Data and Anecdotal Evidence

Shahin Madjidian

This last blog post of mine will discuss the paradox between the growth of both scientific big data and emotional anecdotes. There seems to be a tug of war between on the one hand big data and on the other hand anecdotes and emotions (Aday, Farrell & Lynch 2010:5). How do they compare and interact?

On the one hand we have big data, science, facts, and numbers, as has previously been described and analyzed. On the other hand we have anecdotal evidence, conclusions many times based on emotions. Both grow in impact and spread as social media and internet connectivity grows. Is this a paradox? How can it be explained?

Explaining big data is fairly obvious. The more we use social media and various sites online, the more tracks we leave behind us, ready to be collected, compiled, analyzed and used. Big data today is usually defined as a compilation of fragmented data from many users, but it is hardly revolutionary or thought-provoking to suggest that soon all kinds of data stemming from one individual will be collected and analyzed, making the individual one big emitter of big data itself.

But in this day and age of facts, numbers, and data in each and every corner, how come there are so many people believing in anecdotes, the experiences of others, and the rule of emotions? I believe that this can be traced back to the notion of ‘slacktivists’ and the power of numbers, combined with the still new feeling that “hey, there are others like me out there!”. As Barberá et al. state it, “by expanding the audience of messages sent by the committed minority, the periphery can amplify the core voices and actions, and thus provide a way for larger numbers of online citizens to be exposed to news and information about the protest” (Barberá et al. 2015:11).

Then US Secretary of State Hillary Clinton said about new media technologies that they open plenty of doors, but that there are dark ways it can be used too (Aday, Farrell & Lynch 2010:5). Even though she probably didn’t refer to the anecdotes I discuss here, her point that new media and social media isn’t something inherently good is still valid. For me, one clear danger is how posts, rumours, anecdotes, and sometimes even pure lies, can go viral and become the truth of the day thanks to how easy information can be shared and spread, especially within sub-groups.

One such consequence becomes related to social capital, more especially to the potential growth of bonding social capital. Bonding social capital is when the level of trust between members of a sub-group is high, but the level of trust to outsiders is substantially lower, as opposed to bridging social capital, which refers to high levels of trust between members of two or more sub-groups. Does access to new media bond individual groups tighter together, creating polarization, or does it bridge groups together, creating more understanding and fostering “cross-community communication” (Aday, Farrell & Lynch 2010:10)?

As we humans are pack animals, we tend to be attracted to likeminded people so that our ideas, traditions and prejudices can be confirmed, boosting our self-confidence of being right. I don’t want to qualify this challenge for societies as the most important challenge to deal with, but I do believe that it is of vital importance to continuously work towards spaces where interaction between sub-groups is both possible and required. It is only in the meeting and conversation lies and prejudices can be shattered.

So, “social media may reduce the transaction costs for organizing collective action, by facilitating communication and coordination across both physical and social distance” (Aday, Farrell & Lynch 2010:10f), which in turn may lead to an increase in bonding social capital and subsequent polarization in society, as humans gravitate towards those who share their beliefs and have the same experiences, resulting in an increase in anecdotal evidence based on emotions.

REFERENCES

Aday, S., Farrell, H., Lynch, M. et al. 2010: Blogs and Bullets: New Media in Contentious Politics, Washington, DC: United States Institute of Peace.

Barberá P., Wang N., Bonneau R., Jost J.T., Nagler J., Tucker J., et al. 2015: The Critical Periphery in the Growth of Social Protests. PLoS ONE 10(11): e0143611.


08
Nov 16

Digital Humanitarians

Shahin Madjidian

The previous posts have been somewhat negative of what big data can accomplish and the effects it may have on privacy and democracy. But are there no positive sides to it? It turns out there is!

Patrick Meier is the author of the book Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response. In the book, he lays out a large number of examples of how he and his colleagues and friends have developed and led digital humanitarian relief and aid efforts during catastrophes with the help of big data.

It all started with the earthquake that hit Haiti in 2010. Meier’s interest in this event was sparked by the fact that his partner currently worked in the country, so he had a very personal reason for starting the digital relief effort. I think this may have been one of the main reasons why Meier continued to work with and develop digital humanitarian efforts later on – his emotional attachment to what could have been, had his partner not made it out of there.

