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Auto-Explain analyzes data variations and identifies root causes. This page describes what a single analysis can currently handle, so an analysis can be set up to succeed on the first attempt. It assumes familiarity with the Auto-Explain user guide.

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Atoti Intelligence Essentials

This is part of the Atoti Intelligence Essentials offer.

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What does a single analysis cover?

Each Auto-Explain run is scoped to one focused comparison:
  • Compares one measure between two specific members (for example, two dates) on a single comparison level
  • Explains one measure at a time; comparing several measures requires several separate runs
  • Requires the two compared cells to be identical except for the level being compared (for example, the date)
  • Uses individual cells, not ranges of dates or values
  • Stays within a single cube; a cause in a different cube is out of scope for the run
  • Context values (settings that affect how a query is computed, such as a currency conversion) can be included in the comparison, alongside the cube’s regular hierarchies
  • Searches the cube’s regular hierarchies only, not virtual hierarchies (lightweight hierarchies whose members are not stored in the cube)

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What does an Auto-Explain run require to succeed?

An analysis needs a complete, valid starting point:
  • The measure is numeric and has a value at both compared cells
  • The measure and the cube exist and are valid
  • The measure works with common aggregations, such as SUM, COUNT, AVERAGE, and MULTIPLY. Percentile and median measures are not yet supported

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How does Auto-Explain search for causes?

Auto-Explain drills down automatically through the cube’s regular hierarchies to find the members that are most likely to explain a variation. A few behaviors shape how that search proceeds:
  • Hierarchies are ranked by their number of members at each step, and only a limited number of the smallest ones are explored. A very large hierarchy that holds the true driver can occasionally be skipped as a result. Apply filters to narrow the search if this seems to be happening.
  • Whoever sets up Auto-Explain for the project can narrow or exclude specific hierarchies from the search, to focus the analysis on relevant dimensions
  • Virtual hierarchies (lightweight hierarchies whose members are not stored in the cube) are never explored
  • Sometimes, the search encounters a hierarchy that is correlated with the variation but is not the true cause. In this case, Auto-Explain continues to drill down. The real driver is surfaced alongside the correlated cause.
If certain hierarchies are consistently irrelevant, or if it seems that the true driver lies in a large hierarchy that got skipped, the search can be narrowed or adjusted through configuration. See Set up Auto-Explain for details. Please talk to the person who manages Atoti in your organisation for more details if necessary.