By: Linda Rosencrance
A few months ago, Microsoft released its Quick Insight feature in Power BI that lets you automatically search datasets uploaded to Power BI for hidden insights.
Now Microsoft has released an easier way to access these advanced analytics by targeting specific dashboard tiles for discovering (as the name suggests) quick insights, according to a blog post by Patrick Baumgartner, principal program manager at Microsoft.
You need only view a dashboard tile in Focus mode, click “Get Insights,” and Power BI searches the tile and its related data for correlations, outliers, trends, seasonality, change points in trends, and major factors automatically, said Baumgartner. These are the types of hidden insights that a user typically wants but, may lack the time or know-how to query. Or, they answer useful questions that (as often happens with BI) a user has simply not thought to ask.
The Quick Insights feature is built on a growing set of advanced analytical algorithms developed together between the Power BI team and Microsoft Research, Baumgartner said.
“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,” he said.
To try out the scoped Quick Insights capability, go to In Focus mode on a dashboard tile for data loaded into Power BI and select “Get Insights.” Baumgartner said that Power BI Quick Insights will then scan the data related to the tile and display a list of potential insights for you to explore further.
If you want to get more information about a specific data point, you can select data in the visual and Quick Insights will focus on that data point when it searches for insights, he said.
Some of the insights you can uncover in your data include:
- 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 (see graphic).
- 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.
- 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.
- Seasonality in time series-Finds periodic patterns in time series data, such as weekly, monthly, or yearly seasonality.
Baumgartner said Microsoft is looking for feedback from users so it can continue to hone its algorithms and experiences to make discovering insights and getting answers even easier and faster.