What started as a small-time operation in a campus dorm quickly grew to something that even attracted national and international agencies. Meier himself was surprised, not only by what they were able to accomplish, but also the fact that what they did actually was possible for a beginners group like them, without any previous knowledge or expertise in the humanitarian field.

As the book progresses, Meier’s work and tools develop as he and his colleagues face scenarios with unique challenges. It is about automation in order to handle to huge amount of data that flowed in, it is about streamlining the organization, it is about educating the digital volunteers, and much more.

But the one thing that keeps returning, regardless if it is about Meier’s own efforts or the efforts of others, which the book also presents, is the importance of involving people with local knowledge. “Locals” can identify buildings, landmarks, streets and therefore become a great asset during the analysis part of the digital humanitarian effort. The “locals” also speak the language where a catastrophe has taken place, which for the most part is not English. Without this possibility to translate aid requests, Meier and his team would never be able to figure out which need was needed where.

By constantly returning to this aspect, Meier confirms the notion that big data requires local knowledge in order to properly be analyzed and used.

Another thing that constantly returned throughout the book was how smooth it was for Meier to solve the challenges that kept popping up. He always knew the right person at the right universities or agencies, which he had met during this or that conference, who knew exactly how to deal with an unexpected issue that was unique to a certain humanitarian crisis. After a few chapters it almost became absurd. It is absolutely amazing that Meier has this kind of network, but in all honesty, how many have so many great contacts? Obviously, this brings us back to the question of who big data is useful for, and who is able to handle big data.

Lastly, Meier makes sure to note that all his projects and programs are freely available. This also sounds good, but is he the future norm of big data, or rather the exception? Going back to my first blog post about ownership, I dare say Meier seems to be the grand exception, at least if status quo is kept. However, he does offer a refreshing alternative which I would hope more actors within the big data field will emulate sooner rather than later.

All in all, this book gave me a different perspective to big data and how it can be used to do good things and not only for commercial purposes. I did feel somewhat more positive writing this post compared to the previous ones. But exactly because the perspective was so different, my negative opinion of big data presented in the first two blog posts wasn’t changed much by the book. Meier doesn’t discuss privacy issue much and when he does, he believes that the ends justify the means. A critical view of ownership of big data is non-existent and there is neither a deep discussion on social media users related to power and income questions.

REFERENCES

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.


01
Nov 16

Big data in an African context

Shahin Madjidian

Introduction

Many development goals, policies and programs are based on numbers and statistics. How accurate are these numbers on the African continent and can big data help in improving the accuracy?

African statistics today

In his book, Jerven offers a devastating critique over the state of statistics on the African continent. He notes that the numbers being produced and published are neither reliable nor valid, often being based on estimates, guesswork and/or assumptions. Many times, these assumptions are in turn based on older data sometimes dating back decades. These old baseline numbers have very little relevance with how things look today.

Several consequences can be identified here. Firstly, different actors may look at the same old data, but through different angles, and thus produce very different numbers (in Jerven’s case it is mostly about GDP/capita). Secondly, these poor numbers feed into a larger picture of how African countries are depicted, which problems they have and where these can be found, and any possible solutions to remedy them, to develop the nations.

Jerven laments the poor state of the countries’ statistical offices and argues that they are basically there to serve actors from the international aid, donor and development communities (Jerven 2013:105). “International institutions are the main providers and disseminators”, as he notes (Jerven 2013:8f).

Jerven calls for new baseline estimates, from which fresh statistics can be extrapolated and drawn from. However, he stresses that “these must be based on local applicability, not solely on theoretical or political preference” (Jerven 2013:xiii) and also highlights the importance and necessity of local knowledge and input. Data and statistics ought to serve the needs of the people on the ground, not reaching targets for some faraway aid organization.

Big data replacing statistics?

Can big data replace the poor state of statistics on the African continent and help improve public policy and development goals? First, let us quickly go through what big data is and how it works, before answering the question.

In their book, Mayer-Schönberger & Cukier provide us with a clear overview of big data and what it is. They note that “at its core, big data is about predictions” (Mayer-Schönberger & Cukier 2013:11), about inferring probabilities. Furthermore, big data is about finding the general direction, about a trade-off between being accurate at the micro level versus gaining insights at the macro level (Mayer-Schönberger & Cukier 2013:12f). So far, big data seems like a useful tool to use. In fact, big data can be viewed as pure statistics. But which data can be and is currently collected in big data sets?

