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Within most traditional problem-solving frameworks, practical decision making starts with either a formal or informal assessment of "needs." These initial assessment processes are the preliminary steps in determining the performance criteria by which alternative solutions can later be evaluated and selected. To facilitate this critical step in problem solving, needs assessments typically identify and prioritize discrepancies between the current and desired accomplishments. And these assessments are most valuable when you define "needs" solely as the gap between what results should be accomplished and what results are currently being accomplished without discussion of disparities in resources, inputs, processes, or other means.
In organizational practice, however, needs assessments are often informally skirted when organizations immediately respond to a request (for example, "we need xyz training program," "I need a new computer," or "we need more distance education courses") with either a causal analysis or a course development process while the yet-unverified need continues to be assumed as a performance problem.
By assuming, and neither measuring or validating that the "need" identified in the request is actually a reflection of a discrepancy between the results that are expected to be achieved and those currently being achieved, the problem-solving and decision-making processes begin without adequate justification. Not only may the "need" be exaggerated, misunderstood, or miscommunicated, the "need" may actually be a strength or asset when measured. But without a systematic assessment process, the assumed "need" drives decision-making rather than allowing for measurable evidence and clear criteria to be the drivers for a successful intervention.
As a result, a comprehensive and systematic needs assessment process is necessary for practical decision making. Comprehensive assessments are defined on multiple dimensions, including a first dimension that requires that the assessment collect evidence that includes both hard and soft data; hard...