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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.
98 results
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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…
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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…
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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…
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SAP and Apple Partnership will Transform Warehouse Mobility
Reading time: 4 mins
As customer demand and expectations become complex, associated warehouse operations also become complex. Advances in computing and technology, specifically on the edge, allow organizations to leverage smart devices and mobile solutions in a tactical way to address warehousing operations challenges. Miniaturization, increased performance & energy efficiency, and lower production costs of smart devices, have all…
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Why Data Science Initiatives Fail
Reading time: 1 mins
The video explains stages of data science failures and identifies stages that may lead to the failure of a data science initiative. Membership Required You must be a member to access this content.View Membership LevelsAlready a member? Log in here
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Digital Solutions Branding Evolution: Three Key Steps
Reading time: 3 mins
This article discusses how old school digital solutions brands need to manage evolution and what are the three key steps for that process. Membership Required You must be a member to access this content.View Membership LevelsAlready a member? Log in here
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Thriving in Recession With Analytics and Automation
Reading time: 6 mins
In this article we focus on how executives can leverage the competitive advantage levers of analytics and automation to manage their companies through this and future recessions. Membership Required You must be a member to access this content.View Membership LevelsAlready a member? Log in here
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Advanced Analytics and Process Intelligence
Reading time: 3 mins
Role of Advanced Analytics in Process Intelligence In the latest SAPinsider process automation state of the market research, majority of SAPinsiders indicated that intelligent automation and process intelligence are key capabilities they are planning to build within next two years. Advanced analytics methodologies will play a key role in building intelligent automation solutions. In this article,...…
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Intelligent Solutions for Payment Fraud Risk Management
Reading time: 6 mins
The extensive nature of supply chain networks creates many points of risk. One of such risks, within the procure to pay space is supplier payment fraud. Some examples of procurement fraud are supplier spoofing, business email compromise and spoofing an officer of a company. Procurement fraud is not only becoming more prevalent but is also…
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Discussing Payment Fraud Risk Management with Apex
Reading time: 1 mins
The extensive nature of supply chain networks creates many points of risk. One of such risks is supplier payment fraud within the procure-to-pay space. Some examples of procurement fraud are supplier spoofing, business email compromise, and spoofing an officer of a company. Procurement fraud is becoming more prevalent and is becoming more and more sophisticated.…
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