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Published By: Dell - NVIDIA     Published Date: Nov 04, 2019
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies are expected to permeate day-to-day business as well as customer activity. Industries such as healthcare (advanced diagnosis and treatment), transportation (advanced driver assistance systems and autonomous vehicles), and life sciences (rare disease treatment research) are some of the early adopters of AI. The goal for any organization adopting AI/ML/DL is to deliver meaningful insights and predictions that can significantly improve products, processes, or services across industries and use cases. Today, as AI becomes mainstream, many organizations find themselves in the initial proof-of-concept (POC) stage; only a few are in full production. IDC's 2019 Artificial Intelligence Global Adoption Trends and Strategies Survey found that 18% of organizations had AI models in production, 16% were in the POC stage, and 15% were experimenting with AI.
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Dell - NVIDIA
Published By: Schneider Electric     Published Date: Aug 15, 2017
Schneider Electric is integrating datacenter infrastructure management (DCIM) software, big-data analytics and cloud services into the management of customersí datacenters. Its recently launched StruxureOn cloud offering signals a new wave in datacenter operations, using a combination of machine learning, anomaly detection and event-stream playback to give operators real-time insights and alarming via their smartphones. More capabilities and features are planned, including predictive analysis and, eventually, automated action. Schneiderís long-term strategy is to build a partner ecosystem around StruxureOn, and provide digital services that span its traditional datacenter business.
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incident tracking, historical trending, troubleshooting, operational analysis, prediction model, schneider equipment, maintenance, firmware updates
    
Schneider Electric
Published By: IBM     Published Date: Aug 07, 2012
Most organizations appreciate the potential benefits that customer can reap but many face difficulties effectively turning information into actionable insights. Read this white paper to learn how an effective customer analytics strategy can help drive top-line growth, avoid unnecessary costs and increase customer satisfaction. Understand where your organization is in its pursuit to gain deeper customer insights with four stages of organizational capabilities and associated customer analytics strategies: gain insight from the information explosion; share information internally and across value chain; move from reaction to prediction; and adapt business models that enable faster creation of value.
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customer amalytics. ibm, analytics framework, external data sharing, internal data sharing, increased loyalty, cross sell, wallet share, net promoter score, sales conversion rate, improved regency, improved frequency, improved monetary value, multi-channel next best action, mnba, social media, crm & customer care, marketing research, traditional marketing
    
IBM
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