Oct 17

Can Big Data Help Feeding The World?

Aymen, October 20.

Big Data goes beyond just the existence of data. The ability of Big Data techniques to generate insights through synthesizing data from a range of sources may hold the greatest potential and carry the greatest risks of all. On one hand, Spratt and Baker, in their report “Big Data and International Development: Impacts, Scenarios and Policy Options”, explain that Big Data can be manipulated to promote certain political agenda or increase the possibilities for governments and large corporations to discriminate against certain groups or individuals.

big data feeding world

On the other hand, Big Data may have a positive environmental impact as well as a great potential in agriculture and rural development. It can bring new insights and decision points that lead to product/service innovations. This potential touch on, for example, precision farming with very efficient water and fertilizer use, food security coordination through tracking, tracing and transparency and personalized health and nutrition advice. The availability of easily accessible data plays a major role in documenting quality standards of agricultural products, saving time and improving productivity.

Several projects launched by development organizations rely on Big Data to optimize agriculture. For example, FAO launched in more than 10 countries in Africa, Asia, Eastern Europe, Latin America and Near East the Virtual Extension and Research Communication Network (VERCON). According to FAO, VERCON is a conceptual model that employs internet-based technologies and Communication for Development methodologies to facilitate networking, knowledge sharing and interaction among agricultural institutions, producer organization and other actors of the agricultural innovation system.

In Egypt for instance, where the first project was launched in early 2000, 100 VERCON access points had been installed in various places, such as extension units, agricultural directorates, research institutions and stations, and Development Support Communication Centers. They were connected to the internet to allow farmers to access to an agricultural economic database as well as news and bulletins that help them in solving their problems. In addition, the platform was useful to share ideas and experience of local farmers and monitor the whole project.

The VERCON project was successful since it relied on existing organizational structures and links. Also, the platform ensured rapid response to user feedback thanks to regular monitoring and access to monitoring results. It used rural and agricultural appraisals at the field level to ensure that the virtual network would be accurately focused on the information and knowledge needs of the larger agricultural community.

The project was successful and the Rural and Agricultural Development Communication Network (RADCON) was set up to engage with a wider range of rural and agricultural development issues and to extend the VERCON network to a wider range of stakeholders, including farmer organizations, youth centres, universities, and NGOs.

However, the challenge that the project must take is the use of ICTs by farmers themselves.  Despite the success of projects that imply Big Data for rural development, developing world-based farmers often face difficulties in meeting the quality and safety standards set by the developed world. The conditions that stimulated the growth of Big Data in the farming industry in the global north such as the widespread adoption of mechanized tractors; genetically modified seeds, computers, and tablets for farming activities are less prevalent in developing countries. While large growers can afford specialized machinery, small farmers do not have this opportunity. As a result, they can neither access the data nor interpret it.

Big Data for rural development can help analyzing large amounts of information related to rainfall data or the pest vector could give valuable insights into important issues such as climate change, weather patterns and disease and pest infestation patterns. However, this valuable information largely benefits the Big Data industry in the Global North. It can have a positive impact on big farmers in the global south, but rural communities might be excluded as they still have little or no access to ICTs.

Nowadays, as evoked by Spratt and Baker, those who are in favour of Big Data adopt an evangelical tone to argue for its benefits; while those who are against it tend to stress its dystopian nature. It is important to remember that Big Data is a very recent phenomenon; according to sciencedaily.com, a full 90 percent of all the data in the world has been generated since 2011. In practice, we don’t have the necessary distance to evaluate its real impact.

When it comes to agriculture, farmers all over the world must produce more to feed world’s rapidly expanding population in the coming decades. Will big data help feeding nine billion people by 2050? Time will tell…


Oct 17

Big Data Visualization: A Big Asset for decision making

Aymen, October 10.

As defined in a previous article by Feinleib, Big Data is the ability to capture, store, and analyze vast amounts of human and machine data, and then make predictions from it. On the other hand, Beyer and Laney, in their definition of Big Data, stress that it useful for an enhanced insight and decision-making.

Indeed, stocking large amounts of information is not useful by itself, but the main goal, in this case, is the way to use and combine this stock of data to facilitate decision making. For example, financial markets increasingly rely on Big Data to trace stock prices and refine predictions for computer-based trading. From a development perspective, Big Data can be useful to follow the development of projects and better understand the needs and expectations of beneficiaries.

