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Results 276 - 300 of 383Sort Results By: Published Date | Title | Company Name
By: IBM     Published Date: Apr 29, 2014
Customer Profitability Analytics enables banks to analyze customer, account, product, and transaction data and apply costing models to determine a bank-wide view of profitability. Applying predictive analytics, they can model future behavior and derive a lifetime value for each customer.
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ibm, banking, customer profitability, customer profitability analytics, transaction data, predictive analytics, customer behavior, business analytics
    
IBM
By: Lumension     Published Date: Feb 07, 2014
Java vulnerabilities have dominated the security headlines. Some observers now say organizations should simply turn off the ubiquitous software platform. But what if there were a better way?
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lumension, java vulnerabilities, unpatched vulnerabilities, blacklist order, third-party software, endpoint devices, data-security solutions, web content, security model, dll protection, it environments, security suite, endpoint protection, e-security, targeted phishing, organized attacks, vulnerability management, security, wireless security, best practices
    
Lumension
By: AWS     Published Date: Jul 24, 2019
Many business leaders know that Artificial Intelligence (AI) and Machine Learning (ML) are critical to their future but don’t know where to start. Those who do have an AI/ML strategy struggle to find qualified data scientists; and once they find them, even advanced data scientists need a lot of time—even months—to build and deploy ML models. These challenges put significant limits on the range and number of problems a business can solve. In this webinar, learn how H2O Driverless AI on Amazon Web Services (AWS) automates the best practices of leading data scientists to create advanced machine learning models automatically. With these production-ready models, relative newcomers to AI/ML can generate reliable results and scale-up AI programs that anticipate and capitalize on trends, optimize supply chains, understand customer demand, match consumers with goods and services, and much more. Download our webinar to learn Implement ML successfully with minimal data science expertise. Build
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AWS
By: AWS     Published Date: Jul 24, 2019
Trupanion, a Seattle-based medical insurance provider for cats and dogs, needed to find data insights quickly. With only 1% of pet owners insured, the process of evaluating a claim to approve or deny payment was manual and time-consuming. Building accurate predictive models for decision-making required manpower, time, and technology that the small company simply did not have. DataRobot Cloud, built on AWS, helped Trupanion create an automated method for building data models using machine learning that reduced the time required to process claims from minutes to seconds. Join our webinar to hear how Trupanion transformed itself into an AI-driven organization, with robust data analysis and data science project prototyping that empowered the company to make better decisions and optimize business processes in less time and at a reduced cost. Join our webinar to learn: Why you don’t need to be an expert in data science to create accurate predictive models. How you can build and deploy pr
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AWS
By: AWS     Published Date: Jul 24, 2019
Some organizations are reluctant to migrate to the cloud because they believe they will be forced to learn new skills, start using new tools, and adopt new processes. However, by deploying VMware Cloud on AWS, your organization can continue to leverage existing, familiar VMware investments. This on-demand service delivers a powerful hybrid cloud solution, combining an industry leader in virtualization, VMware, with the largest cloud provider, Amazon Web Services (AWS). One of the first solution providers to achieve the VMware Solution Competency and a participant in the AWS Partner Initiative for VMware Cloud on AWS, RoundTower is uniquely qualified to help your organization adopt and optimize VMware Cloud on AWS. Watch this webinar to see how they can extend your on-premises data center to AWS, enabling you to gain increased flexibility, a rapidly scalable environment, and faster time to innovation. Download our webinar to learn How to take advantage of flexible consumption models t
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AWS
By: Mintigo     Published Date: Nov 20, 2017
Lead scoring models were put in place to prioritize leads for follow-up, nurture and marketing investment. However, with guesswork and data coming from disparate sources, traditional lead scoring has a host of limitations. In today’s competitive landscape, organizations needs to embrace more advanced and reliable scoring models to succeed. Read this ebook to learn 4 reasons why traditional lead scoring is not adequate, and why scoring with AI is necessary.
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Mintigo
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
By: IBM     Published Date: May 27, 2014
If insurers want to succeed in today's digital world, they need to create experiences and business models that are orchestrated, symbiotic, contextual and cognitive.
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ibm, big data, insurance, digital insurer, business model, insurance industry, technology, integration, social business, it management
    
