Life sometimes seems impossible to rationalize as discrete objects or events. But, if you start to consider the connectivity around you, a tapestry of links emerges based on, what appears to be, a finite set of rules. In this way hands are connected to arms and friendships are made through the casual sharing of ideas and kinship. Considering data like this can therefore help uncover previously hidden patterns. That is, if some established scientific approaches are appreciated alongside. For example, the point the way to some very useful techniques for understanding the inherent value in data (or more precisely information).
At surface inspection such laws might seem detached from the idea of information, helping instead to describe physical properties like temperature, energy and entropy. But history tells us how they proved essential in the early development of Information Theory. This then provided a valuable way-point on the road towards more contemporary ideas on how information chooses to connect and structure in the wild. One such idea is that of scale-free networks – networks that maintain their essential characteristics regardless of how large they are. These can be found in the the distribution of popularity of websites across the World Wide Web, or the way that consensus spreads through a social network. Similar patterns emerge from the analysis of economics data, or most other fields of human endeavor.
Uncovering patterns like these is critical to the progression of digitalisation and extracting value from digital data at scale. One can think of uses such as and surely gives us an indication of the advances needed to uncover the underlying laws of human behaviour and the complex systems that intertwine our lives. Interdisciplinary collaboration is needed to advance our understanding of complex data sources, however. As explained by the authors of increased accuracy comes from combining insights from multiple sources and fine tuning the probabilities.
To find your space in the digital economy a combination of skills and techniques, we have depended on for many years, needs to be applied in creative ways to find those hidden patterns, develop the laws and thereby help reduce inefficiencies and create value from the data.
Written in collaboration with https://www.linkedin.com/in/philtetlow/