Remember the "Kodak Moment?” It was a term in photography popularized by Kodak to capture important moments.
Well, right now there is a Kodak Moment going on in healthcare information sciences. It is associated with the attribute-based data structures that are the basis for the revolution in genetic diagnostics, clinical risk management, and personalized medicine. It is also the foundation and source of the advances in Big Data and Artificial Intelligence in healthcare.
In this session, you will learn about a new innovation in business information management called the Locus Model and a new type of business information system called the Functional Information System (FIS). These important innovations have the potential to impact all data management in business, finance, and economics by introducing a universal standard that can unify the disparate systems in disparate countries that we currently use to classify and organize business, finance, products or job information.
It is already the basis for a new financial product, an ETF, on the New York Stock Exchange that uses this system to manage business risk in the S&P 500. It is currently being applied to many other of the most widely held stock market indices such as S&P400, MSCI EAFE, Russel 1000, and the Wilshire 5000. Like in healthcare, this innovation provides the same promise in business and finance that an attribute-based data structure provided Big Data and Artificial Intelligence applications in healthcare.
Rory Riggs is the CEO and Founder of Syntax Indices & Locus Analytics. He and his team created a revolutionary coordinate-based system for identifying and mapping economic activity based on the function of a business or its product.
Rory’s idea for a Functional Information System (FIS) for business and finance Syntax Stratified Indices came from his career in healthcare and the industry’s use of functional attributes to relate common phenomenon population and statistical sampling and stratification across sub-populations to control for inadvertent biases in clinical trial results.
To address the potential of similar biases in index results, he and his team identified a new risk category called related business risks, developed a new classification system with which to identify and group related business risk, and implemented the Stratified Weight methodology to control for the inadvertent over-weighting of related business risks that regularly occur in capitalization-weight and equal-weight methodologies. The system has been found to have broad applications across finance, eCommerce, economic policy and economic development.