Problem:
Where there are items to search and sort, the user wants the most valuable items to be surfaced, via a deep insight into what they value and how that value is manifested in the data.
Solution:
The system deploys new metrics based on emergent properties of the specific data used by it, that are more insightful, more meaningful, and better model the users intent in interrogating the data. These metrics can be used to sort items and assess the relative value of one item to another in a list.
Discussion:
While we always appreciate the benefits of providing data to the user, often we overlook the opportunities to innovate when it comes to parsing and processing that data. As systems grow increasingly intelligent, rather than simply providing the user with raw variables to make sense of, we should think about how the system can do that work itself, providing the user with the most valuable results through a deeper understanding of the content of the data and the user’s relationship with it. This often already takes place behind the scenes (e.g. the algorithm that determines which search results Google thinks are most relevant for each user) but can be opaque and appear to be Mystery Magic. Using emergent metrics explicitly exposes this power to the user, helps them build their conceptual model of the system, and allows them to choose whether to use it or not. And where a piece of software has a particular strength in processing data, attaching that feature to an emergent metric can also help champion that strength as part of a unique value proposition.