Jérôme Ivain

Jérôme Ivain

Jérôme is a Product Manager for SAP Analytics Cloud's Smart Assist feature portfolio. He holds an Engineer Diploma in Data warehousing and a certification in Data Sciences from Polytechnique School in France. Jérôme has 10 years of experience in the software industry in a variety of roles, from Software Developer to Product Management. When not at work, Jérôme enjoys playing drums and sports like rock climbing and triathlon.

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Smart Insights and Smart Discovery are two powerful machine learning features in SAP Analytics Cloud. In this post, we'll explore the differences between the features and show how you can use them to drive your business forward.

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.

Running Smart Insights

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.

Running a Smart Discovery

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 top contributor to the net revenue of sales in Q4, 2018 for a sports clothing company.

By running Smart Insights we quickly see that our Southeast region is the top contributor to our sales.  Now we have the details we need to investigate further and see why this region is so successful.

Smart Discovery and Key Influencers

Key influencers are measures and 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.

Smart Discovery Key Influencers and Heat Map

Next, Smart Discovery identifies unexpected values using a predictive algorithm that calculates the difference between expected and actual results.

Smart Discovery Unexpected Values

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.

Smart Discovery Simulation


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.

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