Point of View is a forum for opinions and thoughts from XBRL US and the XBRL community about business, industry, finance, regulation and legislation, where data standards can play a role. We welcome your feedback on the XBRL point of view.
March 5

From Implicit to Explicit: How the OIM Cube Definition Transforms XBRL Data Modeling

By Campbell Pryde, President and CEO, XBRL US This is the third in our blog series describing the proposed features of the OIM Technical Specification. Our last blog described the importance of the enhanced specification to optimize the performance of LLMs. The new specification is also designed to drive greater efficiency in the reporting and […more]

Creating a Cleaner Semantic Data Model for XBRL

By Scott Theis, President/CEO, Novaworks; Chair, XBRL US Domain Steering Committee (DSC); Leader, XBRL Technical Advisory Committee for XBRL US (XTAC); Member, XBRL Standards Board and OIM Working Group. In our last blog, Modernizing XBRL, we explored why the time is right to simplify and modernize the XBRL technical specification. One of the strongest forces […more]

Modernizing XBRL

By Scott Theis, President/CEO, Novaworks; Chair, XBRL US Domain Steering Committee (DSC); Leader, XBRL Technical Advisory Committee for XBRL US (XTAC); Member, XBRL Standards Board and OIM Working Group. XBRL is modernizing and simplifying. The XBRL community has begun laying the groundwork for a new XBRL technical specification that can be put in place to […more]

Artificial intelligence thrives on structure.

Agency-specific FDTA rules need to make sure the data they collect is AI-ready. AI shines when given access to richly contextualized, standardized, structured data and identifiers. While Large Language Models (LLMs) can source any kind of data, structured or unstructured, for financial analysis and research that demands a high degree of accuracy, structure and standardization […more]

Resources for Extracting Machine-Readable Data

XBRL US Members and others using our Public Filings Database and the XBRL API know extracting data from machine-readable reports yields a wealth of granularity for unambiguous analysis. Extraction involves an XBRL processor – software that reads tagged facts and interprets them according to the taxonomies defined for the report. Altova’s MapForce and XMLSpy feature […more]