Smith Stillion and its Managing Partner David Smith have been researching the following topics for the
last 20+ years:
1) AI structures the other 80% of your data. Why is this a game changer for the data analytics world? Contextualization and implementation for the commodity marketplace. They have developed a data model that allows the aggregation of commodity visualization items into their assembled use-case scenarios. Then allow the cross-reference of like functional items and the substitution of similar function items. In addition, it facilitates the optional alteration of the alternatives through the dimensional/characteristic dimension.
2) Industry database structures that allow disaggregation of previously banded data and modeling it forward to be utilized in ML and AI applications. Deployed in Manufacturing, Transportation, Logistics, Engineering Construction, Research, Banking, Commodities, and Textile industries
3) Contextual Relevance in Human Interactions. In 2012 a research project was started to explore how to augment human capabilities with modern data processes. The volume of information is ever-increasing, the velocity of information is increasing, the disparity of information is increasing and the tenure of employees is decreasing. The research had three main objectives: First: find a way for humans to access billions of pieces of information at the same time they could currently access hundreds of pieces of information (achieved through multidimensional access methodologies). Second, develop an interface paradigm that enabled immersive interaction with data/situations/problems and enabled augmentation and edge case detection (achieved through AR & other visualizations). Third, reduce the cost and complexity of deployment so that information between all parties large and small can flow freely, with the understanding that the more real information parties have, the better decisions can be made (achieved through the democratization of data with Qikspace)
4) Forecasting framework. Developed algorithms to more accurately predict future outcomes based on contextualized current data. Models have proven to accurately project outcomes up to 14 months in advance.v 5) Project controls. We have developed our own project control software used to implement projects, track projects, and diagnose troubled projects.
6) Productivity. We have written two books on productivity and also produced research findings on various situational, compensation, and environmental impacts on productivity, ability to execute, risk-taking, and logistics.
David A. Smith