Informational Triangulation
Informational triangulation, or cross-source insight verification, improves decisions by making the available evidence more credible and dependable. It checks consistency across different data sources and methods of analysis to control for threats to truthfulness.
THE IDEA
Informational Triangulation
When confronting routine or atypical choices, informational triangulation, or cross-source insight verification, enhances the efficacy of decisions by strengthening the credibility and dependability of available evidence. It tests the consistency of findings obtained through different data and analysis means, and increases the chances of controlling for the most pronounced threats to truthfulness of insights extracted from different informational sources. From the conceptual points of view, informational triangulation is framed in the context of ‘what’, or source of information, ‘how’, or means of drawing conclusions, and ‘why’, or the theoretical framework or logical rationale shaping the insight extraction processes.
The what-how-why evaluative context presents different set of challenges across the spectrum of potential sources of decision-guiding insights. In general, empirical sources of information, which include all manners of data available to organizations and results of scientific research studies, lend themselves more naturally to that three-pronged assessment, whereas experiential sources of information, best exemplified by industry benchmarks and practices, as well as individual-level, experiential knowledge, present far greater assessment challenge. That said, the ultimate point of informational triangulation is to arrive at evidence-derived conclusions that demonstrates the greatest degree of support, which in this case means the strongest degree of cross-evidence type congruence.
‘All models are wrong, but some are useful’ is an aphorism widely repeated by statisticians.[3] The essence of this assertion suggests that, individually, all sources of knowledge are suspect, but collectively can produce sound decision-guiding knowledge. This is one of the most fundamental assumptions underpinning the evidence assessment framework detailed in this book.
When to use it
Validating customer insights by comparing survey results, purchase data, and focus group feedback before launching a product.
In scientific research, using multiple experimental techniques and statistical models to ensure findings are robust.
In policy-making, triangulating evidence from empirical studies, expert opinion, and historical case analyses to inform decisions.
In personal finance, cross-checking investment advice across different analysts, historical data, and personal risk tolerance.
FROM THEORY TO EVERYDAY LIFE
In practice
Business market validation
A company considering a new product launch uses informational triangulation by combining customer survey data, sales trends from similar products, and expert interviews to cross-verify demand assumptions.
Scientific hypothesis confirmation
A research team tests a hypothesis using both observational data and controlled experiments, then applies different statistical models to confirm consistency across methods.
A MOMENT TO REFLECT
Put it to work.
Think of a recent decision you made. What sources of information did you use, and how could you have triangulated them to increase confidence?
Identify a situation where one source of information might be biased. How would you use informational triangulation to mitigate that bias?
How can you apply the ‘what-how-why’ framework to evaluate the evidence for a controversial claim?
CONTINUE THE CONVERSATION
Source texts
- Evidence-Based Decision-Making How to Leverage Available Data and Avoid Cognitive Biases (Andrew D. Banasiewicz)