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.

86 results

  1. How AI Can Be Utilized to Achieve Seamless Data Integration Experience

    Reading time: 1 mins

    As time progresses, so does technological advancement. Artificial Intelligence (AI) is gradually becoming a natural actor in businesses’ lives, overtaking various types of operations and transforming legacy systems. Among those operations stands progress in a faster, easier, and more efficient way of integrating data across SAP systems. For years organizations have used a traditional way…
  2. Advancing Data Management and Analytics with Protiviti

    Reading time: 5 mins

    Many companies are anxious to enhance their analytics capabilities. In fact, recent SAPinsider research found that the demand for analytics is a key motivating factor driving decision-making within finance, supply chain management, and many other key business areas. Protiviti has significant experience in helping businesses to achieve the data foundation that they need to enable…
  3. Datasphere

    Avvale Leverages SAP Datasphere to Innovate in Analytics

    Reading time: 6 mins

    AI and automation are among the most-discussed emerging technologies in the SAP space. While every organization is eager to leverage their power to deliver advanced analytics, not all IT and business leaders know how to go about setting up their data strategy to make these possibilities a reality. Often, these organizations turn to partners like…
  4. Overcoming the Challenges of Predictive Analytics Solutions

    Reading time: 3 mins

    Predictive analytics are a powerful tool. Every company can find some way to benefit from using past data to gain insights into the future. Leveraging predictive analytics to their fullest extent can give organizations an important boost in a competitive market. Nearly every enterprise has access to data, but not all are able to leverage…
  5. Business Intelligence Supply Chains

    Evolving Business Intelligence in Supply Chains

    Reading time: 2 mins

    As supply chains become more diverse and complex, so do their information systems and associated business analytics systems. The need to build near-real-time visibility, data-driven culture, and digitalization are business imperatives that have forced organizations to invest in data and analytics tools and technologies.   However, such tools still need to evolve technologically to address business…
  6. Enabling Real Time Insights Into Sustainability In Today’s NFL Stadium

    As part of the 49ers sustainability initiative, the Executive Huddle presented by SAP was upgraded this season with new capabilities allowing the team to monitor Levi’s Stadium utility usage, allowing them to monitor consumption of water and gas. To get access to real-time utility data, six 3D-printed water and gas meter readers that feed data…
  7. Turbo Charge Your SAP Transformation With a Robust Data Strategy

    A robust data strategy is essential to a successful digital transformation with SAP S/4HANA. It should be established at the planning stages of your transformation and needs to consider components such as data management, governance, quality, migration, architecture, analytics, and data retention. Organizations that do not have a data strategy or defer one to later…
  8. Beyond the “Standard” Data Replication Scenarios

    Many SAP customers struggle to effectively gain analytic capabilities and drive value from the SAP data trapped in their various landscape silos.  Daily data loads do not support the agile and forward-looking analytics expected in the modern enterprise.  A lack of meaningful metadata makes deciphering SAP data in non-SAP systems a challenge for data engineers. …
  9. Platform

    Integrating Analytics and Automation for Supply Chain Resiliency

    Reading time: 5 mins

    Pandemic-related supply chain disruptions have caught the attention of C-suite executives. Leadership now recognizes the importance of supply chain resiliency and are prioritizing and investing in strategies to improve supply chain agility. Resilient supply chains not only mitigate or manage supply chain ris and disruptions efficiently, but they also improve customer service. However, the capabilities…
  10. Provide Actionable Insights With Your Next Dashboard

    Reading time: 6 mins

    Dashboards are often created with a boilerplate approach. They sometimes have the most typical metrics listed out, but forget one crucial aspect - what action do we need to take next? The most critical aspect starts with understanding the requirements and making sure that you are visualizing the right KPIs—not just metrics. In this article…