Detect - Settings
Here is the different settings available in DataMa Detect
Scope for detection:
- Detection Method
- Threshold value
- Confidence Interval
- Points Included in Baseline
- Number of Points in Baseline
Find here the details of the main settings DataMa Detect
Scope for detection
Analyse All Metrics Relation Steps
DataMa Detect can anlyse one step or all the steps of your funnel. When all step are selected, you will be able to see in a dropdown menu every step with an anomaly.
Inspect Numerator only
When you’re analysig all metrics relation steps, you can choose to analyse only the numerator. This is useful when your market equation is not important or not relevant and you prefer to analyse independently all the Numerator.
Analyse Latest Point Only
You can choose to display the anomalies of each date or only for the latest point in the data. In run mode (i.e. when sending alerts on a daily basis for instance), we recommand to activate this parameter, to avoid having all the anomalies raised.
Detection method defines which algorithm DataMa uses to flag anomalies. Please refer to the detection method page to learn more
Threshold is used by some anomaly detection methods to define the cutoff between anomalies and normal points. The threshold will be considered as % or absolute value depending on the method. Please refer to the detection method page to learn more
A confidence interval is a range of values that is likely to contain an unknown population parameter. If you draw a random sample many times, a certain percentage of the confidence intervals will contain the population mean. This percentage is the confidence level.
By changing the confidence interval you can modify the precision expected from the significance. Please refer to the detection method page to learn more
Points included in baseline
This allows you to define which type of points (same day, same hour etc) in your historical data the algoritm will consider for baseline. Please refer to the detection method page to learn more
Number of points in baseline
This allows you to define how many points within the defined type of points in your historical data the algoritm will consider for baseline. Please refer to the detection method page to learn more
This is the level of analysis in the explanation part of the solution. How many detail will be given in the comment.
Segment text complement
Segment text complement allows to decide on what to display behind Segment name in the waterfall labelling. As a reminder, a segment is a attribute within a dimension of your dataset. Options for this input are:
- Variation relative to average (e.g. “x2.43”) - this is the default value and allows to spot easily “abnormal” variations way above average (i.e. above 1)
- Percentage Variation (e.g. “-46%”)
Aggregation in %
The level of aggregation that the model is using – e.g. if Level of aggregation is set at X%, segment within each dimension that represents less than X% of the Primary Numerator (e.g. Revenues) of the main KPI you’re analyzing will be clustered in one « Other » segment. X is set at 2 by default, but you may want to play with this parameter quite a bit because it can change significantly the calculation of mix effects.
Click on the drop-down arrow to display the settings menu Move the cursor to the right to increase the level of aggregation Click on « Pivot » to get results Segments are now aggregated at the requested level
DataMa Pivot can be used with a comparison. To activate this mode select a dimension and the element you want to compare. Each graph and the comments will be in a Compare mode reflecting the difference of the elements you are comparing.
In DataMa Detect, you can detect the anomaly of only one KPI or all metric relation.
By default, DataMa selects the first ratio of your metric relation, but you can change that by clicking on the KPI bar in the subheader and ticking the appropriate metric within your metric relation