As data science gains momentum, society gains access to developing a fuller statistical understanding of human development. Rob Kitchin asserts that Big Data is innately high in velocity, exhaustive in scope, fine-grained in its resolution, and extremely relational. These features of Big Data ensure that the human development patterns and processes detected from the data will be up to date, inclusive, detailed, and flexible. As a result, we can analyze and predict development across a variety of countries, noting their similarities, juxtaposing their differences, and considering how these factors affect the country’s development. As the volume of datasets increase, models have become more exhaustive and data-driven. Big Data has allowed them to evolve out of being merely oversimplified representations for the sake of understanding. Chris Anderson stated that now “correlation supersedes causation” and that hypotheses no longer need to be educated guesses scrambling for supporting data, but rather, existing data can inspire hypotheses. Data science has elevated models as we know them, highlighting their traditional readability yet increasing their accuracy and depth. This is a tremendous improvement as a result of data science. It has allowed us to elevate our understanding of human development models by preserving accuracy.
In the future, I hope that data will continue to inspire society’s appreciation for systematic complexity. Rather than oversimplifying data with reductive models or, on the other hand, being intimidated by the sheer complexity of human development and chalking it up to chaos, there has been a growing initiative to detect patterns, predictability, and organization in human development. Not only do we seek structure in human development, but data science has equally illuminated the deep interconnectivity of the factors of human development, or as Amartya Sen calls them, unfreedoms. This harmony between simplicity and complexity, sometimes referred to as “shibui,” is gaining popularity in human development due to the introduction of big data, which reveals patterns while maintaining its wide scope. With this idea in mind, we can appreciate how two communities on opposite sides of the world, despite differences in culture, economy, and beliefs, can have striking similarities in development issues. Similarly, even countries with cultural, economic, and political similarities can have different development rates due to one seemingly insignificant factor, such as proximity to a fault line. Before the upsurge of data, we might not have detected such a unique factor, but now, the data reveals the variables responsible for gaps in development even if a human overlooks their importance. Today, we no longer have to collapse countries into first, second, and third world country categories. We can recognize individualism and appreciate that such predictable and structured simplicity can exist within complex adaptive systems.
Geoff West’s Scale aligns with this approach as he explores unintentional urban biomimicry. West humbly recognizes that “the process of urbanization may just be ‘too complex’ to be subjected to laws and rules that transcend their individuality in a useful way,” however it is still worth noting the processes and patterns that can be understood in a scientific context. He asserts that in nature and in cities, there are accidental similarities in scaling, networks, physical structure, interconnectivity, and frequency. This prompts the inevitable question: if we mimic nature unintentionally, could we intentionally mimic nature to improve the human condition? Janine Benyus introduced this idea, which she called biomimicry, and applied it to engineering and design. She takes West’s idea one step further. Benyus seeks solutions to human problems in nature, taking note of how it has organically solved similar if not identical problems. For example, the famous Japanese Bullet Train was modeled after the aerodynamics of a bird’s beak. We can employ data science to quantify the results of nature-inspired solutions, such as analyzing data on the train’s improved efficiency before and after structurally mimicking nature.
While there are many benefits to the data revolution, becoming increasingly dependent on data as a way to spur human development introduces ways to “cheat” the system. For example, Blumenstock explains that people have created fake thatched roofs so that satellite imagery can categorize the home and its inhabitants as underprivileged, thus, in need of government financial assistance. Furthermore, I have some concerns about personal privacy and the right to be electronically forgotten. General Data Protection Regulation is becoming more important as our data and content output increases, and there are some gray areas when it comes to data and content ownership. For example, if someone wants to be completely anonymous online or possibly have a nonexistent online presence, then being in the background of a stranger’s Facebook photo brings up some issues. While I would hope that the majority of the population can be relaxed about accidental photobombing and a stranger unintentionally initiating a digital footprint for you, I recognize the importance of Internet privacy and see this as a potential issue for those that are more skeptical of an online presence, such as high-profile military personnel or politicians with strict security clearances. This right to privacy and the right to be digitally forgotten may progress from being an unrealistic luxury to being perceived as a human development freedom in the future.