Supported Computations for Insights

Computations are predefined calculations that you can add to an insight. The list of available computations is displayed in the Computation dialog when you create a custom insight or in the Edit narrative dialog when you customize the narrative of an insight.

When you add or edit a computation, you can specify several parameters.

Each computation also generates a set of output parameters. You can add these output parameters to the narrative to customize what it displays. To see the output parameters available for a computation, expand the Computations tab on the right of the Edit narrative dialog. The names of the computations come from the name that you specified when you created the insight.

Anomaly Detection

The ML-powered Anomaly detection computation searches your data for outliers. For example, you can detect the top 3 outliers for total printed jobs on December 3, 2024. If you enable contribution analysis, you can also detect the key drivers for each outlier. For more information about configuring anomaly detection, see Configuring Anomaly Detection.

To use this function, you need at least one dimension in the Time field well, at least one measure in the Values field well, and at least one dimension in the Categories field well.

Note: You cannot add ML-powered anomaly detection to another computation and you cannot add another computation to an anomaly detection.

Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • categoryFields – From the Categories field well.
    • name – The formatted display name of the field.
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • itemsCount – The number of items included in this computation.
  • items – Anomalous items.
    • timeValue – The values in the date dimension.
      • value – The date/time field at the point of the anomaly or outlier.
      • formattedValue – The formatted value in the date/time field at the point of the anomaly or outlier.
    • categoryName – The actual name of the category.
    • direction – The direction on the x-axis or y-axis that is identified as anomalous: HIGH or LOW. HIGH means higher than expected and LOW means lower than expected. When iterating on items, AnomalyDetection.items[index].direction can contain either HIGH or LOW. For example, AnomalyDetection.items[index].direction='HIGH' or AnomalyDetection.items[index].direction='LOW'. AnomalyDetection.direction can have an empty string for ALL. An example is AnomalyDetection.direction=''.
    • actualValue – The actual value of the data metric at the point of the anomaly or outlier.
      • value – The raw value.
      • formattedValue – The value formatted by the metric field.
      • formattedAbsoluteValue – The absolute value formatted by the metric field.
    • expectedValue – The expected value of the data metric at the point of the anomaly or outlier.
      • value – The raw value.
      • formattedValue – The value formatted by the metric field.
      • formattedAbsoluteValue – The absolute value formatted by the metric field.

Bottom Movers and Top Movers

The Bottom movers computation counts the requested number of categories by date that rank in the bottom of the dataset. For example, you can create a computation to find the bottom 3 printers based on printed impressions for a specific time period.

The Top movers computation counts the requested number of categories by date that rank in the top of the dataset. For example, you can create a computation to find the top 3 printers based on printed impressions for a specific time period.

To use these functions, you need at least one dimension in the Time field well and at least one dimension in the Categories field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Date - The date dimension that you want to analyze.
  • Category - The category dimension that you want to rank.
  • Value - The aggregated measure that the computation is based on.
  • Number of movers - The number of ranked results that you want to display.
  • Order by - The order that you want to use, percent difference or absolute difference.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • categoryField – From the Categories field well.
    • name – The formatted display name of the field.
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • startTimeValue – The value in the date dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • endTimeValue – The value in the date dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • itemsCount – The number of items included in this computation.
  • items – Bottom moving or top moving items.
    • categoryValue – The category value.
      • value – The value of the category field.
      • formattedValue – The formatted value of the category field. If the field is null, this parameter displays 'NULL'. If the field is empty, it displays '(empty)'.
    • currentMetricValue - The current value for the metric field.
      • value – The raw value.
      • formattedValue – The value formatted by the metric field.
      • formattedAbsoluteValue – The absolute value formatted by the metric field.
    • previousMetricValue - The previous value for the metric field.
      • value – The raw value.
      • formattedValue – The value formatted by the metric field.
      • formattedAbsoluteValue – The absolute value formatted by the metric field.
    • percentDifference – The percent difference between the current and previous values of the metric field.
      • value – The raw value of the calculation of the percent difference.
      • formattedValue – The formatted value of the percent difference (for example, -42%).
      • formattedAbsoluteValue – The formatted absolute value of the percent difference (for example, 42%).
    • absoluteDifference – The absolute difference between the current and previous values of the metric field.
      • value – The raw value of the calculation of the absolute difference.
      • formattedValue – The absolute difference formatted based on the formatting preferences specified for the metric field.
      • formattedAbsoluteValue – The absolute value of the difference formatted by the metric field.

