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Published By: SAP     Published Date: Nov 29, 2012
"Customer centric" is a term we can all be familiar with and with good reason: Customers buy products and contract for services ass well as contribute on social media and blog sites sharing information on your products. How can we analyze this info?
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analytics, sap, social media, customer centric, analytics technology
    
SAP
Published By: SAP     Published Date: Jul 18, 2016
Organizations are investing in new analytics technologies to improve agility and performance. The technology and data must come together to serve the business users under the guidance and management of IT. Read the white paper to learn key findings in the analytics market and learn how to deliver analytics capabilities suitable for a wide range of use cases.
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SAP
Published By: SAS     Published Date: Mar 14, 2014
The solution to operationalizing analytic s involves the effective combination of a Decision Management approach with a robust, modern analytic technology platform. This paper discusses both how to use a focus on decisions to ensure the right problem gets solved and what such an analytic technology platform looks like.
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sas, predictive analytics, technology platform, solution, operationalizing, production systems
    
SAS
Published By: SAS     Published Date: Aug 03, 2016
As the pace of business continues to accelerate, forward-looking organizations are beginning to realize that it is not enough to analyze their data; they must also take action on it. To do this, more businesses are beginning to systematically operationalize their analytics as part of a business process. Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex and organizations are at different stages of operational deployment.
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best practices, embedding analytics, technology, data, operational analytics
    
SAS
Published By: SAS     Published Date: Aug 03, 2016
A paradigm shift is underway in the cybersecurity industry. Cybersecurity professionals are moving from a focus on attacker prevention to attacker detection. Preventing the “bad guys” from getting in is still important, but cyber adversaries are increasingly able to bypass even the most sophisticated network defenses. Once inside, it is more important than ever to find these attackers fast, before their activities get buried in the daily volume and pulse of network communications. This is where security analytics holds promise. Security analytics provides the necessary and timely visibility into normal and abnormal network behavior. This visibility enables devices and entities acting suspiciously to be quickly identified and investigated.
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cyber security, security, best practices, attacker prevention, paradigm shift, security analytics, technology
    
SAS
Published By: SAS     Published Date: Apr 25, 2017
Organizations in pursuit of data-driven goals are seeking to extend and expand business intelligence (BI) and analytics to more users and functions. Users want to tap new data sources, including Hadoop files. However, organizations are feeling pain because as the data becomes more challenging, data preparation processes are getting longer, more complex, and more inefficient. They also demand too much IT involvement. New technology solutions and practices are providing alternatives that increase self-service data preparation, address inefficiencies, and make it easier to work with Hadoop data lakes. This report will examine organizations’ challenges with data preparation and discuss technologies and best practices for making improvements.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
Analytics is now an expected part of the bottom line. The irony is that as more companies become adept at analytics, it becomes less of a competitive advantage. Enter machine learning. Recent advances have led to increased interest in adopting this technology as part of a larger, more comprehensive analytics strategy. But incorporating modern machine learning techniques into production data infrastructures is not easy.Businesses are now being forced to look deeper into their data to increase efficiency and competitiveness. Read this report to learn more about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. And learn from the experiences of two companies that have successfully navigated both organizational and technological challenges to adopt machine learning and embark on their own analytics evolution.
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SAS
Published By: SAS     Published Date: Jun 05, 2017
Find out what text analytics can do for an organization and the top three things people need to know when adopting text analytics. This research brief from the International Institute for Analytics and SAS outlines the challenges of implementing text analytics solutions and explores what makes this technology unique and exciting.  
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SAS
Published By: SAS     Published Date: Oct 18, 2017
Quality 4.0 isn't really a story about technology. It's about how that technology improves culture, collaboration, competency and leadership. The last decade has seen rapid advances in connectivity, mo­bility, analytics, scalability and data, creating what some call the fourth industrial revolution, or Industry 4.0. With the help of the Industrial Internet of Things (IIoT), manufacturers have digitized operations, transforming efficiency, supply-chain performance and in­novation. This revolution has even created entirely new business models. This e-book gives manufacturers the tools to lead the Qual­ity 4.0 transformation – a transformation that raises traditional manufacturing to the next level. It teaches readers to use advanced technology, analytics and IIoT to strengthen the manufacturing process and bring it forward into a powerful digital age.
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SAS
Published By: SAS     Published Date: Oct 18, 2017
With the support of SAS, the Internet of Things Institute developed the 2017 Internet of Things ROI Research Study to gather real-world insights, lessons learned and future guidance from current users of IoT technology and advanced analytics. This selective sample of IoT users offers valuable insights to both IoT innovators and organizations still waiting to see how the technology evolves before investing. Multiple business layers and functions have input into IoT decision making. Discover which layer was most critical to success for those organizations that have achieved the highest percentage of their targeted ROI. Learn about the main drivers of success for IoT users achieving higher returns. Lastly, the results highlight six primary factors that can undermine an IoT initiative and how they can be prevented.
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SAS
Published By: SAS     Published Date: Mar 06, 2018
For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility. SAS adheres to five data management best practices that support advanced analytics and deeper insights: • Simplify access to traditional and emerging data. • Strengthen the data scientist’s arsenal with advanced analytics techniques. • Scrub data to build quality into existing processes. • Shape data using flexible manipulation techniques. • Share metadata across data management and analytics domains.
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SAS
Published By: SAS     Published Date: Jun 06, 2018
A multitude of “things” generate floods of big data – cars, wearables, machines and appliances. Wouldn’t you like to sift through that noise and become an organization that relies on data to make fact-based decisions? Learn about the three foundations of becoming data-driven – data management, analytics and visualization – and how they can increase profitability, boost performance, raise market share and improve operations. Read about hurdles to becoming a data-driven organization and learn best practices from others. Then get a glimpse of what the future holds with the Internet of Things (IoT), edge analytics, artificial intelligence (AI) and other technology innovations.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever. Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
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SAS
Published By: SAS     Published Date: Aug 28, 2018
With the widespread adoption of predictive analytics, organizations have a number of solutions at their fingertips. From machine learning capabilities to open platform architectures, the resources available to innovate with growing amounts of data are vast. In this TDWI Navigator Report for Predictive Analytics, researcher Fern Halper outlines market opportunities, challenges, forces, status and landscape to help organizations adopt technology for managing and using their data. As highlighted in this report, TDWI shares some key differentiators for SAS, including the breadth and depth of functionality when it comes to advanced analytics that supports multiple personas including executives, IT, data scientists and developers.
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SAS
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: SAS     Published Date: Jan 30, 2019
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever. Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
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SAS
Published By: Sitecore     Published Date: Nov 04, 2009
This report highlights the strategic value of a next generation web content management system integrated with lead scoring, email marketing, customer relationship management, and web analytics. The report links the technology and practices of Best-in-Class organizations to engage customers, provide personalized experiences and manage the lead lifecycle.
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sitecore, aberdeen, lifecycle management, web content management, analytics, crm, emarketing, email marketing, lead generation
    
