Tactical, Operational & Strategic Analysis of Markets, Competitors & Industries
Organizations face complex problems during extraction and analysis of data using open source intelligence emissions.
One of the major problems is the influence of white noise on the accurate perception. If a company is exposed to white noise and conflicting stimuli and data, it creates a deep interference with accurate perception even after better information becomes available later on during analysis stage.
For instance, a person exposed to distorted and hazy picture will make pre-conceptions of the information and will develop more confidence in it. This means initial impression of information will have more impact on subsequent perceptions.
When the image becomes clearer, new data is imbibed into the previous image but we maintain the initial perception and develop resistance to changed image until the contradiction becomes so strong and obvious that it forces itself upon consciousness.
Most of the companies perceive that the early information required to make an initial interpretation is sufficient, but we should not forget that early but incorrect impression tends to endure.
The amount of information to nullify a hypothesis is significantly greater than the amount of information required to make an initial judgments.
The difficulty companies’ face is not in acquiring new perceptions or new ideas, but that already established perceptions meet with resistance.
On availability of very little information, many companies tend to form their assumptions that are not rejected or changed unless rather firm and extensive evidence forces them to reconsider the analysis.
Hence, the impact of pre- existing assumptions and expectations on perception of inputs leads to the ambiguity of the inputs and results in dissonance.
The company’s analysts own pre- conceptions exert a big impact, despite striving for objectivity.
Corporate Risks deals with such highly ambiguous situations as here analysts suspend the pre- existing notions for as long as possible and efficiently extract and analyze valid information from the information emissions in the environment.
In intelligence, we need to join the dots. So how can we join the dots?
We need to separate information from white noise. White noise is like quick sand, the more we delve into it without being focused results in organization seeing an incorrect picture. The problems in data extraction may present a different picture because of the ability or inability of the operator who is gathering information.
We need to strike a balance between what we want to achieve and what the information reflects.