IBM
By: IBM     Published Date: Nov 19, 2014
Risk Management: General: Partially cloudy: the benefits of hybrid deployment models
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ibm, risk management, big data, data management, operational risk, event data, risk aware, risk solutions, hybrid deployment, deployment models, security management, business intelligence
    
IBM
By: IBM     Published Date: Jul 19, 2016
Learn how IBM Cognos Analytics incorporates many governed discovery features, with which business users can easily expose the underlying data modelling of existing reports and modify it to an individual or departmental requirements
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ibm, ibm cognos analytics, analytics, business intelligence, business analytics, business integration, business management, business metrics
    
IBM
By: VSS Monitoring     Published Date: Aug 01, 2014
Learn how to leverage a big data model in the network monitoring domain and see how IT is shifting away from the long-term viability of the existing network monitoring model.
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networking, big data, network monitoring, optimization, data center, monitoring
    
VSS Monitoring
By: IBM     Published Date: Jul 01, 2015
This white paper discusses how enterprise analytics systems can assist provider organizations in building sustainable healthcare systems and achieving their vision for accountable care
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healthcare analytics, data blueprint, enterprise analytics systems, sustainable healthcare systems, mission-critical analysis, integrated data model, operational analytics
    
IBM
By: BlackBerry Cylance     Published Date: Jul 02, 2018
The information security world is rich with information. From reviewing logs to analyzing malware, information is everywhere and in vast quantities, more than the workforce can cover. Artificial intelligence (AI) is a field of study that is adept at applying intelligence to vast amounts of data and deriving meaningful results. In this book, we will cover machine learning techniques in practical situations to improve your ability to thrive in a data driven world. With clustering, we will explore grouping items and identifying anomalies. With classification, we’ll cover how to train a model to distinguish between classes of inputs. In probability, we’ll answer the question “What are the odds?” and make use of the results. With deep learning, we’ll dive into the powerful biology inspired realms of AI that power some of the most effective methods in machine learning today. Learn more about AI in this eBook.
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artificial, intelligence, enterprise
    
BlackBerry Cylance
By: SAS     Published Date: Mar 06, 2018
The 2016 ACFE Report to the Nations on Occupational Fraud and Abuse analyzed 2,410 occupational fraud cases that caused a total loss of more than $6.3 billion.8 Victim organizations that lacked anti-fraud controls suffered double the amount of median losses. SAS’ unique, hybrid approach to insider threat deterrence – which combines traditional detection methods and investigative methodologies with behavioral analysis – enables complete, continuous monitoring. As a result, government agencies and companies can take pre-emptive action before damaging incidents occur. Equally important, SAS solutions are powerful yet simple to use, reducing the need to hire a cadre of high-end data modelers and analytics specialists. Automation of data integration and analytics processing makes it easy to deploy into daily operations.
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SAS
By: SAS     Published Date: Mar 06, 2018
The most recent decade has seen rapid advances in connectivity, mobility, analytics, scalability, and data, spawning what has been called the fourth industrial revolution, or Industry 4.0. This fourth industrial revolution has digitalized operations and resulted in transformations in manufacturing efficiency, supply chain performance, product innovation, and in some cases enabled entirely new business models. This transformation should be top of mind for quality leaders, as quality improvement and monitoring are among the top use cases for Industry 4.0. Quality 4.0 is closely aligning quality management with Industry 4.0 to enable enterprise efficiencies, performance, innovation and business models. However, much of the market isn’t focusing on Quality 4.0, since many quality teams are still trying to solve yesterday’s problems: inefficiency caused by fragmented systems, manual metrics calculations, quality teams independently performing quality work with minimal cross-functional own
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SAS
By: IBM     Published Date: Sep 11, 2013
Through this whitepaper from IBM you will learn how to use predictive intelligence to make faster and better decisions
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spss, modeling, data mining, ibm, spss modeler, business analytst
    
