Normalized value for an assessment

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of Normalized Value for an Assessment

    The normalized value is essential for conducting risk assessments in ServiceNow. It is derived from a linear equation based on the scale definition of a metric, allowing for a standardized evaluation of performance across different metrics.

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    Key Features

    • Calculation of Normalized Value: The normalized value is calculated using the formula:
      Normalized value = (Input Value - Min value) / (Max value - Min value) (current metric weight bias / (sum of all metric weight)) scalefactor
    • Bias Calculation: Bias reflects the total metric weight's ratio to the valid metric weight, excluding scripted metrics.
    • Exclusions: Certain metric types, such as String, Date, Date/Time, Reference, Attachment, and Ranking, are excluded from the calculation.
    • Multiple Selection Metrics: Normalized values for multiple selection metrics are calculated based on the scores of each choice, using the formula:
      Normalized value = (Score of all choices) (current metric weight / (sum of valid metric weight)) scalefactor

    Key Outcomes

    Upon applying the normalized value calculations, customers can effectively assess risks associated with different metrics, ensuring a consistent approach to evaluation. The normalized values can be used for generating insightful reports using the Metric Result table.

    By understanding these calculations, ServiceNow customers can enhance their assessment processes, leading to more informed decision-making and better resource allocation.

    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