Troubleshoot unexpected Results
In this article you can learn how to use some quick tricks to analyze possible deviations of the displayed data from your expectations.
Sometimes a chart or a table shows you data and figures that surprise you. Luckily its very easy with Valsight to get to the ground of the calculation and understand how a number was calculated.
We collected some tips & tricks to catch the most common problems.
1. Look into the details
If you are not really sure were an error comes from, try to dig as deep as possible. For example, if your error is in a high level sum, check the underlying nodes. If single a node value seams off, try to check the product groups for example. A good way of doing this is by looking at the driver tree or an assumption bridge.
2. Quick Modeling and Calculation Check
If your supposed error is shown in a chart or a table, sometimes it makes sense to add a chart showing the driver tree next to it. This way, you can quickly check if there are errors in the model's formulas explaining the wrong number.
Also, you can check for example if the aggregation type for the specific nodes are correct. Per default, the aggregation type "sum" is selected. However in the case of percentages, it often makes sense to use the type "average" for a more understandable display of the figure. In this particular case, the model will still calculate the higher nodes correctly, but the seemingly wrong precentage value might be confusing.
If you use the model tree to trace your figures, check that the scenarios and the years you are comparing match.
3. Check if you are in the right scenario and what kind of assumptions are active in this scenario.
Sometimes, differences in the value that is shown to you by the tool and what you were expecting might be caused by the assumptions defined for certain scenarios. An easy way to check this, is by creating an assumption bridge for the node with the differing value. In the assumption bridge you can see all the assumptions affecting the nodes and trace whether the perceived discrepancy comes from one of them.
If you still can't explain the variation in your data, don't hesitate to contact us!