”Data has become increasingly important to the way we think and talk about conflict and our humanitarian responses to it”, Read, Taithe and Ginty write in their article ”Data hubris? Humanitarian information systems and the mirage of technology” from 2016. They exemplify this by referring to the UN High Level Panel on the Post-2015 Development Agenda’s ”call for a ”data revolution””, which ”would draw on existing and new sources of data to fully integrate statistics into decision making, promote open access to, and use of, data” (UN High Level Panel; Read, Taithe and Ginty (2016, p. 1).
However, ”data is not knowledge”, as the authors of this article emphasize, and they refer to geographer Trevor Barnes’ question: are we generating useful knowledge or are we collecting ”data for data’s sake”? (Read, Taithe, Ginty, 2016, p. 2).
I can relate this very well to my experience from advising newly arrived refugees at my home town in Northern Norway. From the moment when new asylum seekers or refugees arrive in Norway, the different authorities that are involved in the processing of the refugees’ cases will begin to gather data about them and their families. Throughout their asylum process and after they are granted a residence permit, the same data will be gathered again and again because so many different bodies or stakeholders are involved. In our work, therefore, I sometimes question myself if we are collecting ”data for data’s sake”. New technology gives us opportunities to collect, store and manage data in new ways. At the same time, the requirement on data collection, together with applying new (and sometimes hard to implement) technology where the data is stored, has the tendency to be caught up in bureocratic procedures that may make our work much less efficient than it could have been.
In their article, Read et al. conclude that ”the declarations of emancipation via a data revolution are premature” (Read et al, 2016, p. 12). The cases for this conclusion may be different from my case of data gathering about refugees, however the conclusions can be applied here as well. The authors of the article suggest, among other things, an improvement of the data-processing capabilities of humanitarian organisations as well as a request to ”collect enough, but not excessive, information” (p 13). This needs to be taken into consideration in the “data revolution” that is called for by the UN High Level Panel.
This is not intended to be a pessimistic statement against the ICT for development (ICT4D) or the ”datafication” of humanitarian work, but is meant to highlight one of the challenges one is facing when the digital world meets humanitarian work or development practice. New perspectives on this will come in the following blog posts.
References: Roisin Read, Bertrand Taithe & Roger Mac Ginty (2016): Data hubris? Humanitarian information systems and the mirage of technology, Third World Quarterly.
UN High Level Panel, Economies through Sustainable Development.
The term big data is used to describe an enormous volume of data which can be both structured and unstructured. And at the same time, this data is difficult to understand if using conventional information processing techniques (Wikipedia 2017, Big data).
Definitions of big data “vary by industries such as information technology (IT), computer science, marketing, social media, communication, data storage, analytics, and statistics” (Spratt and Baker, 2016, p. 8).
Big Data and Development
In terms of international development, big data provides important contributions in key development areas. These areas include resource management, economic productivity, health care, natural disaster, job market, etc. The analysis of online user-generated data provides opportunities for people from all over the world to have their voice heard.
In their research, Spratt and Baker argue that big data “will be the fuel that drives the next industrial revolution, radically reshaping economic structures, employment patterns and reaching into every aspect of economic and social life” (Spratt and Baker, 2016, p. 4). If in 1946 the first computers weighed thousands of kilograms and could do no more than 500 calculations per second, these days, the IBM Watson supercomputer can process 500 gigabytes per second. That is the equivalent of reading one million books per second.
However, as Spratt and Baker suggest, we should “distinguish big data from two related concepts: information and communications technology (ICT) and ‘open data’” (Spratt and Baker, 2016, p. 8). They argue that “big data is not always open, and at times will not be accessible without special skills or software” (Spratt and Baker, 2016, p. 8).
Big Data and Its Direct Impact on Development
As suggested by Spratt and Baker (2016), the potential impacts of big data can be classified as direct or indirect.
Directly, big data contributes to the process of creating new markets which are based on both production and consumption of data. Inevitably, this leads to the creation of a new complex physical infrastructure that is able to fully support the process of data production and consumption.
The three V’s of big data stimulated innovations in software, including data analysis, data management and networking. This allowed the creation of many now multi billion companies. Such companies had their start from open source projects such as Hadoop which was initially created to store and process huge amounts of data (Spratt and Baker, 2016, p. 8).
Thus, data science becomes a profession and data scientists combine “the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data” (The Economist, 2010). Such employment opportunities require special qualification. And the demand for this special kind of knowledge creates these new employment opportunities.
In fact, these consequences have a great impact on developing countries. Many corporations outsource their data analysis departments to developing countries where computer skills are high and costs are low. And “skilled young adults in Uruguay will find themselves competing for certain types of jobs against their counterparts in Orange County” (MSNBC 2013). Amazon Mechanical Turk and Samasource (a non-profit business) are some of the organizations that promote the outsourcing of digital work to unemployed people around the world.
Big Data and Privacy
In terms of privacy, the buying and selling of data can create negative impacts as well. Once they collect the data, consumers are not in control of that data (Craig and Ludloff, 2011). And that’s an issue in both developed and developing countries.
Furthermore, issues like privacy and discrimination seem to be working in favor of the digital divide. In fact, “while data-driven discrimination is advancing at exactly the same pace as data processing technologies, awareness and mechanisms for combating it are not” (Taylor, 2017, p.2). This contributes to various issues like online data privacy, transparency, (technological) inequality… (Wikipedia 2017, Big data). In this regard, Taylor draws a parallel between the idea of justice in general and the idea of data justice. We need data justice to “determine ethical paths through a datafying world” (Taylor 2017, p. 2). Linnet Taylor argues that the importance of big data and datafication and their positive impact on “citizenship, freedom and social justice are minimal in comparison to corporations and states’ ability to use data to intervene and influence” (Taylor, 2017, p. 2).
The Indirect Impact of Big Data
Indirectly, various institutions and sectors will experience the positive influence of the impact of big data. Because it can increase efficiency and productivity. Methods using big data can create organizational improvements of companies, public institutions, NGOs and even social movements. But at the same time, it may negatively impact concerns around privacy and civil rights. And this may lead to increasing social and economic inequalities such as the so called ‘digital divide’.