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Results 26 - 50 of 56Sort Results By: Published Date | Title | Company Name
Published By: TIBCO Software     Published Date: May 16, 2019
Financial Services is an industry driven by disruption. Transformative business models such as low-cost brokerages, innovative investment products like ETFs, and the huge regulatory mandates like Gramm-Leach-Bliley are but a few examples. Here are some others: • New fintech firms such as a recent nine billion dollar investment in Ant Financial Services Group and myriad other venture capital-led fintech startups targeting well established segments across the financial services industry • Robo-advisor services powered by artificial intelligence and machine learning intermediating financial advisors and portfolio managers alike • Ever changing regulatory and risk management mandates, such as GDPR, Basel III, and Open Banking, transforming customer engagement and capital allocation Read this whitepaper to learn how you can overcome these and other disruptions.
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data, applications, analytics, algorithms, innovation, cloud, technology, customer
    
TIBCO Software
Published By: Adobe     Published Date: Oct 24, 2018
Adobe automates the process of turning insights into action by connecting Adobe Analytics to other solutions in Adobe Experience Cloud, including Adobe Target and Adobe Audience Manager. Four features make this possible: • Anomaly detection. The technology automatically analyzes trends and determines if they are statistically significant — in milliseconds. • Analyze play button. With analytics, you can take insights and connect them to your email, DMP, and personalization platform in seconds. • Intelligent alert. A built-in alerting system sends an SMS text or email when it detects an anomaly. There are also predictive algorithms that help you forecast how often the alert is likely to trigger. You can set these to only notify you of the most important changes. • Intelligent recommendations. It’s simply impossible to manually create every alert you might need, so Adobe is building machine learning directly into analytics to analyze users’ behaviors. Like a virtual data assistant, it co
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Adobe
Published By: Reputation.com     Published Date: Jun 29, 2018
The past year ushered in some big changes for Online Reputation Management (ORM) — and the practice has become indispensable for any location-based enterprise. In 2017, review sources proliferated, consumers became more savvy about the validity of online reviews, and the position of Chief Experience Officer started to gain traction among locationbased organizations. ORM and SEO became increasingly intertwined as Google refined its search algorithms with a strong emphasis on reviews and star ratings. This year, expect to see these four trends move to the forefront: 1) Google will extend its dominance in online review volume and consumer exposure, eclipsing all other specialty sites. 2) SEO will be reinvented as user-generated reviews weigh more heavily in search rankings. 3) The voice of the customer will no longer be siloed across disconnected categories. 4) Consumer feedback from reviews and social media will drive operational improvements.
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Reputation.com
Published By: Pure Storage     Published Date: Dec 05, 2018
Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.
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Pure Storage
Published By: Gigaom     Published Date: Oct 07, 2019
While interest in Machine Learning/Artificial Intelligence/ (ML/AI) has never been higher, the number of companies deploying it is only a subset, and successful implementations a smaller proportion still. The problem isn’t the technology; that part is working great. But the mere presence and provision of tools, algorithms, and frameworks aren’t enough. What’s missing is the attitude, appreciation, and approach necessary to drive adoption and working solutions. To learn more, join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and panelists Jen Stirrup, Lillian Pierson, and special guest from Cloudera Fast Forward Labs, Alice Albrecht. Our panel members are seasoned veterans in the database and analytics consulting world, each with a track record of successful implementations. They’ll explain how to go beyond the fascination phase of new technology towards the battened down methodologies necessary to build bulletproof solutions th
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Gigaom
Published By: FICO     Published Date: Nov 10, 2015
"ACG Michigan, a large auto insurance underwriter in the US state of Michigan, needed a user-friendly system that would enable its agents (internal and independent) to churn out precise and consistent policy quotes and underwriting decisions. They turned to FICO Blaze Advisor decision rules management system to create an enterprise decision management framework to execute decisions. Learn more on how FICO Blaze Advisor helped ACG Michigan automate its underwriting About FICO FICO (NYSE: FICO), formerly known as Fair Isaac, is a leading analytics software company, helping businesses in 90+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company's groundbreaking use of Big Data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimiz
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decision analytics, optimization, profitability, finance
    
FICO
Published By: Monetate     Published Date: Oct 22, 2018
Monetate Intelligent Recommendations automates recommendations at scale without sacrificing any of the control you require. Our proprietary algorithms know what to serve each individual shopper to maximize brand value, while still allowing the control of an unlimited number of business guardrails that you define.
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monetate, intelligent, recommendations, business, individual, automation
    
