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.

87 results

  1. How analytics changes with SAP S/4HANA and impact to modern data platform

    As you transform your business with SAP S/4HANA, the way analytics are handled is going to change. SAP S/4HANA opens insights like never before. A new approach to a modern data platform adds significant value and provides the opportunity to unlock the potential of a SAP S/4HANA transformation. Attend this comprehensive session to: -Learn how…

  2. Using Financial Analytics to Unlock Data Insights

    May 26, 2021

    Attend this fireside chat with top experts who will discuss practical strategies for integrating SAP data with modern analytics to accelerate business insights for finance professionals. As a finance team, you want to become the most strategic partner to your organization’s lines of business. But are you tired of waiting for never-ending processes to gather and…

  3. SAP S/4HANA image

    Leveraging Machine Learning for Demand Forecasting

    Reading time: 4 mins

    Until recently, conventional time series forecasting methods have been predominantly used for forecasting in demand planning. A majority of demand forecasting tools in the market leverage these methods in their solutions. With advances in technology and computing power, the sophistication of these time series algorithms has increased thereby increasing forecast accuracy. However, with the advent…

  4. A quick comparison of SAP S/4HANA Cloud, SAP Analytics Cloud, and SAP Data Intelligence

    The goal of the presentation is to explore the latest updates related to SAP cloud technologies (SAP Analytics Cloud, SAP Data Intelligence, and SAP S/4HANA Cloud), and as a useful guide to companies to choose when each of these technologies is applicable to them depending on their key processes they want to involve in the…

  5. The Role of Analytics in Business Process Intelligence

    The Role of Analytics in Business Process Intelligence: A RISE with SAP Perspective

    Reading time: 6 mins

    Increasingly, businesses are shifting their focus to accommodate the new digital reality. Technology is a key enabler in this aim, but businesses also need to consider what needs to change within their enterprises, including their processes and organizational design. Business transformation has become the front-and-center strategic priority that helps organizations adapt to the digital world…

  6. SAP AI Strategy featured image

    Enterprise Artificial Intelligence – Embedded AI

    Reading time: 9 mins

    If your company has not begun building an artificial intelligence (AI) strategy, it may risk falling behind competitors. That could mean lagging in areas of innovation that AI can enable, including improved or new products, reduced risks, and a better bottom line. There is good news for SAP customers: SAP has already begun embedding AI…

  7. Enterprise Data and Analytics State of the Market Benchmark Report

    Reading time: 1 mins

    Organizations in today’s digital age face a set of challenges that they have never experienced before. As commoditization of technology continuously lowers barriers to entry across industries, incumbents are being disrupted by players that are smaller, nimble, and agile. New business models, products and services are being launched on a daily basis by new entrants,…...…

  8. Build an Agile Data Platform with SAP Data Warehouse Cloud

    The journey from Strategy to an Analytics Algorithm : A Marketing Analytics example

    Reading time: 1 mins

    Every Analytics algorithm that you use in your teams should tie to your Corporate strategy goals. However, very few resources currently clearly highlight how. Closing this link, in my mind, is one of the ingredients to the secret sauce to developing analytical products that will work, in a sustainable way (not a blip that goes…

  9. Maximize Your Investment in SAP and Azure Synapse to Create a Cost-Effective Data Analytics Strategy

    February 01, 2021

    Businesses across the globe are struggling as they attempt to wrangle multiple data sources into a cohesive data analytics strategy. Bringing together SAP and non-SAP data is complex and requires significant technical resources. In this session, learn from experts about how you can leverage IBM to help you bring this data together in a data fabric that maximizes your investment in SAP combined with Azure Synapse creating a cost-effective data analytics strategy and…

  10. Impact on Analytics and Visibility

    Reading time: 3 mins

    SAP S/4HANA, for many organizations, is the single sources of truth for finance and business functions. As organizations conduct their SAP S/4HANA transition projects, they are consolidating data from multiple systems across the enterprise. This has an impact on the business capabilities of making critical decisions, conducting analysis, and understanding the direction of the business…