Still, when thinking of Big Data, large SQL or Excel tables or algorithms for instance usually come to our minds. Although these two tools require a certain expertise to have the ability to “read between the lines,” they remain incomprehensible for the ordinary person. In his book “The Promise and Peril of Big Data,” David Bollier explains that Big Data usually rely on powerful algorithms that can detect patterns, trends, and correlations over various time horizons in the data, but also on advanced visualization techniques as “sense-making tools”.

In this sense, it is equally important to present eye-catching visualizations of the results extracted from Big Data. They will firstly contribute to making a large amount of information understandable and accessible, in addition to the dissemination of the findings through academic publications, reports, presentations, and social media. In his book “Data Visualization with JavaScript”, Stephen A. Thomas defines Data Visualization as the way to visualize large amounts of data in a format that is easily understood by viewers. The simpler and more straightforward presentation, the more likely the viewer will understand the message.

Indeed, Data Visualization is an important feature of Big Data analytics, as it can provide new perspectives on findings that would otherwise be difficult to grasp. For example, “word clouds”, which are a set of words that have appeared in a certain body of text – such as blogs, news articles or speeches, for example – are a simple and common example of the use of visualization, or “information design,” to unearth trends and convey information contained in a dataset, but there exist many alternatives to word clouds, such as geographic representation.

For instance, the infographic here below, based on large amounts of information, explains how mobile technology is used worldwide as a tool to improve health care, education, public safety, entrepreneurship, or the environment. These worldwide initiatives are part of the 9th Sustainable Development Goal (SDG), which aims to “Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation.”

big data visualization

In this example, Big Data is doubly useful as it is the base of projects launched in the various countries represented on the map. Concretely, it contributes to reducing maternal deaths from placenta praevia in Moroccan rural areas. Furthermore, the simple visualization of this large volume of information facilitates its analysis and can consequently help decision makers to track the progress of projects and can be used as benchmark data to reproduce the same successful initiatives in other countries.

Geographic representation of Big Data is used in the various field, especially in monitoring migration movements. In this sense, almost 200 academic studies involving big data and migration had been published between 2007 and 2016. The Syrian refugees’ crisis is a significant example of the use of Big Data to visualize migration flows. The infographic here below from the New York Time explains in a clear way how nearly half of Syria’s entire population was displaced due to the civil war.

big data visualization

In a wider context, the geographic representation of migration flows between 2000 and 2016 based on Big Data gathered by the UN Refugee Agency (UNHCR) summarizes in a clear and synthetic way the countries of origins and of residence/asylum of migrants.

These are two examples of how geographic visualization of Big Data can – through the historical track record – predict more accurately how many more refugees could be expected over coming years into which points of entry. As a result, military, police, and humanitarian efforts be more coordinated and pre-emptive based on this information.

It is important to take into consideration that stocking large amounts of information is not useful by itself, but its main goal is to use and combine this stock of data to facilitate decision making and create added value. The analysis of Big Data is a big asset as it might facilitate tracking the progress of projects, understand migration trends or allow a better-coordinated mapping of conflict or adversity and delivery of aid to people in dire circumstances.

However, we must remember that Big Data also enable strategies of surveillance, control, and population management. Big Data involves the quantification, classification, and construction of individuals and populations, and categories that are never impartial or objective but embedded in socio-political contexts. It is the researcher’s ethical role to keep these crucial points in mind when deciding what data to use, how to get it, treat it, store it, and share it. In the UK for instance, the manipulation of data and statistics has played a major role in bolstering anti-immigration narratives and xenophobic political agendas. This is to say that raw Big Data or visualized one are not themselves harmful, but the way they are used is indeed dangerous.

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


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


Sep 17

Big Data and Development: Challenges and Opportunities

Aymen, September 21.

Even though algorithm, coding, Big Data is becoming part of our daily life; these technical terms remain complicated for us, common mortals. What is Big Data for instance?

In his book “Big Data Demystified: How Big Data Is Changing The Way We Live, Love And Learn,” David Feinleib defines it as the ability to capture, store, and analyze vast amounts of human and machine data, and then make predictions from it.

Big Data is used for various purposes, for example, to understand online consumers’ behaviour and orient advertisements to their specific needs, and is the base of the predictive power of search engines. It is transforming the nature of business and profits worldwide and is gaining in importance in the development sector.

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