SAP Machine Learning: What You Should Know

Find out what is SAP machine learning and how integrating machine learning into SAP makes sense.

SAP is one of the top enterprise resource planning (ERP) systems in the world today. SAP applications deliver high-end business intelligence (BI) across multiple platforms to streamline operations by allowing all employees to use the same system. In recent years, SAP’s BI offerings have become even more powerful, thanks to the introduction of artificial intelligence (AI) and machine learning (ML).

Let’s learn how machine learning enhances SAP products and how you can employ these powerful tools in your business.

What is Machine Learning?

While many people frequently use the terms AI and ML interchangeably, they are not the same.

Artificial intelligence is an overarching term for the process of replicating human intelligence and decision-making processes in machines.

Machine learning is a set of algorithms dealing with adaptive training of data analytics models and subsequent decision-making processes.

Fundamentally, ML models use their input data and their actions and experience to improve performance and accuracy without human intervention iteratively. Numerous specific machine learning algorithms can be used for everything from trend analysis to recommendations.

Machine learning helps improve the performance of many well-known and widely used services. Whether machine learning is employed in stock market analysis, targeted advertising, personalized movie or book recommendations, or natural language processing (NLP) in digital personal assistants, it’s silently permeating many aspects of our daily lives.

Companies are applying machine learning internally to enhance security, automate tasks, and perform trend analysis. With more than 50% of business devices now mobile, safety is a critical concern for businesses. Forward-looking predictions, which ML algorithms are built for, are a great way to augment traditional strategic planning.

Integrating machine learning into SAP data analysis was a natural choice, given the increasingly large data sets company’s host in SAP systems. Several SAP modules now rely heavily on machine learning to enhance data analytics, predictive analytics, and decision making.


SAP HANA Cloud is a cloud-native database as a service (DBaaS). It allows SAP users to build applications based on aggregate data from multiple sources. HANA Cloud can access data from within other SAP modules and from remote sources such as Amazon Athena, Google BigQuery, and Spark SQL.

SAP HANA Cloud comes with machine-learning augmented analytics, including the Automated Predictive Library (APL) and the Predictive Analytics Library (PAL). While APL and PAL have similar functionalities, it is essential to keep in mind that PAL requires knowledge of SQL scripting language.

Automated Predictive Library

APL comprises a set of functions allowing users to build predictive data mining models to answer business questions based on company data. Five predictive algorithms are available in APL:

  1. Classification algorithms assign input data to predefined categories or labels. Classification algorithms can help build your customer profiles, for example, by separating customers by demographics.
  2. Regression algorithms predict future values based on input data. Regression analysis is helpful for general business forecasting.
  3. Clustering algorithms, such as classification algorithms segregate data into groups. However, clustering models do not use predefined groups but instead group data by similar characteristics. Clustering is another way to analyze your customer base by their behavioral tendencies rather than predefined categories.
  4. Time series analysis algorithms analyze the relationship between data points collected over time and predict future values accordingly. A classic time series analysis is stock price prediction.
  5. Recommendation algorithms use past data to provide recommendations for future actions. Targeted advertising is a widely known example of a recommendation model.

APL is helpful for functions like forecasting, generating recommendations, determining data influencers, and data scoring and profiling.

Predictive Analytics Laboratory

Although PAL also provides the models built into APL, it can do far more. PAL includes additional machine learning algorithm models:

  • Association algorithms attempt to define the dependencies among points in a data set. For this reason, association models can help predict customer behavior.
  • Preprocessing algorithms help refine data sets by eliminating excess and limited-utility data, filtering out the noise, and normalizing data before other models analyze the data.
  • Numerous statistical algorithms are available in PAL to process data, including goodness-of-fit tests, cumulative distribution functions, multivariate analysis, and T-tests.
  • Social network algorithms are an increasingly popular tool. Algorithms in PAL include link prediction and page rank analysis.

 PAL also includes what it calls “miscellaneous” algorithms, which aid in classification, data visualization and dimensionality reduction, and data scoring.

SAP Analytics Cloud

The SAP Analytics Cloud is an all-in-one analytics platform encompassing business intelligence, predictive analytics, planning, and forecasting. SAP Analytics Cloud relies on ML to help users mine data across the company for trends and patterns that can form sound, forward-looking business decisions.

High-performance analytics have limited value if their data cannot be translated into easily understandable business information. SAP Analytics Cloud includes powerful, configurable dashboards for analytics reporting. SAP Analytics Cloud comes with the digital boardroom for the C-suite, presenting historical data and future predictions to support company-wide strategic planning.

To make things easier to use, SAP Analytics Cloud users build queries with natural language processing. SAP Analytics Cloud returns real-time results in easily understandable formats, improving the accuracy and speed of market intelligence and business intelligence analysis.

NLP allows decision-makers of all skill levels to quickly extract the data they need without waiting for data scientists or IT personnel responses. Of course, decision-makers can also work with data scientists to build highly sophisticated analytical algorithms. SAP Analytics Cloud makes it easier than ever for less experienced users.

SAP Intelligent Robotic Process Automation

Automation is a critical component of efficient business processes. With SAP Intelligent Robotic Process Automation (RPA), companies can create bots that perform repetitive manual tasks, replacing the need for employees to spend time on them.

SAP Intelligent RPA has uses in all areas across the company, from cybersecurity and operations to R&D to HR and accounting. SAP RPA includes machine learning capabilities that allow the bots to learn from their previous actions and their data to improve their decision-making and performance iteratively.

By 2023, the RPA services market is expected to reach more than $12 billion. A wave of investment in Intelligent RPA seems to forecast the continued growth of these solutions in business and beyond.

SAP Conversational AI

SAP is applying artificial intelligence and machine learning to enhance customer experience and employee engagement through intelligent chatbots. Conversational AI chatbots are becoming all the rage because they are easy to develop and easy to use.

The chatbot development engine is low code, relying on NLP and logic modules to quickly deploy bots. Built-in machine learning enhances the chatbot’s ability to analyze NLP inputs from users, whether customers or employees.

One of the goals of your business intelligence efforts is likely retaining customers. Existing customers may need help at all hours of the day, and they often want to find their answers online rather than using a helpline. ML trains the NLP of Conversational AI, allowing it to provide 24/7 access to your customers without having to have staff available all day, every day. And even if Conversational AI cannot fully answer a customer’s question, the customer still feels valued.

Companies can also use Conversational AI to help automate everyday employee activities, including human resources functions such as expense reimbursements or vacation requests.

Additionally, Conversational AI can facilitate communication between employees and management, providing corporate updates and addressing employee feedback.

This AI-based communication automation could also significantly improve threat detection for malware and other security threats by streamlining security processes. With the importance of cybersecurity in marketing, these innovative uses of AI will be increasingly critical.

Have SAP Machine Learning Work Hard for You

If you are an SAP customer, you are probably already using machine learning without realizing it. Whatever your skill level, SAP’s machine learning-augmented business intelligence tools can help you quickly digest massive amounts of SAP data and turn it into actionable insights. In the era of big data, your business can move beyond traditional ERP and outperform competitors. To learn more about SAP, visit our SAP insights page, where you can access SAP whitepapers, webinars, data sheets, and much more.