18
Oct 17

Is BIG DATA against of for low-income countries?

Goda, October 18.

The development of new ICTs has brought many changes into our day to day life. These technologies are often seen as being undoubtedly good with the recognised capacity to make the world better. Big data is one of the key elements of it. According to Spratt & Baker big data is the belief which offers new and higher knowledge ‘with the aura of truth, objectivity, and accuracy” (Spratt & Baker, 2015).

However, my last post will be focused on Unwin’s argument that even if the purpose and introduction of such technology has a potential to do good, quite often this potential has negative outcomes, especially for poor and marginalized communities. Moreover, although big data is seen as offering new solutions for development issues (Spratt & Baker, 2015), it is mainly focused on “what is”, rather than on “what should be” (Unwin, 2017).

Big data benefits and risks have been discussed in all our previous posts from many different perspectives and illustrated by using different examples. As mentioned earlier, it can be used for various decision-making models. It can create added value or be used for manipulative purposes. So, as data becomes all-pervasive in our lives, it is getting more difficult indeed to achieve a right balance between possibilities and dangers of it.

bid data lowincome countries

According to Unwin, big data is designed “with particular interests in mind, and unless poor people are prioritized in such design they will not be the net beneficiaries“ (Unwin, 2017). In other words, big data primarily maintains the interests of governments or shareholders and it is much less interested in the people, especially from low-income populations. Despite such issues, in the previous posts was clearly stated that governments still play inevitably important role in creating the legislative and policy framework.

This concluding post highlights the most important aspect of big data which should be taken into account. Technologies need to focus on empowerment of people, especially of people from less developed countries rather than controlling them.

Therefore, there is no doubt that big data has been used for reasonable purposes. However, it is difficult to decide if all of them are positive. The use of social media to provoke a certain political change can be seen as being good and bad at the same time. Big data can be an opportunity in various contexts as well as a problem that needs to be solved. Everything depends on the context, particular situation and particular human intervention (Unwin, 2017).

Moreover, in terms of the less developed countries, as the world becomes even more digitally connected, there is a real need for the sharing the knowledge and technical capacity by richer countries and international organizations in order to improve global digital security. While this can cause privacy issues, it needs to be discussed openly and transparently within countries especially if it is related to an equal decision-making towards the reduction of inequality.

Additionally, even if Unwin declares that much more attention needs to be paid to the balance of interests between the rich and the poor than to the ways through which data are used I agree with Read, Taithe and Mac Ginty that data to become explicit, requires a careful analysis in terms of how it is being gathered and used. Especially when the technology itself becomes cheaper and social networking platforms such as Facebook became mainstream forms of communication (Read, R., Taithe, B., Mac Ginty, R, 2016).

However, it is not just the access to technology that matters. The data revolution risks strengthening specialists in headquarters. Thus, not only access to connectivity needs to be provided, but also governments should be innovative and open to new ideas. Also, there should be integrated an appropriate content which should empower, integrate less developed countries and help to use big data for their own interests (Unwin, 2017).

Nevertheless, despite all the risks in terms of poor communities, there are many potential benefits of big data analysis also. Among other things, such information can offer more employment opportunities, transform health care delivery, and do even much more than that (slate.com, 2016).

Therefore, the capacity to meaningfully analyse big data still has the same importance as a balance between developed and developing countries (Rettberg, 2016).

 


12
Oct 17

(Big) Data for women’s empowerment? – How does it work?

Eraptis, October 12

In my last post, I asked the question about how data could be used in order to measure the impact of the #HeForShe movement on women’s empowerment and argued that theory could guide us in the interpretation of such data. But through logical deduction data must first be generated before it can be analyzed, how does it work when data is generated in practice?

In accordance with Morten Jerven, a basic point of departure when we want to know something about a population is to first establish what the population is. Only after we have established this can we know something about other properties affecting that population. From a development perspective factors such as economic growth, agricultural production, education and health measurements are all predicated upon population data to be meaningful. Many times, however, the definite (real) population number is not actually known but estimated through a population counting process commonly referred to as a census. Jerven illustrates the possible implications of census-taking in a development context through a case study from Nigeria, saying that:

“Today, we can only guess at the size of the total Nigerian population. In particular, very little is known about the population growth rate. The history of census-taking in Nigeria is an instructive example of the measurement problems that can arise in sub-Saharan Africa. It is also a powerful lens through which the legitimacy of the Nigerian colonial and postcolonial state can be observed” – Morten Jerven

Without us getting into the particulars of the Nigerian census-taking case, Jerven points out an interesting aspect of this quote which he elaborates further elsewhere in his book: the involvement of the state in the production of data and official statistics. Thus, argues Jerven, if a particular state is interested in achieving development, we should expect that it also has an interest in measuring (that particular) development. If that’s the case, then the availability of state-generated data should reflect its statistical priorities, which is likely to mirror its political priorities. We, therefore, once again, arrive at the question asked in my first blog post – is all data created equal?

