SAP Data Science

Self-service Data Science in SAP Analytics Cloud

SAP Analytics Cloud (SAC) has data science algorithms built-in that can allow non-data science users to perform advanced modeling. Predictive analytics remains a key advanced analytics approach among various analytical approaches. In this blog, we will explore the smart predict functionality of SAC and understand how expert, non-data scientist users can leverage them to build predictive analytics models.

Self-service Data Science in SAP Analytics Cloud

SAP Analytics Cloud (SAC) has data science algorithms built-in that can allow non-data science users to perform advanced modeling. Predictive analytics remains a key advanced analytics approach among various analytical approaches. In this blog, we will explore the smart predict functionality of SAC and understand how expert, non-data scientist users can leverage them to build predictive analytics models.

What are predictive analytics algorithms?

A simplified explanation of predictive analytics is that it is a form of advanced analytics that helps make future predictions based on historical data. Predictive analytics models do so by leveraging a combination of statistics, data mining, and machine learning (ML) algorithms.

SAP Analytics Cloud Smart Predict helps perform self-service data science by using the power of augmented analytics. Augmented analytics is a term assigned to a collection of features enabled by artificial intelligence (AI) and ML that perform some complicated tasks in order to allow users to perform advanced analytics they would not have been able to perform by themselves. Smart Predict allows the users to build advanced models, including ML algorithms, with a few simple clicks. As SAP puts it: “The focus is on the business questions, not algorithms, which helps speed the prediction and recommendation process”.

Overview of the Smart Predict Process

Select the model algorithm: When you open a new predictive modeling scenario, you get the option to choose the appropriate algorithm. Three key options are available which are listed below:

• Classification
• Regression
• Time Series

You can select the model based on the business question that you are trying to answer. For example, will a particular customer default on a credit card payment? In a subsequent blog, Once you chose an algorithm, the underlying augmented analytics functionalities will present to you an interface that you can use to:

• Select the underlying data source
• Select the variables in your data that you believe are relevant for your analysis
• Select the variable roles, like the variable that you want to predict (target variable) and the predictors, dates, etc.

Smart Predict then trains on the selected data and builds a predictive model that aligns best with the underlying data, variables, and other parameters selected. Training, parameter setting, and optimization are all taken care of by Smart Predict. It will then present to users an output report, along with some form of performance indicator of the model. The performance indicator will vary by model type.

Here are a couple of resources that you can leverage to gain depth in the topic of augmented analytics:

Bring the Power of Machine Learning Directly to Business Users

Make Smarter Business Decisions