Several months ago we released our first Quick Insights capability in Power BI allowing you to automatically search datasets uploaded to Power BI for hidden insights. Today, we’ve taken another step in making it even easier to use these advanced analytics by allowing you to target specific dashboard tiles for discovering quick insights. Simply view a dashboard tile in focus mode, click Get Insights, and Power BI will search the tile and its related data for correlations, outliers, trends, seasonality, change points in trends, and major factors automatically, within seconds.
For example, here Quick Insights found outliers in a time series for average flight delays...
The Quick Insights feature is built on a growing set of advanced analytical algorithms developed in conjunction with Microsoft Research. We're excited to continue building this functionality into more places in Power BI to allow more people to find insights in their data in new and intuitive ways. To try out the scoped Quick Insights capability today, go to ‘in focus mode’ on a dashboard tile for data loaded into Power BI…
Then select ‘Get Insights’…
Power BI Quick Insights will now scan the data related to the tile and display a list of potential insights you may want to explore further. To drill into a specific data point, you can even select data in the visual and Quick Insights will focus on that data point when searching for insights. Here are some of the insights we can uncover in your data:
| Major factor(s)
Finds cases where a majority of a total value can be attributed to a single factor when broken down by another dimension. |
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| 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. |
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| 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. |
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| Overall trends in time series
Detects upward or downward trends in time series data. |
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| Seasonality in time series
Finds periodic patterns in time series data, such as weekly, monthly, or yearly seasonality. |
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| 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. |
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| Correlation
Detects cases where multiple measures show a correlation between each other when plotted against a dimension in the dataset. |
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