Directing that question to Data2x seems to yield the simple answer “No”, at least not yet. Data2x is a joint initiative of the UN Foundation, the Bill & Melinda Gates Foundation, and the William & Flora Hewlett foundation dedicated to “improving the quality, availability, and use of gender data in order to make a practical difference in the lives of women and girls worldwide”. According to this report produced by Data2x, approximately eighty percent of countries produce sex-disaggregated data on education and training, labour force participation, and mortality. But only one third do the same on informal employment, unpaid work, and violence against women. Mapping the gender data gap across five development and women’s empowerment domains by using 28 indicators identified several types of gaps for each indicator as shown in this table:

big data womens empowerment

Source: Data2x

To close these gaps Data2x argues that existing data sources should be mined for sex-disaggregated data, and new data collection should be designed as a tool for social change that takes into account gender disaggregation already in the planning stages. But useful as they are, conventional data forms generated by household surveys, institutional records, and national economic accounts are not very well suited to capture a detailed account of the lives, experiences, and expressions of women and girls.

Can big data help close this gap? In this new report, Data2x shows it might by profiling a set of innovative approaches of harnessing big data to close the gender data gap even further. For example, have you ever wondered how data generated by 500 million daily tweets across 25,000 development keywords from 50 million Twitter users could be disaggregated by sex and location for analysis? I admit it, before reading the report; I cannot recall being struck by that thought. But, apparently, open data generated from social media platforms may not be sex-disaggregated from the outset. To solve this, the UN Global Pulse and the University of Leiden jointly collaborated together with Data2x to develop and test an algorithm inferring the sex of Twitter users. The tool takes into account a number of classifiers such as name and profile picture to determine the sex of the user producing a tweet and was developed so it could be applied on a global scale across a variety of languages. Comparing the gender classification results generated by the tool to that of a crowdsourced panel for which the correct results were assumed assessed the accuracy of the algorithm. In 74 % of the cases, the algorithm indicated the correct sex, a number that UN Global Pulse deems could be improved through further system development. The results of the project show great potential in generating new insights on development concerns disaggregated by both sex and location by using user-generated data from social media channels, as shown in this screenshot of the online dashboard (go and explore it for yourself!):

big data womens empowerment

Source: UN Global Pulse

Before ending this post I’d like to highlight three other relevant posts from our blog that takes up the relevant questions of bias in data generated from social media, the issue of privacy, as well as the geo-mapping and visualization of big data. Read, reflect, and tell us what you think in the comments field below!

 


05
Oct 17

Biased #data

Diana, October 5.

bias data

To continue my previous post, I’ll talk more about the biased data in this one.

Like I mentioned before, the Big Data is, unfortunately, not objective, but a human creation: Taylor and Schroeder accentuate that if we know the whole information on the matter, it can lead to the difficulty in understanding it and to the unwillingness to share it. Also, if we are not critical enough towards data we are receiving, we can buy false information as it is, without the evidence.

Big Data is everywhere. Big companies or “development professionals” such as the United Nations (UN) or Organisation for Economic Co-operation and Development (OECD) are using these types of data for research and exploration. Companies meet a lot of technical concerns on the way, like risks and issues of bias have tended to dominate the discussion so far.

Taylor and Schroeder point out the role of biased data in development politics. One example is how data is politicised, namely, that even correct data may not be accepted: all information has to be agreed upon in order to be useful to country authorities as support for policy decisions. Many undeveloped countries have that problem, where real information is hard to acquire. Officials censors all information that comes from sectors of the population who feel underrepresented.

bias data

Kate Crawford — a Principal Researcher at Microsoft Research New York City, a Visiting Professor at MIT’s Center for Civic Media and a Senior Fellow at NYU’s Information Law Institute, her research addresses the social impacts of big data and she’s currently writing a new book on data and power with Yale University Press — published an article in Harward Business Review: “The Hidden Biases in Big Data”.

