SAP Analytics Cloud (SAC) Feature Highlight: Smart Insight for Variances
I am sure most of you have seen already the Smart Insight feature in SAP Analytics Cloud, but let me provide you a very short introduction before we are looking at the new option to use Smart Insight in combination with variances.
Lets say we have two charts (see below).
The left chart shows the actual and budget sales by product category and on the right hand side we see the shipping costs broken down by province in Canada.
In case we would want to find out more about our actual sales number – for example for book cases, we can select the item directly in the chart and use the Smart Insight option (light bulb).
After the data has been “inspected” we are presented with several facts – in our case for the sales numbers on the Bookcases.
Using the Smart Insight option, we can now quickly identify the top contributing province as well as the Customer Segment and we can use the shown findings to navigate to the different details for each of them.
Until now the Smart Insight option was not available for Variances, so you couldn’t find out for example what are the details behind the difference of your actual sales and your sales forecast.
This is now possible.
So let’s go back to the chart showing our sales numbers and find out what the details are behind the variance for the Office Machines.
Similar as before the system will present us with the key findings, but this time focused on the variance that we selected.
Now if you look at the chart shown above you can identify very quickly – basically with a single glance at the data – that you have 3 customer segments that are below the budget and that you have one particular customer segment that is driving this variance – Corporate.
So not only are we shown the key influences for the data, but we are also shown the variances as part of the detailed chart so that we can quickly identify the positive as well as the negative influencing factors.
If we now go through the next Smart Insight presented to us, we can quickly identify that the Jumbo Box is responsible for nearly 50% of the change and that the region Prarie is responsible for close to 50%.
So not only were we able to identify the details behind our sales revenue for a specific product category, but with a few clicks were we able to identify the root cause behind the variance for our actual and budget sales and we are now able – with the additional new learnings – to outline the next steps and perhaps we can leverage things from the team in region Prarie in other regions as well to increase our revenue.