Sitecore
Published By: SPSS     Published Date: Mar 31, 2009
This whitepaper details how predictive analysis can help your business.  Predictive analytics help you make better, faster decisions, giving your organization a significant competitive advantage in the technology sector.
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spss, predictive analysis, roi, data, data driven decision making, mitigate risk, fraud, real time intelligence, data analysis, analytical methods, strategic planning, customer intimacy, crm, best practices, business intelligence, statistics, statistical analysis, data management, decision-making
    
SPSS
Published By: SPSS     Published Date: Jun 30, 2009
Read how Cabelcom recognized the key to tackling churn and was able to identify the point at which customers become dissatisfied with their service.
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spss, cablecom, customer retention, crm, churn rate, reduce churn, predictive analytics, statistics, statistical analysis, cable network operator, tailored marketing, targeted marketing, best-of-breed, data mining technology, customer feedback, customer lifecycle, cross-selling, data management, data analysis, decision-making
    
SPSS
Published By: SPSS, Inc.     Published Date: Mar 31, 2009
Read how Cabelcom recognized the key to tackling churn and was able to identify the point at which customers become dissatisfied with their service.
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spss, cablecom, customer retention, crm, churn rate, reduce churn, predictive analytics, statistics, statistical analysis, cable network operator, tailored marketing, targeted marketing, best-of-breed, data mining technology, customer feedback, customer lifecycle, cross-selling, data management, data analysis, decision-making
    
SPSS, Inc.
Published By: SumTotal Systems     Published Date: Oct 10, 2013
Workforce analytics has become an essential business tool for leading companies that view workforce performance as the key to improving company results, according to a new global survey of business leaders by Harvard Business Review Analytics Services. Workforce analytics is a set of integrated capabilities (technologies, metrics, data, and processes) to measure and improve workforce performance. The goal is simple: put the right people with the right skills in the right work, provide them with the necessary training and development opportunities, and engage and empower them to perform at their highest possible level.
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sumtotal, harvard business review, workforce, workforce analytics, analytics, company performance, workforce management, workforce data, workforce analysis, human resources, hr technology
    
SumTotal Systems
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