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Understanding Sisu's Analysis Types

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Sisu's Explorations and Key Driver Analyses provide valuable tools for understanding what is happening with your data and what is driving changes in performance.


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Explorations and Key Driver Analyses (KDAs)

Sisu provides two basic ways to get answers from your data:

  • Key Driver Analyses
    In Key Driver Analyses,Sisu uses its proprietary algorithms to identify subgroups that drive metric performance and calculate relevant statistics for each subgroup if they are statistically significant.


    Use Key Driver Analysis when you want to understand WHY your data changed.

    Refer to Understanding Key Driver Analyses for more information.


  • Explorations
    Sisu Explorations enable you to slice and dice your data manually. You can select the metric or dimension you want to explore, add or pivot by dimensions, and define filters—all without any SQL. You can then run the Exploration and visualize the output in table format and as various graph types that can be added to a dashboard and shared with other team members.

    tip_icon.png Use Explorations when you want to understand WHAT is happening to your data. As you are exploring your data in an Exploration, you can automatically create a Key Driver Analysis for any segment of interest to learn what drivers are affecting that segment.

    Refer to Understanding Explorations for more information.


About Sisu’s KDAs

Sisu’s three Key Driver Analysis (KDA) types described in this article in use our algorithms to explore your data and provide you with valuable insights into your data that you would not otherwise be able to easily discover on your own. 

info_icon.png KDA types are described further in Understanding Key Driver Analyses.

The insights Sisu provides can lead you to impactful decisions, enabling you to take advantage of hidden opportunities and mitigate risks to your organization.

To do this, Sisu identifies subgroups that drive your key metric and calculates relevant statistics for each. Sisu then  uses our algorithm to determine which subgroups are statistically significant. 

info_icon.png Statistical significance is driven by the potential impact the information from the data combination will have on possible outcomes in your business. For more information, refer to Sisu 101.


In all KDA types, Sisu will show you:

  • The size of each subgroup, as a percentage of your total dataset
  • The metric’s value in each subgroup
  • The relative impact of each subgroup
  • And much more