Bottom Ranked and Top Ranked

The Bottom ranked computation finds the dimensions that rank in the bottom of the dataset. For example, you can create a computation to find the bottom 3 locations by printer throughput.

The Top ranked computation finds the dimensions that rank in the top of the dataset. For example, you can create a computation to find the top 3 locations by printer throughput.

To use these functions, you need at least one dimension in the Categories field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Category - The category dimension that you want to rank.
  • Value - The aggregated measure that the computation is based on.
  • Number of results - The number of ranked results that you want to display.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • categoryField – From the Categories field well.
    • name – The formatted display name of the field.
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • itemsCount – The number of items included in this computation.
  • items – Bottom ranked or top ranked items.
    • categoryValue – The category value.
      • value – The value of the category field.
      • formattedValue – The formatted value of the category field. If the field is null, this parameter displays 'NULL'. If the field is empty, it displays '(empty)'.
    • metricValue - The value for the metric field.
      • value – The raw value.
      • formattedValue – The value formatted by the metric field.
      • formattedAbsoluteValue – The absolute value formatted by the metric field.

Forecast

The Forecast computation uses machine learning to forecast future metrics based on patterns of previous metrics by seasonality. For example, you can create a computation to forecast total printer throughput for the next six months.

To use this function, you need at least one dimension in the Time field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Date - The date dimension that you want to analyze.
  • Value - The aggregated measure that the computation is based on.
  • Periods forward - The number of time periods in the future that you want to forecast. The supported values range from 1 to 1,000.
  • Periods backward - The number of time periods in the past that you want to base your forecast on. The supported values range from 1 to 1,000.
  • Prediction interval - An estimate of an interval in which future observations can fall, with a certain probability, based on what has already been observed.
  • Seasonality - The number of time periods involved in the predictable seasonal pattern of data during a calendar year. Ranges from 1 to 180. The default setting automatically detects the data seasonality.
  • Forecast boundaries - The boundaries that prevent forecast values from going above or below a specified threshold.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • metricValue – The value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • timeValue – The value in the date dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date field.
  • relativePeriodsToForecast – The relative number of periods between the latest date/time record and the last forecast record.

Growth Rate

The Growth rate computation compares values over time periods. For example, you can create a computation to find the 3-month compounded growth rate for printed impressions, expressed as a percentage.

To use this function, you need at least one dimension in the Time field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Date - The date dimension that you want to analyze.
  • Value - The aggregated measure that the computation is based on.
  • Number of periods - The number of time periods in the future that you want to use to compute the growth rate.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • previousMetricValue - The previous value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • previousTimeValue – The previous value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • currentMetricValue - The current value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • currentTimeValue – The current value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • timePeriods – The number of periods set in the computation properties.
  • compoundedGrowthRate – The percent difference between the current and previous values of the metric field.
    • value – The raw value of the calculation of the percent difference.
    • formattedValue – The formatted value of the percent difference (for example, -42%).
    • formattedAbsoluteValue – The formatted absolute value of the percent difference (for example, 42%).
  • absoluteDifference – The absolute difference between the current and previous values of the metric field.
    • value – The raw value of the calculation of the absolute difference.
    • formattedValue – The absolute difference formatted based on the formatting preferences specified for the metric field.
    • formattedAbsoluteValue – The absolute value of the difference formatted by the metric field.

Maximum and Minimum

The Maximum computation finds the maximum dimension by value. For example, you can create a computation to find the month with the highest printer throughput.

The Minimum computation finds the minimum dimension by value. For example, you can create a computation to find the month with the lowest printer throughput.

To use these functions, you need at least one dimension in the Time field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Date - The date dimension that you want to analyze.
  • Value - The aggregated measure that the computation is based on.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • metricValue - The value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • timeValue – The value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.

Metric Comparison

The Metric comparison computation compares values in different measures. For example, you can create a computation to compare two values, such as the actual printer throughput and the target throughput value.

