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The key principles that underly the data structure are: Multi-Dimensional: Every ETF in the world is tagged on multiple dimensions. For example, the 'Wisdom Tree International Hedged Dividend Growth ETF' is tagged as 'Equity', 'Developed', 'Ex-US', 'Currency hedged', 'Dividend weighted', 'Growth', etc. This multi-dimensional tagging is of the utmost importance since the analysis of ETFs could start on any of these dimensions. Consistent: While multi-dimensional tagging is important, a uniform standard must be applied on individual dimensions. For example, on sector & industry, the data stays consistent with the Global Industry Classification System (GICS) hierarchy Prospectus Based: If an investment manager classifies an ETF as 'growth', then it will be classified as 'growth' within the data. Doing so simplifies the data as there is no industry-wide agreement on how to define specific dimensions (i.e. growth or value). Additionally, it avoids confusion (i.e. if the manager labeled an ETF as 'growth' and it was to be labeled as 'value' within the data lists, it would complicate ETF search and analysis). Extensible: New types of ETFs are consistently being brought to market. Therefore, First Bridge constantly monitors the ETF universe and has a flexible database where new 'tagging' fields are added as appropriate, all while maintaining consistency on existing fields.
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