Structural Coherence Analysis
A framework for measuring the alignment between what empirical data shows and what public discourse says, applied across policy, investment, and geopolitical domains.
The core question
When does the narrative match reality?
Every issue domain has two registers of truth: what the data shows (investment flows, trade volumes, production indices) and what the discourse says (media framing, stakeholder positioning, public narrative). These two registers rarely align perfectly. SCA measures that gap, systematically, across actors, and at scale.
How it works
Dual-track measurement
Each analysis runs two parallel tracks simultaneously, then measures their coherence.
Empirical track
Quantitative data: indices, trade volumes, FDI, production stats. Processed via Social Network Analysis to map each actor's influence and interest positions within the network.
Narrative track
Qualitative signals: media coverage, stakeholder statements, public discourse. Weighted via Analytic Hierarchy Process for consistent, validated parameter scoring.
Structural Coherence Index, measuring how aligned is an actor's empirical position with its narrative position, across influence and interest dimensions.
82
High coherence
51
Partial coherence
23
Incoherent
Analytical tools
What powers each track
Social Network Analysis (SNA)
Maps actor positions within empirical networks: who holds influence, who has the greatest stake. Output becomes the quantitative input for SCI calculation.
Analytic Hierarchy Process (AHP)
Weights narrative parameters with validated consistency ratios, ensuring scoring is structured and defensible, not arbitrary.
Stakeholder Mapping
Translates SNA output into four-quadrant actor maps, visualizing influence, interest, and coherence direction simultaneously.
Coherence Factor
Converts the gap between empirical and narrative scores into a proportional penalty, which is the mathematical core of the SCI calculation.
Inside the StratDNA pipeline
How SCA powers our products
SCA is not a standalone product, it is the confidence engine that runs underneath the entire intelligence pipeline.
Input layer
StratData
Every dataset is built with explicit empirical and narrative columns per parameter. This dual-track structure gives SCA the raw material to measure coherence.
Explore StratData →Validation layer
SCA Engine
Measures the gap between empirical and narrative signals across all actors and dimensions. Produces SCI scores and flags incoherence for recalibration.
Early warning signal
StratLens
Validated signals surface as prioritized alerts in StratLens. Users see which issues demand attention before committing to deeper analysis.
Explore StratLens →Strategic conclusion
StratInsights
From the signal, users move into StratInsights for the full intelligence brief. Prescriptive recommendations grounded in coherence-validated evidence, not just data synthesis.
Explore StratInsights →What clients receive
Outputs per project
SCI score per actor
Ranked coherence scores across all actors in the issue domain, with direction indicators.
Coherent stakeholder map
Four-quadrant positioning of actors by influence, interest, and coherence direction.
Strategic intelligence brief
Prescriptive recommendations grounded in coherence-validated findings, delivered via StratInsights.