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]

80 results

  1. SAP Industry Cloud

    SAP Industry Cloud – Need of The Hour?

    Reading time: 4 mins

    SAP Industry Cloud is a portfolio of cloud-based solutions designed specifically for specific industries, by SAP and SAP partners, on the SAP BTP platform. That is the simple explanation but should help you see how it comes into play after the narrative described in the last paragraph. Companies in specific industries can save a lot…
  2. Supply chain analytics maturity

    Five Stages of Achieving Supply Chain Analytics Maturity

    Reading time: 2 mins

    Supply chain analytics has rapidly evolved in the last few years, supported by progress in technology and computing power. This evolution can also be attributed to increasing supply chain complexities, and challenges companies are increasingly running into. SAPinsiders have consistently highlighted their increased focus on leveraging analytics and automation in the supply chain in our…
  3. Optimization Simulation

    Blurring Boundaries between Optimization and Simulation Tools

    Reading time: 2 mins

    Optimization and simulation tools have been used in supply chain analytics for a long time.  Generally, they have been categorized into prescriptive analytics methodologies that help you understand the best way to do something. This is not a concrete characterization, however. If your simulation model of your current state, for example, helps you understand which…
  4. Enterprise Analytics

    Exploring Key Technical Features of Enterprise Analytics

    Reading time: 1 min

    As businesses become more diverse and complex, so do their associated business analytics systems. Imperatives like the need to build near real-time visibility, a data-driven culture, and digitalization have forced many organizations to invest in many data and analytics tools and technologies. Though technology and computing power have rapidly evolved during the last decade to…
  5. Cloud Market

    The Three Ss of Cloud Market Dominance

    Reading time: 3 mins

    There is no doubt that cloud infrastructure will redefine how organizations build their technology capabilities and how they innovate. This has emerged as a consistent theme in our research and conversations with the SAPinsider community. Our research reports like Future of Business Intelligence, Analytics in The Cloud, Modernizing Logistics and Inventory Tracking, and Supply Chain…
  6. Customer Data

    Amazon’s Acquisition of iRobot : The War For Consumer Data

    Reading time: 5 mins

    Amazon's decision to buy iRobot is a strategic step geared towards collecting more consumer behavior data points. This article discusses this strategy in detail and explores how Amazon may end up leveraging this data. Specifically, in this article, we explore (1) Why customer data is important (2) How Amazon has worked over the years to…
  7. SAPinsider 2022 Vegas : Data & Analytics Session Themes

    Reading time: 3 mins

    SAPinsider events have been the most widely attended, independent networking and knowledge-sharing events in the SAP technology ecosystem for decades. After taking a hiatus from in-person events for a couple of years, due to the pandemic, SAPinsider was back with a bang this year. SAPinsider 2022 saw 3000+ members in attendance in Vegas in July…
  8. NLP

    Maturation of NLP : SAP’s AskData Acquisition

    Reading time: 4 mins

    Fortune Business Insights estimates that the global big data analytics market will reach $655.5 billion by 2029, at a CAGR of 13.4% from 2022 to 2029. To tap into this pie and create additional categories, companies will have to increasingly build capabilities through acquisitions and partnerships in the technology ecosystem. SAP's acquisition of AskData is…
  9. Supply Chain Analytics

    Analytics Journey in Supply Chain Planning

    Reading time: 2 mins

    In the responses for SAPinsider research, Supply Chain Planning in The Cloud, advanced analytics emerged as a key technical requirement as well as a key technology . Lack of relevant and timely data and insights was identified as a major pain point with current planning solutions portfolio as well . Analytics plays a key role…
  10. Supply Chain Planning Skills

    Critical Skills for The Future of Supply Chain Planning

    Reading time: 3 mins

    In response to the survey for our upcoming research report, Supply Chain Planning in The Cloud, SAPinsiders highlighted advanced analytics capabilities as the top technology when the current state and immediate next steps timeframe are considered together, as shown in figure 1. As you can see from figure 1, SAPinsiders have indicated that they have…