data streams

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Published By: IBM     Published Date: Aug 05, 2014
There is a lot of discussion in the press about Big Data. Big Data is traditionally defined in terms of the three V’s of Volume, Velocity, and Variety. In other words, Big Data is often characterized as high-volume, streaming, and including semi-structured and unstructured formats. Healthcare organizations have produced enormous volumes of unstructured data, such as the notes by physicians and nurses in electronic medical records (EMRs). In addition, healthcare organizations produce streaming data, such as from patient monitoring devices. Now, thanks to emerging technologies such as Hadoop and streams, healthcare organizations are in a position to harness this Big Data to reduce costs and improve patient outcomes. However, this Big Data has profound implications from an Information Governance perspective. In this white paper, we discuss Big Data Governance from the standpoint of three case studies.
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ibm, data, big data, information, healthcare, governance, technology
    
IBM
Published By: SAS     Published Date: Jan 04, 2019
How can you open your analytics program to all types of programming languages and all levels of users? And how can you ensure consistency across your models and your resulting actions no matter where they initiate in the company? With today’s analytics technologies, the conversation about open analytics and commerical analytics is no longer an either/or discussion. You can now combine the benefits of SAS and open source analytics technology systems within your organization. As we think about the entire analytics life cycle, it’s important to consider data preparation, deployment, performance, scalability and governance, in addition to algorithms. Within that cycle, there’s a role for open source and commercial analytics. For example, machine learning algorithms can be developed in SAS or Python, then deployed in real-time data streams within SAS Event Stream Processing, while also integrating with open systems through Java and C APIs, RESTful web services, Apache Kafka, HDFS and more.
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SAS
Published By: IBM     Published Date: Feb 04, 2014
Explore the top five practices in today's customer-data-driven supply chain and examine the impact of using customer data on key supply chain metrics.
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ibm, aberdeen group, analytics report, customer data, data-driven, supply chain, best practices, social shopping, social networks, analyst insight, supply chain metrics, data streams, big data, data center, data analysis, industry demands, omni-channel environment, collaboration, social business, deployment trends
    
IBM
Published By: IBM     Published Date: Jul 07, 2015
In this book you will also learn how cognitive computing systems, like IBM Watson, fit into the Big Data world. Learn about the concept of data-in-motion and InfoSphere Streams, the world’s fastest and most flexible platform for streaming data.
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big data, mobility, compute-intensive apps, virtualization, cloud computing, scalable infrastructure, reliability
    
IBM
Published By: SAS     Published Date: Apr 16, 2015
The large array of connected devices, often referred to as the “Internet of Things” (IoT), is delivering an array of new data from the sensors they contain. This data offers the promise of new services, improved efficiency and, possibly, more competitive business models.
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SAS
Published By: Cisco     Published Date: Sep 15, 2015
IDC finds that leveraging data analytics in business decisions is becoming a top priority for an increasing number of companies. This in turn is placing new demands on IT organizations; the need is twofold: to manage new streams of unstructured data from sources such as social media and to speed response times to deliver real-time analytics.
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sap, analytics, data, real-time
    
Cisco
Published By: AWS     Published Date: May 18, 2018
We’ve become a world of instant information. We carry mobile devices that answer questions in seconds and we track our morning runs from screens on our wrists. News spreads immediately across our social feeds, and traffic alerts direct us away from road closures. As consumers, we have come to expect answers now, in real time. Until recently, businesses that were seeking information about their customers, products, or applications, in real time, were challenged to do so. Streaming data, such as website clickstreams, application logs, and IoT device telemetry, could be ingested but not analyzed in real time for any kind of immediate action. For years, analytics were understood to be a snapshot of the past, but never a window into the present. Reports could show us yesterday’s sales figures, but not what customers are buying right now. Then, along came the cloud. With the emergence of cloud computing, and new technologies leveraging its inherent scalability and agility, streaming data
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AWS
Published By: Rackspace     Published Date: May 28, 2019
Today, it isn’t a matter of if you’re taking SAP to the cloud – but a matter of when, and how you’re going to make it happen. Because by moving your SAP workloads to the cloud, you are putting them together with other data streams, advanced analytics and machine learning, to create a powerful combination to better engage customers, empower employees, optimize operations and transform products. This e-book introduces Rackspace as the managed cloud service provider to partner, for moving SAP workloads to Azure. Being certified in all the leading SAP technologies, including hosting services, HANA Operations and HANA Enterprise Cloud (HEC) – and having been awarded Microsoft Hosting Partner of the Year five times – Rackspace has got the whole SAP on Azure solution covered, from planning to deployment and ongoing management. Check out the case studies of global companies, like Rockwell Automation, The Mosaic Company, Malaysia Airlines and Coats & Clark, to discover how they have benefi
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Rackspace
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