The Unequal Diffusion of Big Data Capacity 200w" sizes=" 639px) 100vw, 639px" data-recalc-dims="1" />
A 2014 White House report from the office of President Obama underlined that Big Data leads to ‘vexing issues’. Big data technologies can cause societal harms beyond damages to privacy, such as discrimination against individuals and groups. While at the same time it emphasized the tremendous opportunities these technologies offer to improve public services, grow the economy, and improve the health and safety of our communities.
In Big Data for Development, a review by Martin Hilbert of over 180 pieces of mainly recent literature, and several pieces of hard fact empirical evidence, has confirmed that the Big Data paradigm entails both opportunities and threats for development.
On the one hand, an unprecedented amount of cost-effective data can be exploited to inform decision-making in areas that are crucial to many aspects of development, such as healthcare, security, economic productivity and disaster and resource management, among others.
The extraction of actionable knowledge from the vast amount of available digital information seems to be the natural next step in the ongoing evolution from the ‘Information Age’ to the ‘Knowledge Age’.
On the other hand, the Big Data paradigm is a technological innovation and the diffusion of technological innovation is never immediate and uniform, but inescapably uneven while diffusion proceeds.
Big Data for Development has shown that the Big Data paradigm currently runs through an unequal diffusion process that is compromised by structural characteristics, such as the lack of infrastructure, human capital, economic resource availability and institutional frameworks in developing countries.
This creates a new dimension of the digital divide: a divide in the capacity to place the analytic treatment of data at the forefront of informed decision-making and, therefore, a divide in (data-based) knowledge.
These development challenges add to the perils inherent to the Big Data paradigm, such as concerns about State and corporate control and manipulation, and the blind trust in imperfect algorithms. This shows that the advent of the Big Data paradigm is not a panacea. It is essential that this transition be accompanied and guided by proactive policy options and targeted development projects.