Hidden biases in both the collection and analysis stages present considerable risks and are as important to the big-data equation as the numbers themselves. — Kate Crawford.

Kate takes up an example to explain the hidden bias in data. There was a lot of tweets about Hurricane Sandy, more than 20 million, between October 27 and November 1. A study shows that these data don’t represent the whole picture. The highest number of tweets about Sandy came from Manhattan: the city has a high level of smartphone ownership and Twitter use. On the other hand, it forms the illusion that Manhattan was the hub of the disaster. Not so many messages originated from affected locations, such as Breezy Point, Coney Island, Rockaway and even fewer tweets came from the worst-hit areas.

Here we can ask ourselves: how do the people outside of affected areas know about what is really happening there?

We rely more and more on Big Data’s numbers to speak for themselves, but we risk in misunderstanding the results and in turn misdirecting important public resources are as big as data itself. “Development professionals” do that mistake also, they rely on information without questioning it. All that misinformation can cause a wrong type of help to a wrong place or be an obstacle in aid relief.

Taylor and Schroeder take a similar example of biased data: the Big Data being used by “development professionals” in mobiles for tracking population movement in disaster relief. The problem with collecting this data is that it is not totally complete: not everyone uses mobile phones, with users particularly low amongst vulnerable and ‘hidden’ populations such as children, the elderly, the poorest and women.

As we move into an era in which personal devices are seen as proxies for public needs, we run the risk that already existing inequities will be further entrenched. Thus, with every big data set, we need to ask which people are excluded. Which places are less visible? What happens if you live in the shadow of big data sets? — Kate Crawford.

 


02
Oct 17

Have you #HeForShe’d yet? – Data for women’s empowerment?

Eraptis, October 2

As the third anniversary of the global #HeForShe solidarity movement for gender equality just passed us by on September 20, I decided to renew my commitment to ”he for she” (apparently, I had already committed to the campaign two years ago). But now that I’ve committed (again), what do I do? As I browsed further on the website I discovered their action kit specifically targeting students, I felt compelled to act, the results so far? A tweet and a blog post (starting small…):

Since its inception, the #HeForShe campaign, which is organized by UN Women, has gathered an impressive 1.5 million commitments, of which 1.2 million are from men, sparked over 1.3 billion gender equality actions and generated 1.1 thousand offline events and counting. Using “online, offline, and mobile phone technology to identify and activate advocates in every city, community, and village around the world” surely this must generate a large amount of data, possibly even “big data”, for analysis of the contribution of the movement towards UN Women’s core strategic pillars. This is important because achieving gender equality everywhere is absolutely crucial, and perhaps not the least so in communities and villages in developing countries. However, with this is mind, the question from my previous blog post echoed loudly in my head when I saw the global distribution of commitments on the interactive map on the #HeForShe site. Is all data created equal?

Photo: heforshe.org (accessed September 28, 2017)

Although it looks like the question “Have you #HeForShe’d yet?” is mostly a phenomena asked among “Western” men, there are also some beacons shining brighter in Magenta (the color symbolizing the movement) than others. In Rwanda, over 200,000 commitments have been made, of which over 160,000 are from men. While its neighbor Uganda have less than 1,000 commitments. Why this difference? Perhaps part of the answer spells Rwanda’s President Paul Kagame. But does Kagame’s role as an IMPACT Champion for #HeForShe help to better position his country to empower women? It could, for a number of reasons – some which could be extrapolated from his quote:

Women and men are equal in in terms of ability and dignity, and they should be equal in terms of opportunity. As Rwandans, as a global community, we need every member of our society to use his or her talents to the fullest. – Paul Kagame

Kagame’s words can be elaborated further in terms of power by using Naila Kabeer’s distinction of positive and negative agency. The interpretation of the first sentence of the quote could be that of limiting men’s power over women (negative meaning of agency) by clearly making the statement of equality in both ability and dignity, coupled with a vision of equality in opportunity. Whereas the second sentence is more directed towards the positive meaning of agency aimed at nurturing the power to pursue ones own choices and goals in life. The latter aspect can be further traced to Amartya Sen’s capabilities approach and the notion of development as freedom. Although these elements are primarily aimed at altering the power balance of individual agency between men and women, there is also a structural aspect related to the issue of women’s empowerment. In this second dimension, Kagame’s position in Rwandan society offers the opportunity to greatly influence the structural and institutional barriers hindering such a development.