Monetate
Published By: Marketo     Published Date: Mar 22, 2018
Advertising on social media platforms has changed tremendously. Recent updates to many social networks' algorithms give users a better experience—one with less promotional content and more relevant content that they want to see. This means that, as a marketer, you need to supplement your organic posts with paid promotion to get your posts seen by your audience. Download this eBook to learn more!
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Marketo
Published By: ESV Digital     Published Date: Feb 10, 2015
The multiplication of marketing channels and devices concerning consumers has greatly increased the complexity faced by brands in their marketing efforts. This white paper aims to explain the issues, the principal attribution models used and the related challenges.
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esv digital, algorithms, interactions, point of contact, nurturing, marketing data, marketing analytics, business technology
    
ESV Digital
Published By: AWS     Published Date: Jun 24, 2019
Join our webinar to hear how Consensus, a Target-owned subsidiary, utilizes AWS and Trifacta to prepare data for use in fraud detection algorithms. You’ll learn how self-service automated data wrangling can save your organization time and money, and tips for getting started with Trifacta’s solution, built for AWS. Webinar attendees will learn: Why automating your data wrangling tasks can lead to greater data accuracy and more meaningful insights. How you can reduce your data preparation time by 60% and more with self-service data wrangling tools built for AWS. How easy it is to get started with machine learning solutions for data wrangling on the cloud.
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AWS
Published By: Group M_IBM Q119     Published Date: Jan 08, 2019
• Do you want to win with AI in the hybrid, multi cloud world? Are you tackling data, algorithms and apps to drive business value from AI? We got you covered. Come and learn how you can simplify and scale your AI projects on Watson Studio. Hybrid cloud use cases spanning cloud, desktop and local are featured. Key Takeaways: • Open, trustworthy and secure approach to put AI to work for business • Go live and scale faster with AI-infused platform • Build train and deploy models across hybrid, cloud environments – including popular public cloud environments like AWS and Azure • Flexibility for cloud, on-premise and desktop deployment, bringing algorithms to wherever data resides • Progressing your AI/data science with Watson Studio • Register now and get ready to simplify and scale your AI investments to work for your business.
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Group M_IBM Q119
Published By: SAS     Published Date: Mar 06, 2018
Imagine getting into your car and saying, “Take me to work,” and then enjoying an automated drive as you read the morning news. We are getting very close to that kind of scenario, and companies like Ford expect to have production vehicles in the latter part of 2020. Driverless cars are just one popular example of machine learning. It’s also used in countless applications such as predicting fraud, identifying terrorists, recommending the right products to customers at the right time, and correctly identifying medical symptoms to prescribe appropriate treatments. The concept of machine learning has been around for decades. What’s new is that it can now be applied to huge quantities of data. Cheaper data storage, distributed processing, more powerful computers and new analytical opportunities have dramatically increased interest in machine learning systems. Other reasons for the increased momentum include: maturing capabilities with methods and algorithms refactored to run in memory; the
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SAS
Published By: SAS     Published Date: Mar 06, 2018
Machines learn by studying data to detect patterns or by applying known rules to: • Categorize or catalog like people or things • Predict likely outcomes or actions based on identified patterns • Identify hitherto unknown patterns and relationships • Detect anomalous or unexpected behaviors The processes machines use to learn are known as algorithms. Different algorithms learn in different ways. As new data regarding observed responses or changes to the environment are provided to the “machine” the algorithm’s performance improves. Thereby resulting in increasing “intelligence” over time.
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SAS
Published By: IBM     Published Date: Apr 03, 2017
Predictive analytics is powerful. It can help drive significant improvement to an organization’s bottom line. Look for ways to use it to grow revenue, shrink costs and improve margins. Provide a platform that enables your data scientists to work efficiently using tools and algorithms they prefer. Enhance your analyses with internal and external data, structured and unstructured data. Then make the analytics accessible in order to reap the full benefits of these valuable analyses. Stay ahead of the curve in your market with predictive analytics, and give your organization a competitive advantage and an improved bottom line.
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predictive analytics, analytics, data analytics, financial marketing, market analytics, data resources
    