A lot of the data comes from using various communication tools, such as cell phones and computers, while simultaneously being connected to the Internet. Taylor & Schroeder warn us when they point out that far from everyone use cell phones or is connected to the Internet in developing countries. This results in user bias and a situation where vulnerable or ‘hidden’ populations, such as children, the elderly and the poorest in society are left out in the data collection (Taylor & Schroeder 2015:510f). They argue that “mobile phone use is highly differentiated by gender and income level” in India (Taylor & Schroeder 2016:506), and a qualified guess is that many African countries exhibit the same patterns.

Meier concurs, saying that “not everyone is on social media. In fact, social media users tend to represent a very distinct demographic, one that is younger, more urban, and more affluent than the norm” (Meier 2015:37). So perhaps inferring national probabilities from a rather narrow subset of the population is a fairly poor idea, which will not give a rewarding big picture, as is one of big data’s positive sides.

Quality of analysis

If the previous section discussed the quality of data, this will delve deeper into the quality of analysis regarding big data. In the previous post I briefly mentioned how big data actors mostly are big corporations and governments. What they have in common is that the majority of them are based in the global North, far away from the realities of Africa.

Jerven writes: “In order to employ the evidence usefully, one must know the conditions under which the data were produced. This is readily recognized in qualitative analysis, but somehow these principles have not been applied to quantitative evidence” (Jerven 2013:7).

Read, Taithe & MacGinty are even more pessimistic and question the quality, reliability and validity of data when “field level information may be sent to headquarters in a different country, collated with other data and then sent back to the country of operation” (Read, Taithe & MacGinty 2016:7). They continue saying that there is a risk where people analyzing the data are cut-off from local knowledge and context, only looking at numbers (Read, Taithe & MacGinty 2016:12).

Mayer-Schönberger & Cukier in turn touch upon the very real possibility of a situation where “data-driven decisions are poised to augment or overrule human judgment” (Mayer-Schönberger & Cukier 2013:141). Let us hand over everything to the machines!

Big data the statistical saviour?

Based on the literature reviewed here, this question can only be answered with a resounding no. Jerven complained about the dominance of outsiders when producing statistics and I cannot see how things would be any different if big data actors were to run the show instead of today’s powerhouses within the statistical field. The same objections, such as democratic deficit and out-of-touch with local circumstances, can be raised and more, such as the gender and income gap among users, may even be added.

Big data proponents argue that big data “offers new and higher knowledge ‘with the aura of truth, objectivity, and accuracy’” (Read, Taithe & MacGinty 2016:10). But statistics, be it presented as big data or traditional surveys carried out on the ground, is always subjected to human bias. This is actually something that Meier, himself a big proponent of big data, confirms when he says that everything is biased (Meier 2015:39).

REFERENCES

Jerven, M. 2013: Poor Numbers: How We Are Misled By African Development Statistics and What To Do About it. Ithaca, NY: Cornell University Press.

Mayer-Schönberger, V., Cukier, K. 2013: Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray Publishers.

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.

Read, R., Taithe, B., MacGinty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming.

Taylor L, Schroeder R. 2015: Is bigger better? The emergence of big data as tool for international development policy. GeoJournal 80: 503-528.


01
Nov 16

Big data, privacy and ownership

Shahin Madjidian

Introduction

Studying big data critically leads to several interesting topics which can be examined and developed. In a string of four blog posts this is exactly what I will do. The first post is perhaps the heaviest as it deals with ownership issues, democracy and whether or not big data can be seen as something revolutionary that will lead to social change.

I will start with a short analysis of the individual’s right to his/her data and then move on to the macro level – who owns the data, who can store it, analyze it or draw conclusions from it, and later act on them?

Privacy

Today, most people in the world leave digital fingerprints as we go on about our businesses, whether we like it or not. This data gets stored and many times analyzed and acted upon by the actors who picked up the data in the first place. The data can be anonymized and used in a big data set where it is very difficult, or even outright impossible, to identify individuals, or it can be used to improve targeted commercial ads suited for a unique individual.