How could data generated by #HeForShe be used in order to measure the impact of the movement on women’s empowerment both in terms of agency and structure in Rwandan society? Here, we enter the domain of theory. A good starting point would be to depart from Dorothea Kleine’s depiction of the choice framework.

In her framework, the combination of individual resources (agency) and the structural dimensions of a particular society determine the degrees of empowerment of individuals in that society, and whether or not they can identify the various degrees of choice and use it to achieve a set of development outcomes, of which choice itself is the primary outcome. But choice is complex. In her paper, Kabeer points out the difficulties of qualifying choice itself referring to the conditions (access or absence of viable alternatives) and the consequences (the degree of importance) of choice. Furthermore, Kabeer has also demonstrated the conceptual difficulties of using indicators in order to measure empowerment due to a very complex and dynamic interrelation between choice and access to resources, achievements, and agency. However, combining the development outcomes suggested by Kleine with Kabeer’s insights from her “reflections on the measurement of women’s empowerment” could provide a practical blueprint for how theory could be used in order to analyze the large amount of data generated by #HeForShe and determine its impact on women’s empowerment in Rwanda and elsewhere.

Thus, the point I try to make here is similar to that of my previous post that data is “facts and statistics collected together for reference or analysis”, but to make sense of it all we need to view the data through a theoretical lens. Do you agree? Let me hear your thoughts in the comment field below and let’s engage in dialogue!

 


30
Sep 17

Big Data: a Tool to Improve Local Governments

Aymen, September 30.

Nowadays, data is everywhere around us, from our Smartphone to our tablet, to that laptop or PC on our desks; data is pervasive and plentiful. Indeed, as reported by the Economist, “The world’s most valuable resource is no longer oil, but data”. Big Data may create risks with respect to rights, as surveillance opportunities are increased, and the growth in e-waste creates environmental risk, but it also generates a wealth of opportunities.

Continue reading →


29
Sep 17

Is a BIG DATA always a GOOD DATA or is it sometimes a MESS?

Goda, September 29.

dataAs it was discussed in the previous chapters, the big data has a potential to do many things and add value to major areas of our lives. So almost everyone is heavily involved in the big data. Moreover, one of the important aspects, reshaping the world today, is the worldwide accessibility to the Internet which has become incredibly broad. Internet live stats shows that approx. 40% of the entire population has an internet connection today. In 1995, it was less than 1%. The number of internet users has amazingly increased from 1999 to 2013.  The first billion was reached in 2005, the second billion in 2010, the third one in 2014 (Internetlivestats.com, 2017). While big data is concerned with all kinds of sources, it is estimated that the majority of it comes from unstructured sources and social media constitutes perhaps the biggest source of unstructured sources for big data including blogs, forums, social networking platforms, gaming and many other networks (The Statistics Portal, 2017).

Social media has become a key element in every society and culture, encouraging individuals to gather together on common interests and share opinions through the Internet. This has prompted the development of new big data approaches to capture, process and analyse large and complex data. New statistical methods and tools can process and examine such big data effectively at such a scale and speed that would have been unimaginable just a few years ago (Spratt & Baker, 2016).

However, to have lots of data doesn’t mean to have a good data. Surprisingly, the biggest long-term challenge of implementing and integrating big data isn’t technology – it is data governance and management or proper data collection and analysis (Read, Taithe & Mac Ginty, 2016).

When talking about data governance and management many articles concentrate on businesses to gain insights through analysis, to understand consumers’ behaviors by targeting products and services more effectively. Nevertheless, it has the same impact in politics too in terms of improving government decision-making and informing about their activities.

WHY? Because the DATA is the main element, not a technology, and implementation of the data into the whole picture is very important. Therefore, if the big data quality is POOR and INCORRECTLY analysed, proper integration of a big data can become one of the major problems.

EXAMPLE:

Nowadays, almost anyone can easily access public information from social media platforms such as Facebook or Twitter. Access to large amounts of data on millions of peoples’ activities and behaviors is an exceptionally good resource for every organisation by describing potential customer’s profile from the way how individuals naturally communicate online.

