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Germain UX offers different types of SLAs (Service Level Agreements) that can be applied to various KPIs, whether they are preconfigured or custom, and whether they are related to business or technical metrics. Here are the different types of SLAs available in Germain UX:

Fact-Based SLA

This type of SLA evaluates individual data points or facts. It allows for the evaluation of specific metrics against predefined thresholds or targets.

Statistical SLA

Statistical SLAs evaluate collections of data points or facts. Several types of statistical SLAs are available:

Aggregate SLA

It evaluates a specific period of time and compares it to a constant threshold. This type of SLA is useful for monitoring trends or performance over a specified time frame.

Color Percentile SLA

This SLA evaluates a period of time and compares the percentile of data points in the GREEN range to a constant threshold. It helps identify if performance is within an acceptable range.

Percent Change SLA

It evaluates a period of time and compares it to a prior period, measuring the percentage change. This SLA is useful for monitoring performance improvements or regressions over time.

Predictive SLA

This SLA creates predictions based on past data points or historical trends. It helps in forecasting future performance or identifying potential issues based on predictive analytics.

Smart Insight SLA

This SLA automatically identifies trends that are considered abnormal or noteworthy. It evaluates a business period of time (e.g., year-end, quarter-end, month-end, specific weekdays) and compares it to other similar business periods. Smart Insight SLAs provide alerts or notifications for significant deviations or insights.

These SLAs help organizations set performance benchmarks, monitor adherence to targets or thresholds, and gain insights into trends and predictive analytics. By utilizing different types of SLAs, Germain UX enables users to evaluate and track KPIs effectively, both in real-time and historically.

Examples of smart insights in Germain UX include predictions about disk usage exceeding a certain threshold, changes in user errors volume compared to previous periods, sales forecast accuracy predictions, and predictions of visitor increases in eCommerce by month-end.

In summary, Germain UX provides a range of SLA types, including fact-based and statistical SLAs, to monitor and evaluate KPIs. These SLAs enable organizations to set performance targets, identify trends, and gain predictive insights. The Smart Insight SLAs offer automated detection of abnormal trends, providing valuable information for proactive monitoring and decision-making.


Via Wizard

Go to Germain Workspace > Left Menu > Wizard > SLA

Via SLA Screen

Go to Germain Workspace > Left Menu > Analytics > SLA

Predictive SLA for your KPI

How-to enable Predictive SLA for your KPI

  • Log on to Germain UX > Left Menu > Wizards

  • Select the KPI for which you want to configure a predictive SLA (selection is disabled if you came from KPI View)

  • Select the “Predictive-Curve” SLA type

  • Specify the details of the value and timeframe you want to predict

  • Select the alert template and action(s) you want the system to execute if the previous SLA check fails

How to enable Smart Insights for your KPI

  • Germain UX > Left Menu > Automation > Alerts > analytical-sla-alert


  • Germain UX > Left Menu > Analytics > SLA > Smart Insights SLA - Day


In addition to receiving individual email alert whenever a kpi is exceeding SLA, you can receive a summary email once per day that summarizes all the kpi that have exceeded SLA that day.

Germain UX’ Smart Insight engine can be enabled for any of the KPIs that are active in your system. KPIs can be Technology, Process or User Behavior-specific.

Example: Smart Insights Summary Email that is sent once per day.



FactBasedSLA Context

The context object available from a FactBasedSLA has the following structure:

  • sla (Object)

  • threshold (Object)

  • kpi (Object)

  • fact (Object)

  • value (Double)

  • resource (String)

  • violation (boolean)

  • violationCount (int)

More info on the structure of these objects is available below. For example, if you have a LocalActionExecutor that executes a command when an SLA is breached, you can reference the variables above inside your command or arguments follows:



Statistical SLA Context

The context object available from a StatisticalSLA has the following structure:

  • sla (Object)

  • kpi (Object)

  • value (Double)

  • resource (String)

  • values (Map<String, Double>)

  • timestamp (Object)

  • result (Object)

More info on the structure of these objects is available below. These variables can be accessed in the same way as above.

SLA Object

The above objects have the following structure:

                name (String)
                color (Object)
                constantValue (String) – this is the SLA threshold
                operator (Object)
                thresholdValue (Double)
                periodCount (Integer)
                periodGranularity (Object)
                rank (Double)
                operator (Object)
                thresholdValue (Double)
                periodCount (Integer)
                periodGranularity (Object)
                operator (Object)
                thresholdValue (Double)
                periodCount (Integer)
                periodGranularity (Object)
                baselineGranularity (Object)
                operator (Object)
                thresholdValue (Double)
                periodCount (Integer)
                periodGranularity (Object)
                dataGranularity (Object)
                extrapolateValue (boolean)

Threshold Object:

name (String)
color (Color)
thresholdValue (Double)
thresholdExpression (String)

KPI Object

Name (String)
Description (String)

Fact Object

see documentation

Result Object

CurrentInterval (Object)

start (Object)

end (Object)

baselineInterval (Object)

start (Object)

end (Object)

measure (String)

unit (String)

Service: Analytics

Feature Availability: 8.6.0 or later

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