SAP Analytics

Analytics pertains to leveraging data generated and captured across the organization to generate insights that can help transform the way organizations run. Analytics can also  help build capabilities that can automate many aspects of day-to-day decision making that is currently performed manually. There are three main categories of analytics leveraged in organizations today:

Business Intelligence (BI)

Analytics pertains to leveraging data generated and captured across the organization to generate insights that can help transform the way organizations run. Analytics can also  help build capabilities that can automate many aspects of day-to-day decision making that is currently performed manually. There are three main categories of analytics leveraged in organizations today:

Business Intelligence (BI)

The end-to-end process of BI involves analyzing the data generated by businesses, transforming the data into insights, and leveraging those insights to make optimal decisions. BI tools primarily leverage “descriptive analytics,” because these tools traditionally focus on analyzing current and historical performance based on data generated by the enterprise.

Machine Learning

Machine learning (ML) is a subset of artificial intelligence (AI) algorithms. The differentiating aspect of these algorithms is that they can learn from the input data and modify the model based on changes in that data. It is this “learning” aspect that makes these algorithms powerful.

Artificial Intelligence

In simple terms, Artificial Intelligence (AI) refers to systems or solutions that can replicate human decision-making capabilities. These solutions often leverage a combination of software and hardware to mimic human capabilities like problem -solving and decision making

Key Considerations for SAPinsiders

SAP applications leverage AI and ML algorithms extensively to either embed innovative capabilities within their solutions, help end-users perform advanced analytics with minimal technical proficiency, or allow data scientists and ML engineers to build advanced ML models and solutions. SAP HANA has been designed to be easily leveraged as a scalable ML platform. A powerful tool built into SAP HANA is the Predictive Analytics Library (PAL).

SAP Data Intelligence has a rich ML content library. Like most best-of-breed analytics tools, SAP Analytics Cloud provides users the ability to leverage advanced Machine Learning (ML) algorithms.  While ML algorithms have many applications, predictive analytics remains a key one.

On the business processes side, SAP AI offerings promise to infuse transformative intelligence to all key business processes areas like lead to cash, design to operate, source to pay and recruit to retire. AI algorithms help include innovative features across all these processes.

Given that you reference AI in ML, would it make sense to put AI first? [RH1]

A little confused here. the first sentence is about SAP Data Intelligence but then you pivot immediately into Analytics Cloud. Did something connective get removed? [RH2]