Therefore, recently there has been a lot of complaints due to the subjectivity in data because it was being used as a manipulation strategy by governments. Governments have faster, simpler methods to access the data. A large amount of articles talks about the company Cambridge Analytica which helped to manipulate both the US election and the EU referendum by using social networking platforms data in terms of how people show their opinions and preferences in social media and in terms of creating fake online profiles to give an impression that many  individuals support their certain political position (www.parliament.uk, 2017).

The  REASON of these problems is that governments can access data more accurately and quickly and by adjusting their power relations (Read, Taithe & Mac Ginty, 2016). Moreover, there are some discussions that social media data may not include vulnerable groups such as the elderly or groups with lower salaries. Therefore, there are big gaps in the data and there are no procedures yet for controlling these gaps.

Taking everything into consideration, you don’t necessarily need to examine a lot of data to get an accurate result, you just need to be sure you are analysing the RIGHT DATA. You should seek out new and diverse sources that can give you a well-rounded overview of the subject. Therefore, before making important decisions it is important to consider thoroughly all the information we have available, to reach a fair and just judgement.


26
Sep 17

Think About It – Is Bigger Better?

Diana, September 26.

Information and communication technology (ICT) haven’t even existed for some years ago. Now, it helps us to interact in the digital world. ICT gave way to Big Data revolution, namely, to all voluminous amount of structured and unstructured data which meant to be quarried with information. The amount of data that’s being created and stored on a global level is almost inconceivable, and it just keeps growing.

I challenge you to think about it one more time – is bigger really better? Let’s try to answer this question.

SAS Software

Taylor and Schroeder talk about that the development of data and technologies, as well as usage of those by people, have the potential to give the public a rich mine of information about health interventions, human mobility, conflict and violence, technology adoption, communication dynamics and economic behaviour.

The bigger data, the better: it allows us to perceive the environment in new ways. By having more information, we can do things that you couldn’t do before. We can collect information, share it, analyse it, learn from it and store it for years to come. Also, big data is a good tool to solve some of the world’s problems, like global food insecurity, medical care, energy and climate change.

Additionally, data and technologies bring together heterogeneous “development professionals“, such as donors, non-governmental organisations’ activists, government policy officers, consultants, academics, intended beneficiaries and so forth, who are active in various development aid organisations distributed all over the world.

In the data-driven world, the usage of data is also necessary. “Development professionals” are using data not only to promote and endorse development discourse but, as well, to save time. Big data accessibility and availability to useful information allows organizations to better understand the changing aspects of local field environments and, in turn, simplifies a better decision-making. Big data is a game changer if it is good, clean, accurate and transparent.

Nevertheless, what about the risks of losing data in the sea of all that informational overflow?

Taylor and Schroeder stress that bigger is not better, namely, there is an absence of good data. They lift up few drawbacks with “Bigger” Data. Data is not always simple and stable, namely, we need knowledge of how to use it. Most of the time, it is enough with some basic knowledge. It depends, of course, on what is the purpose of usage: a post on Facebook or managing a website.

Further, data can be bias. If we know the whole information on the matter, it can lead to the difficulty in understanding it and to the unwillingness to share it. The other risk here can be that we are not critical enough towards data we are receiving, namely, we buy it as it is, without the evidence.

Moreover, risks with an absence of the clear ethical framework, as well as rules for handling and sharing. The data revolution is so far mainly a technical one: the power of data to sort, categorise and intervene has not yet been clearly linked to a moral basis. In fact, while data-driven unfairness is evolving at exactly the same pace as data processing technologies, awareness and tools for fighting it are not.

Furthermore, anonymization techniques are unreliable. Data anonymization is the system intended to make it impossible to identify a particular individual from stored data related to him/her. Unfortunately, it doesn’t always work. One aspect of anonymization that worries individuals who value their privacy is that the process can be reversed.

The only way to stop big data from becoming big brother is to introduce privacy laws that protect the basic human rights online.”
― Arzak Khan

 


24
Sep 17

Thinking About Data – But What About Human Rights?

Louise, September 24.

As has been mentioned in previous posts, we are constantly reminded that we live in a world where information and communication technologies (ICT) not only affect our lives but to a large extent shape them. These new media technologies are rapidly advancing both in scope and scale. Some authors, like Unwin (2017:42), suggest that the notable expansion of new technologies such as mobile devices and new social media platforms emerged in an era of optimism about ICT as means to positive change for the poorest and most marginalised people in the world.