IBM
By: IBM     Published Date: Sep 11, 2013
Learn how customer profitability analytics enables banks to analyze customer, account, product, and transaction data and apply costing models to determine a bank-wide view of profitability
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banks, customer profitability analytics, analytics, account, product, transaction data, costing models
    
IBM
By: IBM     Published Date: Nov 14, 2014
IBM SPSS Modeler is a powerful, versatile data and text analytics workbench. Learn how you can build accurate predictive models quickly and intuitively, without programming. So you can use data to understand the current state of your organization and get a view into the future.
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ibm, webinar, business intelligence, entity analytics, modeler, automated modeling, predictive intelligence, building models, spss modeler gold, data management, change management
    
IBM
By: Symantec     Published Date: Jul 11, 2017
The technology pendulum is always swinging. And chief information security officers must be prepared to swing with it—or get clocked. A look at recent history illustrates the oscillating nature of technology. In the 1980s, IBM mainframes dominated the landscape. In the ’90s, client-server computing came on the scene and data was distributed on personal computers. When the Web assumed predominance, the pendulum started to swing back to a centralized server. Then, just as quickly, mobile took the lead, with apps downloaded to workers’ devices—the new client server. Now, as mobile devices continue to populate the enterprise at a rapid rate, the IT model is changing again—to the provisioning of information on a just-what’s-needed, just-in-time basis from centralized servers consolidated in the cloud. The pendulum continues to swing and IT workloads are moving to the cloud en masse.
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cloud, security, data protection, data loss, information security
    
Symantec
By: IBM     Published Date: Aug 05, 2014
Watson Explorer provides organizations with a combined, trusted 360-degree view of both structured and unstructured data. The solution indexes information from unstructured data sources (PDFs, Microsoft SharePoint, flat files, social media, blogs and so on) as well as structured sources for search and discovery. Organizations can leverage their data in place, respecting existing security models when creating this virtual access point to all information. Watson Explorer requires trusted master data to ensure that links are accurate and reliable, so the end user can confidently leverage a holistic view of a customer or product.
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ibm, data, customer, watson explorer, master data, structured, it management, data management
    
IBM
By: IBM     Published Date: Apr 09, 2015
As The Business Becomes Digital, Security Must Become Data-Centric S&R leaders of enterprises undergoing a digital transformation will soon realize that in order to adequately ensure customer protection and enable a digital workforce, S&R pros must abandon traditional perimeter-based security and put the focus on the data by embracing Forrester’s Zero Trust Model.
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data security, ibm, customer protection, digital workforce, zero trust model, security, it management, knowledge management, data management
    
IBM
By: IBM     Published Date: Jun 07, 2016
The database you pick for your next application matters now more than ever. It can be difficult, and oftentimes impossible, to quickly join today's data into the relational model. Learn how a NoSQL database can act as a viable alternative to or compliment an existing relational database.
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ibm, nosql database, database, analytics, networking, knowledge management, enterprise applications, database development
    
IBM
By: IBM     Published Date: Jul 12, 2016
Join us for a complimentary webinar with Mark Simmonds, IBM big data IT Architect who will talk with leading analyst Mike Ferguson of Intelligent Business Strategies about the current fraud landscape. They will discuss the business impact of fraud, how to develop a fraud-protection strategy and how IBM z Systems analytics solutions and predictive models can dramatically reduce your risk exposure and loss from fraud.
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ibm, z systems, fraud loss reduction, fraud management, fraud prevention, fraud analytics, roi, security
    
IBM
By: IBM     Published Date: Sep 30, 2016
Data is the lifeblood of today’s digital businesses; protecting it from theft, misuse, and abuse is the top responsibility of every S&R leader. Hacked customer data can erase millions in profits, stolen IP can destroy competitive advantage, and unnecessary privacy abuses can bring unwanted scrutiny and fines from regulators while damaging reputations. S&R pros must take a data-centric approach that ensures security travels with the data regardless of user population, location, or even hosting model; position data security and privacy capabilities as competitive differentiators; and build a new kind of customer relationship.
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ibm, security, data, privacy, forrester, forrester research, data management
    
IBM
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