Privacy issues have been raised, especially as individuals have very few possibilities to reject the data collection. Spratt & Baker argue that algorithm transparency is important, as well as the fact that people should be allowed to know which data is stored on them and where. They suggest that “all individuals have the right to control their own personal data, and can choose to sell as much or as little of this as they like” (Spratt & Baker 2016:30). While this sounds laudable, there are several problems which are ignored. How will an individual get access to this data and where can s/he store it? What about situations when the individual requires money or are in other desperate situations and decide to sell data, doing a trade-off between short-term gain in favour of perhaps long-term exposure? Which population sectors in which countries may be most prone to do this?

In reality, as Spratt & Baker note, consumers may object to their personal data being bought and sold, but in reality have very little control over it once it has been collected (Spratt & Baker 2016:12). This leads us to the next section, namely who these holders of data are.

Ownership and “usership”

The main holders and owners of data today are big corporations, especially those in the social media and communication sectors, and government. Often, data collected from actions and events are used to create new forms of value in innovative ways, as “the system takes information generated for one purpose and re-uses it for another” (Mayer & Schönberger 2013:97, 103). The trick is in finding secondary usage of the data and as a result, hidden correlations which may turn out to be highly valuable. Related to the privacy issue, it is very difficult to prohibit something that has not yet happened, to prohibit uses of data which the data owners have not previously thought about.

Read et al. argue that “if the power of initiative, design, funding and analysis still resides with the tech-savvy individuals and organisations based in the global North, then it is difficult to concur with the view that technology is empowering or liberating” (Read, Taithe & MacGinty 2016:12).  This notion is amplified in the global South considering “the growth of private-sector involvement in public infrastructure projects across the globe” (Lovink & Zehle 2005:10), with infrastructure here broadly meaning Internet and cellphone development.

A few huge corporations have taken the lead in the use of big data and to remedy this, Spratt & Baker propose state support to startup companies within the field in order to learn and become more competitive (Spratt & Baker 2016:26).

Are there any possibilities of individuals becoming owners, analyzers and users of big data? Meier certainly believes so, and I will return to his book “Digital humanitarians” in a later post. For now, I will use his own words against him, as he writes that big data can easily turn into information overload and that the data coming in during one of his humanitarian efforts was simply too much for him and his hundreds of volunteer to handle (Meier 2015:4, 50, 52).

It is not only the vast amount of data that makes it difficult or impossible for individuals to use, but also its messiness and complexity. The data comes from different sources, in a wide variety of shape and form, many times unclear and fragmented. The technologies required means that big data use today is limited to a few actors. Individuals, or groups of individuals, are usually not among the lucky ones.

Mayer & Schönberger have a somewhat romantic view of the future development, believing that just like everyone with cell phones has the potential of being a “journalist” in the broad sense, everyone may be able to extract and analyze big data as “tools get better and easier to use” (Mayer & Schönberger 2013:134). It may not be necessary to be a statistician, engineer or software developer working for a government agency or Facebook.

Conclusion

While development may allow more people to become big data users, today’s actors will have a huge head start. Furthermore, Mayer & Schönberger predict that data owners will increasingly be in the most lucrative position in the future (Mayer & Schönberger 2013:134) and as long as the privacy laws are not changed, the data owners will be social media and communication corporations, not individual citizens.

I agree with the somewhat glum view of “although cloaked in an the language of empowerment, data technology may be based on an ersatz participative logic in which local communities feed data into the machine /…/ but have little leverage on the design or deployment of the technology” (Read, Taithe & MacGinty 2016:11).

In many ways, big data is revolutionary and holds great possibilities for humankind, but used within today’s societal and economic logic, it is but a furthering and strengthening of the status quo, with little to none possibility of empowering individuals or inciting social change.

REFERENCES

Lovink, G. & Zehle, S. (eds.) 2005: The Incommunicado Reader. Amsterdam: Institute of Network Cultures

Mayer-Schönberger, V., Cukier, K. 2013: Big Data: A Revolution That Will Transform How We Live, Work, and Think. London: John Murray Publishers.

Meier, P. 2015: Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response. Boca Raton, FL: CRC Press.

Read, R., Taithe, B., MacGinty, R. 2016: Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly, forthcoming

Spratt, S, & Baker, J. 2016: Big Data and International Development: Impacts, Scenarios and Policy Options. Brighton: IDS.