Business executives incorporate an intelligent framework known as data triangulation to look at challenges from diverse viewpoints to present a more transparent resolution. Triangular thinking aids in solving intricate and adaptive challenges that are believed to be continuously changing. This type of thinking is an excellent approach to solving evil challenges that are frequently contradictory and includes incomplete data (Critical Thinking, 2020). When incorporating this aspect of triangulation, every perspective of the challenge turns out to be consistent. Non-existence of coherent opinions signifies lack of a distinctive understanding of the challenge.
There are numerous ways of describing evil challenges. Foremost, it is imperative to understand that there is no ultimate principle for a malicious challenge. Malicious challenges similarly bear no preventing rule, since there is no mode of assessing an individual’s ultimate pronouncement. The answers to harms are neither considered to be true or false, but they may be regarded as wither good or bad (Subbotin & Voskoglou, 2015). According to Livingstone (2014), there exists no instant substantiation of an explanation to an evil problem. Every solution to any harm is a “one-time process”; as there is no mode of learning through trial and error as every effort is significant. Additionally, there exists more than one clarification for evil challenges as the clarifications greatly vary dependent on the personal viewpoint.
Triangular thinking one mode of creating this credit crystal. If executives are unable to directly measure an aspect directly, they may merge hypotheses, trends, expectations and interpretations, and “vector in” or “triangulation” in the outcomes. For instance, three responses or a suggestion present almost the similar value or response, then it can be indicated that maybe they are near to finding a response. If two assumptions result to similar clarifications, the researcher might deem this theory as being accurate. If they fail to “triangulate,” then maybe the hypothesis or expectations are inappropriate, and they should be appraised before making a conclusion.