Decision dataset

  • Updated

A decision dataset integrates your experimentation data and helps you centralize and analyze your experiment results, along with additional information in your data warehouse. 

  1. Go to Data > Datasets > + New Dataset > Source Dataset.

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  2. Select a data table on the Pick a source page and click Save.

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  3. Select Decision stream from the drop-down list in the Semantics tab.

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Decision stream

Captures decision events such as experiment bucketing. If Decision Stream is chosen, you must configure the following fields:

  • Actor dataset – The actor corresponding to whatever identifier is used for experiment variation decisions.
  • Experiment ID – The experiment ID used by Optimizely. ID is preferred, but if it is not available, it can be left blank if the experiment name used by Optimizely is populated.
  • Experiment name – The experiment name used by Optimizely.
  • Variation ID – The variation ID used by Optimizely. If the variation ID is unavailable, you can use the same picker to select the variation name, and it works automatically.
  • Is holdback – The definition of a group that must be excluded from all experiments you are performing so that you can compare how this group performs on specific metrics as compared to other visitors who took part in the experiment.
  • Timestamp – The time at which the decision was made. This field is required.
  • Custom partition time column – The option that optimizes time-based queries by mapping your event time to a warehouse partition column. When enabled, time filters in queries apply precisely to the event timestamp and loosely to the partition column (with a configurable skew), improving performance without missing relevant data. Select the column and set the time skew before and after the event time.

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