| Type | |
| Focus | Complex biological systems |
| Concerns | Ethical issues • Societal implications • Privacy concerns |
| Initial pioneers | |
| Emerging technologies | Artificial intelligence |
| Evolving technologies | Computing • Storage |
| Expanded impact areas | Climate change • Urban planning • Public health |
| Pioneered in the field of | Whole-genome sequencing • Ecological models |
Big data emerged in this alternate timeline in the mid-20th century as a result of collaborative work between scientists across various fields. Unlike in our reality, where big data became synonymous with targeted advertising and consumer profiling, this timeline's exploration of big data has focused primarily on understanding and modeling complex biological systems.
The story of big data begins in the mid-20th century with the formation of the Global Systems Collective (GSC), a think tank comprised of scientists and researchers from various disciplines. By pooling resources and expertise, the GSC aimed to develop new tools and techniques for analyzing the vast data sets that were becoming increasingly available due to advancements in genetic sequencing and ecological modeling.
The focus on complex biological systems was the result of the need to better understand human health, environmental conservation, and sustainable resource management. The advent of new sequencing technologies in the 1970s enabled the GSC to explore entire genomes, leading to breakthroughs in disease research and genomics.
The lack of computational power and storage capabilities in the pre-Internet era posed a significant challenge for the GSC. To overcome these limitations, scientists and engineers worked closely to develop more efficient algorithms, reducing the storage footprint of the vast data sets and enabling faster computations.
Big data proved invaluable for addressing some of the most pressing global challenges. Scientists used these tools for improved disease outbreak tracking, ecological monitoring, and resource management. In the policy realm, big data was employed to make complex decisions on issues like climate change mitigation, urban planning, and public health strategy.
As the GSC expanded its work with big data, concerns around societal and ethical implications began to arise. Increased knowledge about complex systems has led to issues surrounding privacy and bias in decision-making. The question of data ownership has also become increasingly prominent, as well as considerations around the creation and implementation of data protection regulations.
As technology continues to evolve, big data will face new obstacles and opportunities. Managing the rise of Artificial Intelligence and dealing with complex ethical issues are essential factors to consider when shaping the future of big data. As its potential for creating better solutions for society increases, so does the need to address the privacy and protection concerns that come with it.
