Normalized value for an assessment

  • Release version: Xanadu
  • Updated August 1, 2024
  • 2 minutes to read
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    Summary of Normalized value for an assessment

    The normalized value for an assessment metric in ServiceNow is calculated using a linear equation based on the metric's scale definition. This normalized value supports risk assessments by providing a standardized measure of metric results relative to their defined scales and weights within a metric category.

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    Normalized Value Calculation for Single Metrics

    The normalized value is proportional to the metric's scale, and if lower scale values represent better outcomes, the formula is inverted accordingly. The calculation uses values from the Metric Result [asmtmetricresult] table and follows this formula:

    Normalized value = ((Input Value - Min Value) / (Max Value - Min Value)) × (Current Metric Weight × Bias / Sum of Valid Metric Weights in Category) × Scale Factor

    Bias adjusts for excluded scripted metrics and skipped metrics, ensuring only valid weights contribute to the sum. Metrics of types String, Date, Date/Time, Reference, Attachment, and Ranking are excluded from normalization calculations.

    Example for Single Metric

    • Input Value: 3
    • Min Value: 1
    • Max Value: 6
    • Current Metric Weight: 10
    • Number of Metrics in Category: 6 (4 numeric, 1 yes/no, 1 string excluded)
    • Scale Factor: 10

    Normalized value calculation results in 0.8, demonstrating the application of the formula in a typical scenario.

    Normalized Value Calculation for Multiple Selection Metrics

    For multiple selection metrics, the normalized value is calculated by summing the scores of all selected choices, each score being the ratio of the choice’s normalization input to the maximum value. The minimum value is always zero. The formula is:

    Normalized value = (Sum of Choice Scores) × (Current Metric Weight / Sum of Valid Metric Weights) × Scale Factor

    Where, Score of each choice = Normalization Input of the choice / Maximum value of the metric

    Example for Multiple Selection Metric

    • Choices A, B, C with normalization inputs 1, 1, and 2 respectively
    • Max Value: 4 (sum of all normalization inputs)
    • Current Metric Weight: 10
    • Number of Metrics in Category: 5
    • Scale Factor: 10

    If choices A and B are selected, the normalized value calculates to 1, illustrating the weighted impact of selected options.

    Weighted Value for Risk Assessment

    The weighted value used in risk assessments is derived from the metric result as follows:

    Weighted Value = Metric Weight × Actual Result Value

    This provides a direct metric impact measure factoring in the metric's importance (weight) and the observed result.

    The normalized value is calculated based on a linear equation and the scale definition of the metric. This value can be used for risk assessment.

    Normalized value for any metric

    The normalized value is directly proportional to the scale definition of the metric. If the scale definition is low, that is, the lower scale values are better, then Normalized value = 1.0 – Normalized value.

    For the reporting purpose, use the Metric Result [asmt_metric_result] table.

    Normalized value = (Input Value - Min value defined in metric) / (Max value defined in metric - Min value defined in metric) * (current metric weight * bias / (sum of all metric weight in the metric category)) * scale_factor

    Bias is the ratio of total metric weight in the category and the sum of valid metric weight in the metric category. While calculating bias, the scripted metrics are excluded.
    Note:
    • If a metric is skipped when taking the assessment, its weight is excluded when calculating sum of valid metric weight in the metric category.
    • The following metric types are excluded in the normalized value calculation:
      • String
      • Date
      • Date/Time
      • Reference
      • Attachment
      • Ranking

    For example, consider the following scenario.

    Calculate the normalized value for the Please rate the competency of the technician metric.

    Table 1. Values of the metric
    Input value 3
    Minimum value 1
    Maximum value 6
    Current metric weight 10
    Number of metrics in the metric category 6
    • 4 of type=number
    • 1 of type=yes/no
    • 1 of type=string (invalid data type; value cannot be calculated)
    Valid metric weight of each response 10
    Scale factor 10

    Normalized value = (3 - 1) / (6 - 1) * (10 / (10 + 10 + 10 + 10 + 10)) * 10 = 0.8

    Normalized value for a multiple selection metric

    The normalized value for a multiple selection metric is calculated by using the weight of the metric and the score for each choice of the metric.

    In a multiple selection metric, for each choice that should be used for the normalization calculation, define the normalization input value.

    Normalized value = (Score of all choices) * (current metric weight / (sum of valid metric weight in the metric category)) * scale_factor

    Here, score of all choices of the metric is the sum of individual scores of each choice.
    • Score of each choice in a multiple selection metric= Normalization input of the choice / max value of the metric
    • max value of the metric = Sum of the normalization input for all choices of the metric
    • min value of the metric is always 0

    For example, consider the following scenario.

    Calculate the normalized value for the multiple selection metric, Please rate the competency of the technician, with three choices, A, B, and C.

    Table 2. Values of the metric
    Choice A Normalization input is 1
    Choice B Normalization input is 1
    Choice C Normalization input is 2
    Minimum value 0
    Maximum value 4
    Current metric weight 10
    Number of metrics in the metric category 5
    Valid metric weight of each metric 10
    Scale factor 10

    If Choice A and B are selected, Normalized value = ((1 / 4) + (1 / 4)) * (10 / (10 + 10 + 10 + 10 + 10)) * 10 = 1

    Weighted value for a risk assessment

    For a risk assessment, the weighted value from metric results table is calculated as following.

    weighted_value = metric.weight * result.actual_value