Take a social media platform like Facebook for example. On this platform, which remains the largest social network site in the world, more than two billion users have the possibility to every day share small or large fragments of their lives to the public. The information, the so-called actively generated data (Spratt & Baker, 2015:7), that is shared can range from how a one person likes her eggs in the morning to a large-scale mapping of human rights abuses in a repressive country.

human rights

Photo: Caucasus Business Week | 22 Jan 2015

In large volumes, the data that is generated is called big data – large-scale information that can be collected, analysed, distributed and, not least, used by a second party. Fellow authors to this blog have already raised several of the general challenges and opportunities of big data. It is to me inevitable that we all in one way or another enter that discussion as it serves as a base for analysis.

What I would like to add to the discussion is the element of human rights and, in particular, the worrying trend of shrinking of civic space. What are the possibilities and issues in the intersection between big data and human rights? In addition, I would like to spend some time drawing attention to how ICT as such can provide tools that in one way or another ensure that big data is collected and used in a secure way that goes in line with international human rights standards.

Thinking about data and human rights, I have realised that there is a question that should not pass by without further discussion; who is using the data, and for what purpose?

This is a question that I personally come in contact with every day at work. As an employee at a large international human rights organisation, I am constantly introduced to various forms of big data usage. It allows me to see elements of potential on the one hand, as well as how it can be used for pure repression on the other.

human rights

Photo: Human Rights Data Analysis Group

On the bright side, I see examples such as when the Kenyan organisation Ushahidi launched a platform that allowed the citizens of Kenya to map post-election violence in 2008. The technology has since been used in various aspects, such as for example mapping the destruction of the Haiti earthquake in 2010. I have also seen examples of small grassroots organisations in South Sudan coming together to encourage efforts of citizen journalism that have drawn attention widespread human rights abuses and extrajudicial killings in the country in recent years.

But then I have on the other hand seen first hand how large collections of data can be used by repressive regimes in countries such as Vietnam, Russia and Cuba. Here, the information is rather used by authoritarian governments to silence critical voices of independent journalists, human rights lawyers, students and human rights defenders. The trend, that governments are increasingly able to collect and use data, including personal data, may well be one of the greater threats to human rights in our modern and digital time.

The way I see it, big data can, on the one hand, be a tool of justice, but on the other a weapon of repression. And to a large account, this depends on in whose hands it is placed.

This first blog post of mine should be seen as an introduction to what is to come. In my next post, I will highlight some of the examples where data has in one way or another enhanced human right and global development. In my third post, I will revisit the danger of having the data placed in the hands of repressive state and non-state actors. In my fourth and final post, I will try and have a concluding discussion on whose responsibility it is to ensure that the data handled correctly as well as if there might be ICT tools that could be used in that very process.

I am looking forward to the journey. Do feel free to comment as we go along.

 


22
Sep 17

Thinking about #data (for development)?

Eraptis, September 22.

Thinking about data (for development) has really got me thinking. What is data, and what makes it “big”? How can it be used in the context of development, and which data really matters – is all data created equal? Blogging to reflect on these and other questions, sharing our thoughts with the (social media) world, might help in putting these things into perspective. It may even change how we view things. But then again, to who are we talking – who reads our blogs, and which blogs do we read ourselves?

Tobias Denskus and Andrea S. Papan have taken a closer look into the practices of blogging by development practitioners. In their paper, they interviewed a set of international development bloggers’ on their motivations for maintaining a blog. Although the motivations might be many, their research primarily points towards some individual reasons for maintaining a blog. Among the reasons stated were venting and reflecting on everyday occurrences in the professional life of the bloggers, putting them “out there” for others to engage with. The “Others” mainly comprising other development professionals sharing and consuming information as a way to stay in tune with the “hot topics” of the development sphere, and as a way to show one’s own expertise on the issues of the day. The networking aspect of it all, thus, seems to be an important motivation for blogging – leading to both virtual and real meetings. From an organizational perspective, these things certainly have the potential to create innovative ideas, and lead to improved processes – but there seems to be something missing. As the authors also point out, for blogging to be truly impactful, it needs to turn away from this inward tendency to also engage with local communities.