IBM
Published By: SAS     Published Date: Oct 03, 2018
Unlike rules-based systems, which are fairly easy for fraudsters to test and circumvent, machine learning adapts to changing behaviors in a population through automated model building. With every iteration, the algorithms get smarter and more accurately find activities that represent risk to the firm.
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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: Cisco     Published Date: Jan 05, 2016
With a global monthly reach consisting of more than 500 million mobile users and billions of requests, Tapjoy historically relied heavily on their IT footprint. They needed an environment that would allow them to accelerate the development and improve the performance of their big data algorithms, which help drive real time decision-making that delivers the best content to their global audience. Read more to learn how Tapjoy engineers opted for a cloud-based model to run their big data platform instead of bare metal, prioritizing agility and the ability to scale over bare metal performance.
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Cisco
Published By: SPSS     Published Date: Jun 30, 2009
The intrepid data miner runs many risks, including being buried under mountains of data or disappearing along with the "mysterious disappearing terabyte."  This article outlines some risks, debunks some myths, and attempts to provide some protective "hard hats" for data miners in the technology sector.
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spss, data mining, data miner, data management, business process, best practices, business intelligence, statistics
    
SPSS
Published By: SPSS, Inc.     Published Date: Mar 31, 2009
The intrepid data miner runs many risks, including being buried under mountains of data or disappearing along with the "mysterious disappearing terabyte."  This article outlines some risks, debunks some myths, and attempts to provide some protective "hard hats" for data miners in the marketing sector.
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spss, data mining, data miner, data management, business process, best practices, business intelligence, statistics
    
SPSS, Inc.
Published By: SAS     Published Date: Oct 18, 2017
Machine learning uses algorithms to build analytical models, helping computers “learn” from data. It can now be applied to huge quantities of data to create exciting new applications such as driverless cars. This paper, based on presentations by SAS Data Scientist Wayne Thompson, introduces key machine learning concepts and describes SAS solutions that enable data scientists and other analytical professionals to perform machine learning at scale. It tells how a SAS customer is using digital images and machine learning techniques to reduce defects in the semiconductor manufacturing process.
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SAS
Published By: Cisco     Published Date: Jul 11, 2016
With a global monthly reach consisting of more than 500 million mobile users and billions of requests, Tapjoy historically relied heavily on their IT footprint. They needed an environment that would allow them to accelerate the development and improve the performance of their big data algorithms, which help drive real time decision-making that delivers the best content to their global audience. Tapjoy engineers opted for a cloud-based model to run their big data platform instead of bare metal, prioritizing agility and the ability to scale over bare metal performance. Initially they deployed at AWS, but as the platform grew and their AWS costs increased, Tapjoy began to look for ways to better manage their growing public-cloud spend while increasing efficiency. They needed to do this without compromising the cloud experience they were giving their developers.
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Cisco
Published By: IBM     Published Date: May 30, 2008
WinterCorp analyzes IBM's DB2 Warehouse and how it addresses twin challenges facing enterprises today: improving the value derived from the torrents of information processed every day, while lowering costs at the same time. Discover why WinterCorp believes the advances in data clustering strategies and intelligent software compression algorithms in IBM's Data Warehouse improves performance of business intelligence queries by radically reducing the I/O's needed to resolve them.
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data warehousing, data management, database management, database administration, dba, business intelligence, ibm, leveraging information
    
IBM
Published By: Microworld     Published Date: Dec 12, 2007
NILP from MicroWorld is an advanced, next generation technology that detects Spam and Phishing mails using unique algorithms. Before we see how NILP works, let’s first check out the magnitude of spam trouble and why traditional methods are failing to counter it.
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spam, email security, anti-spam, anti spam, email, intrusion prevention, mail scan, mailscan
    
Microworld
Published By: ChoiceStream     Published Date: Feb 04, 2009
ChoiceStream's approach towards personalization differs dramatically from others.  In "Traditional Approaches Contrasted with ChoiceStream's Universal Recommender" technology brief you'll learn about the components of personalization systems.  Most importantly, you’ll learn how to choose a winning algorithm that provides the right personalization solution for your online store.
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choicestream, personalization technology brief, universal recommender, best-of-breed personalization, collaborative filtering, preference capture, algorithms
    
ChoiceStream
Published By: SiliconExpert Technologies     Published Date: Dec 23, 2010
A case for proactive component management using lifecycle forecasting algorithms from independent component databases.
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obsolescence, component, bill of materials, boms, electronic parts
    
SiliconExpert Technologies
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