Types of insights supported by Power BI

How does Insights work?

Power BI quickly searches different subsets of your dataset while applying a set of sophisticated algorithms to discover potentially-interesting insights. Power BI scans as much of a dataset as possible in an allotted amount of time.

You can run insights against a dataset or dashboard tile.

What types of insights can we find?

These are some of the algorithms we use:

Category outliers (top/bottom)

Highlights cases where, for a measure in the model, one or two members of a dimension have much larger values than other members of the dimension.

Category outliers example

Change points in a time series

Highlights when there are significant changes in trends in a time series of data.

Change points in time series example

Correlation

Detects cases where multiple measures show a correlation between each other when plotted against a dimension in the dataset.

Correlation example

Low Variance

Detects cases where data points are not far from the mean.

Low Variance example

Majority (Major factors)

Finds cases where a majority of a total value can be attributed to a single factor when broken down by another dimension.

Major fators example

Detects upward or downward trends in time series data.

Overall trends in time series example

Seasonality in time series

Finds periodic patterns in time series data, such as weekly, monthly, or yearly seasonality.

Seasonality example

Steady share

Highlights cases where there is a parent-child correlation between the share of a child value in relation to the overall value of the parent across a continuous variable.

Steady share example

Time series outliers

For data across a time series, detects when there are specific dates or times with values significantly different than the other date/time values.

Time series outliers example

Next steps

Power BI insights

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