Why is this important? In my view, it potentially reflects a common practice of development discourse to view the means to an end as the end itself. ICT and data seem to be no different. In the opening chapter of his recent book, Tim Unwin argues exactly this point when writing:

All too often, ICT4D research and practice has been technologically driven, and has therefore tended to replicate existing social and economic structures, thereby failing sufficiently to explore, interpret, or change the very conditions that have given rise to them.” – Tim Unwin 

So – how to challenge this? Perhaps a good starting point is to look at the very definition of “data”, defined by the Oxford dictionaries as “facts and statistics collected together for reference or analysis”. Data, thus, is by its very nature positivistic. It can, at best, tell us what ”is”. But can data be normative? Can data tell us what ”should be”? As Spratt and Baker writes, the answer is not straight forward. While some would argue that if the data sets were just big enough theory as we know it would cease to exist, others point to such statements as hubris – dangerously overestimating the potential of technology to advance development.

Although I would tend to join ranks with the latter, I simultaneously share Unwin’s optimistic mindset that done right ICTs can indeed be a powerful medium for advancing development. But before that can happen we all need to engage in self-reflection and be critical about the appropriate use of ICT4D in order to consider all aspects of an implementation of data-driven development initiatives. At the heart of all this, Unwin argues, lies adopting a critical theory mindset. Among many things, this means doing away with the rather naïve and problematic notion that ICT, in and by itself, is good. And instead see it for what it is, one of many potential means to an end where the central aspect must be to understand the needs of those people for whom these kinds of interventions are intended. In the context of big data, this means working with and empowering those peoples to both generate and analyze this kind of information for themselves.

Before ending this post I would like to leave you with a thought related to one of my initial questions – is all data created equal? Well, Spratt and Baker note that big data analysis, particularly in the context of social media, is heavily biased towards information in the English language. If data is “facts and statistics collected together for reference or analysis”, and most data analyzed is in English, then who’s voices are we really listening to?

Featured image: Eventfinda

 


18
Sep 17

What is BIG DATA and why it can be dangerous?

Goda, September 18.

big dataOver the last few decades, information communication technologies (ICT) have developed gradually and brought many changes to global and international development.  The rise of the social media and evolution of big data are one of them. In order to talk about big data in the context of social media, we have to understand what big data means.

So let’s imagine the time when there were no computers yet and all the information was stored in written sources – books, letters and etc. Let’s imagine that we have so many of these sources that we have to open a huge library that would help anyone to find any small text or photo they need. This means that library must have a clear system and the ways to find any information quickly.

The same thing happened with the digital data. However, the amount of it is more than enormous comparing to written sources. Some time ago this process of handling and storing information was covered by simple databases, but when practically everything that goes on in our society moved into a virtual space, all the impressive amount of information needed to have impressive programs. This is where the term “Big Data” came from.

Many authors compare big data with the fuel that drives the next industrial revolution into every aspect of economic and social life (Tufekci, 2017). Soon, particularly in developing countries, big data will be able to help to improve even areas such as government decision-making, implementation of social welfare programs or scientific research.

There are lot’s great benefits associated with big data. However, big risks are also widely discussed. The three main big risks can be the following:

  • Quantity and the quality do not match. Having a large amount of data does not necessarily mean having a high-quality data. For example, many of the developments in digital humanitarianism are based on what is possible rather than what is needed. More and more money is being invested in developing these technologies but their use is often limited.
  • Moreover, everything can be tracked – how, why and by whom information is collected, stored and processed. Social media, music or videos have all been stored as a data that has become available for analysis. For example, Facebook always can show where you are or when was the last time when you were online.
  • Furthermore, it can cause big privacy problems. Data protection online is a significant and increasingly urgent challenge especially in the previously mentioned networking platforms and many others, such as Facebook, Twitter, WhatsApp, Uber or AirBnB which have shown how huge amount of data can be achieved in the short period of time and, for example, how sharing one post on Facebook can make a difference.

Taking everything into consideration, it seems that big data will become inevitable everywhere. Nevertheless, despite its benefits, the three examples of the main risks of big data mentioned above are currently seen as very important challenges in the international development field which will be discussed in more detail later.

Thus, even if a huge amount of money is being invested in developing and improving digital technologies, their use is still quite limited. Also, these are the early days of the data revolution and many uses of it still remain unforeseen. Moreover, human impact how information is gathered and used needs to be considered too.

Therefore, thinking about the potential of big data, it requires more consideration about information gathering and processing especially in the light of accuracy, risk evaluation and assessment (Meier, 2015).