To use this function, you need at least one dimension in the Time field well and at least two measures in the Values field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Date - The date dimension that you want to analyze.
  • From value - The measure that you want to compare.
  • Target value - The measure that you want to use as the comparison target.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • fromMetricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • fromMetricValue – The value for the metric field that you want to compare.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • toMetricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • toMetricValue – The value for the target metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • timeValue – The value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • percentDifference – The percent difference between the values of the compared metric fields.
    • value – The raw value of the calculation of the percent difference.
    • formattedValue – The formatted value of the percent difference (for example, -42%).
    • formattedAbsoluteValue – The formatted absolute value of the percent difference (for example, 42%).
  • absoluteDifference – The absolute difference between the values of the compared metric fields.
    • value – The raw value of the calculation of the absolute difference.
    • formattedValue – The absolute difference formatted based on the formatting preferences specified for the metric field.
    • formattedAbsoluteValue – The absolute value of the difference formatted by the metric field.

Period Over Period

The Period over period computation compares values from two different time periods. For example, you can create a computation to see how much printer throughput increased or decreased since the previous time period.

To use this function, you need at least one dimension in the Time field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Date - The date dimension that you want to analyze.
  • Value - The aggregated measure that the computation is based on.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • previousMetricValue - The previous value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • previousTimeValue – The previous value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • currentMetricValue - The current value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • currentTimeValue – The current value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • percentDifference – The percent difference between the current and previous values of the metric field.
    • value – The raw value of the calculation of the percent difference.
    • formattedValue – The formatted value of the percent difference (for example, -42%).
    • formattedAbsoluteValue – The formatted absolute value of the percent difference (for example, 42%).
  • absoluteDifference – The absolute difference between the current and previous values of the metric field.
    • value – The raw value of the calculation of the absolute difference.
    • formattedValue – The absolute difference formatted based on the formatting preferences specified for the metric field.
    • formattedAbsoluteValue – The absolute value of the difference formatted by the metric field.

Period to Date

The Period to date computation evaluates values for a specified period to date. For example, you can create a computation to find the year-to-date printed impressions.

To use this function, you need at least one dimension in the Time field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Date - The date dimension that you want to analyze.
  • Value - The aggregated measure that the computation is based on.
  • Time granularity - The date granularity that you want to use for the computation, such as year to date.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • timeField – From the Time field well.
    • name – The formatted display name of the field.
    • timeGranularity – The time field granularity ( DAY, YEAR, and so on).
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • previousMetricValue - The previous value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • previousTimeValue – The previous value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • currentMetricValue - The current value for the metric field.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.
  • currentTimeValue – The current value in the date/time dimension.
    • value – The raw value.
    • formattedValue – The value formatted by the date/time field.
  • periodGranularity – The period granularity for this computation (MONTH, YEAR, and so on).
  • percentDifference – The percent difference between the current and previous values of the metric field.
    • value – The raw value of the calculation of the percent difference.
    • formattedValue – The formatted value of the percent difference (for example, -42%).
    • formattedAbsoluteValue – The formatted absolute value of the percent difference (for example, 42%).
  • absoluteDifference – The absolute difference between the current and previous values of the metric field.
    • value – The raw value of the calculation of the absolute difference.
    • formattedValue – The absolute difference formatted based on the formatting preferences specified for the metric field.
    • formattedAbsoluteValue – The absolute value of the difference formatted by the metric field.

Total Aggregation

The Total aggregation computation creates a grand total of the value. For example, you can create a computation to find the total number of printed impressions.

To use this function, you need at least one dimension in the Time field well and at least one measure in the Values field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Value - The aggregated measure that the computation is based on.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • metricField – From the Values field well.
    • name – The formatted display name of the field.
    • aggregationFunction – The aggregation used for the metric (SUM, AVG, and so on).
  • totalAggregate – The total value of the metric aggregation.
    • value – The raw value.
    • formattedValue – The value formatted by the metric field.
    • formattedAbsoluteValue – The absolute value formatted by the metric field.

Unique Values

The Unique values computation counts the unique values in a category field. For example, you can create a computation to count the number of unique values in a dimension, such as the number of locations.

To use this function, you need at least one dimension in the Categories field well.

Parameters
  • Computation name - A unique descriptive name for the computation. You can use the default name or add a custom name.
  • Category - The category dimension that you want to analyze.
Computation outputs
Note: The items displayed in bold monospace font can be included in the narrative.
  • categoryField – The category field.
    • name – The display name of the category field.
  • uniqueGroupValuesCount – The number of unique values included in this computation.
Parent topic: Using Insights