information-structure-quality

What this datapoint measures

Quality of tables, comparison matrices, lists, structured information presentation. Whether the brand presents information in formats that AI systems can extract as structured data, or whether information is buried in prose without structural cues.

What high looks like

• Tables used for tabular data with proper table markup
• Comparison content presented as comparison tables
• Lists used for enumerable items
• Definition lists used for term-definition pairs
• Structured information signaled with appropriate HTML elements
• Numerical and quantitative data presented in extractable formats

What low looks like

• Tabular data presented as prose (“First, X is true. Second, Y is true. Third, Z is true.”)
• Comparison content as paragraphs rather than tables
• Lists rendered as div containers without list markup
• Numerical data embedded in prose without structural emphasis

What at floor looks like

A brand at floor on information-structure-quality has structured content (data, comparisons, enumerations) that exists in the brand’s pages but is presented without the structural cues that make it AI-extractable. The information is there; AI systems cannot easily extract it.

The remedy is content restructuring, often as part of M-2 (Answer-First Architecture) or O-6 (Content Audit). The work is editorial-engineering hybrid: editorial review identifies structured content; engineering ensures the markup supports the structure.

What affects this datapoint

• Use of HTML table elements for tabular data
• Use of list elements for lists
• Use of definition lists for term-definition pairs
• Numerical data in extractable formats
• Comparison content in comparison-table format

OMG actions that influence this datapoint

Action	Influence
M-2 Answer-First Content Architecture	Substantial. Answer-first work often involves restructuring information into extractable formats.
O-6 Content Audit & Baseline Optimization	Substantial. Audit identifies prose-buried structured information.
M-9 Interactive Tool Development	Indirect. Interactive tools often present information in structured formats that flow into related pages.

Multilingual considerations

Information-structure quality is largely language-neutral in markup. The structural elements work the same way regardless of content language. Considerations:

• Tables with CJK content require proper character-encoding handling (utf-8 throughout)
• Comparison tables may need per-language translation of comparison criteria
• Numerical formats may differ by locale (decimal separator, thousand separator) but should not affect structural quality

Common failure modes

• Pricing tables presented as prose
• Specification comparisons in unstructured paragraph form
• Lists of features without list markup
• Numerical data embedded in narrative without structural cues
• Tabular data implemented as image (a screenshot of a spreadsheet)

Diagnostic interpretation

Information-structure-quality at floor with content-formatting (V2.2) also low indicates broad structural work needed. M-2 and O-6 work address both.

Information-structure-quality at low with content-depth at high indicates a brand with deep content where the structure obscures extractability. Editorial restructuring lifts the datapoint without requiring new content.

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