Smart Insights uncovers top contributors of a selected value or variance point. To run Smart Insights, click a data point on a chart to display the quick-action menu and select the light bulb symbol.
Smart Discovery identifies the key influencers of a selected measure or dimension. To run a Smart Discovery, enter data-view, select a data model and choose the dimension or measure you’re interested in exploring.
But what’s the difference between Top Contributors and Key Influencers?
While “contributors” and “influencers” sound very similar, how they’re calculated and the value they provide to the business is very different.
Smart Insights and Top Contributors
Top Contributors refer to the dimension members that provide the highest contribution to the data point being analyzed. The Smart Insights feature answers the question “what are the top contributors to the data point or variance selected in this chart?”
To answer the question, machine learning calculations run on information that is of the same nature as the selected data point. For example, if the selected data point is volume, the top contributors are based on volume.
Without Smart Insights, a business user would have to manually pivot the data to identify the members from each dimension that contribute most to the data point. This makes Smart Insights a major time saver for business users looking for quick answers to a particular value.
Smart Insights use-case example
In this case, we want to use Smart Insights to explain the variance in net revenue of sales for a sports clothing company over two time periods.
By running Smart Insights we quickly see that the variance is caused by a drop in athletic shorts sales. The Smart Insights give us the details we need to investigate further.
Smart Discovery and Key Influencers
Key influencers are dimensions that influence results; they are identified from the information in your selected model using classification and regression techniques. Classification techniques are used to identify dimensions that segregate results into different groups of outcomes. Regression techniques identify relationships between data points in order to predict future outcomes.
Smart Discovery use-case example
In this case, we’ll use our sales data to run a Smart Discovery on a sales dataset.
Right away, Smart Discovery helps a user to understand the significance of key influences on the selected measure or dimension. Then, by selecting key influencers to explore, you’re able to examine them in greater detail and analyze the intersection of two influencers using a heat map.
Next, Smart Discovery identifies unexpected values using a predictive algorithm that calculates the difference between expected and actual results.
With Smart Discovery, users can simulate how the predicted value may change in different scenarios. This helps the user understand sensitivities around the influencers and predict a future outcome.
Comparison Table and Summary
Smart Discovery and Smart Insights help users to take advantage of advanced contribution, classification, and regression techniques with the power of machine learning. The features empower anyone to surface hidden patterns and complex relationships within their information, even without any data science knowledge or experience.
Together, they are powerful machine learning capabilities that help businesses make faster, better decisions with SAP Analytics Cloud.