Difference between revisions of "Scorecard View from a Pivot"

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A pivot view is a great tool for calculating the main indicator for a scorecard.
 
A pivot view is a great tool for calculating the main indicator for a scorecard.
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You can create a scorecard from any value on a pivot as long as the pivot does not have animation (page-level).
  
 
==An Example==
 
==An Example==

Revision as of 21:44, 3 February 2016

Introduction

A pivot view is a great tool for calculating the main indicator for a scorecard.

You can create a scorecard from any value on a pivot as long as the pivot does not have animation (page-level).

An Example

In this example, we'll start with a pivot that calculates US Sales as the percent of total for this month.

Us sales percent pivot.png

To create a scorecard, we simply click on the value (60.83%) and select "Create a scorecard view from this cell".

Scorecard from pivot cell.png

We give the new scorecard a name. The name will also be its title.

Scorecard from pivot cell2.png

The result is this scorecard

Scorecard from pivot cell3.png

The scorecard is showing the current value for this month.

Adding Trend to the Scorecard

Continuing with our example, we can instantly add trend information to the timeline using the "Add Trend" button. You can see the "Add Trend" button in the previous image.

Using the Track Trend dialog, we can select a monthly frequency for the trend. Next, we're prompted to add the trend to the scorecard. Click "Yes".

Add trend.png

The result is shown next.

Scorecard from pivot cell4.png

And after a few months of data accumulation, it might look like this:

Scorecard from pivot cell5.png

Important Note

The resulting scorecard in our example is based on two views. The main indicator is based on the pivot view and the sparkline is based on a new timeline view that's using data that's collected in the trend table in Explore Analytics.

The secondary indicator as well as the up/down indicator calculate change by taking the main indicator value (the current value for this month) and comparing it against the last (most recent) trend data (last month).