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Results 1 - 25 of 350Sort Results By: Published Date | Title | Company Name
By: TIBCO Software     Published Date: Jul 22, 2019
What if you could use just one platform to detect all types of major financial crimes? One platform to handle the analytical tasks of fraud detection, including: Data processing and aggregation Data visualization Statistical/mathematical/machine learning modeling Batch/real-time scoring One platform that could successfully reduce complex and time-consuming fraud investigations by combining extremely different domains of knowledge including Business, Economics, Finance, and Law. A platform that can cover payments, credit card transactions, and know your customer (KYC) processes, as well as similar use cases like anti-money laundering (AML), trade surveillance, and crimes such as insurance claims fraud. Learn more about TIBCO's comprehensive software capabilities behind tackling all these types of fraud in this in depth whitepaper.
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TIBCO Software
By: RMS     Published Date: Jul 18, 2019
When evaluating single risks, underwriters and coverholders typically have to request exposure analytics from their portfolio managers and brokers, or gather their own supplementary risk data from a range of external resources, whether it is from Catastrophe Risk Evaluation and Standardizing Target Accumulations (CRESTA) zones, through to lookups on Google Maps. But all this takes valuable time, requires multiple user licenses and can generate information that is inconsistent with the underlying modeling data at the portfolio level.
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RMS
By: Paladina Health     Published Date: Jul 12, 2019
Watch our webinar to gain “4 Creative Cost Strategies Changing Healthcare for Good.” You’ll hear healthcare experts from AON, Activate Healthcare, and Paladina Health share key insights on how to reduce employee healthcare expenses by understanding: • 4 strategies to curb employer and employee healthcare costs • What employers really want in their employee benefit offerings • What the future of healthcare looks like—and why it’s brighter than it seems WATCH NOW
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rising employer healthcare costs, employer-sponsored healthcare, employer healthcare costs, population health & data utilization, funding models, personalized healthcare, better healthcare access, future of healthcare, employee benefit offerings, per-employee-per-month, one point-of-care, fee-for-service, direct primary care, primary care benefits, telemedicine, on-site or near site clinics, wellness screenings, 24/7 provider access, value-driven healthcare system, health data
    
Paladina Health
By: Group M_IBM Q2'19     Published Date: Jul 01, 2019
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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Group M_IBM Q2'19
By: Group M_IBM Q3'19     Published Date: Jul 01, 2019
This white paper considers the pressures that enterprises face as the volume, variety, and velocity of relevant data mount and the time to insight seems unacceptably long. Most IT environments seeking to leverage statistical data in a useful way for analysis that can power decision making must glean that data from many sources, put it together in a relational database that requires special configuration and tuning, and only then make it available for data scientists to build models that are useful for business analysts. The complexity of all this is further compounded by the need to collect and analyze data that may reside in a classic datacenter on the premises as well as in private and public cloud systems. This need demands that the configuration support a hybrid cloud environment. After describing these issues, we consider the usefulness of a purpose-built database system that can accelerate access to and management of relevant data and is designed to deliver high performance for t
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Group M_IBM Q3'19
By: Genesys     Published Date: Jun 19, 2019
Successfully managing a contact center requires a collaborative, multidisciplinary approach to handle a broad range of operational and tactical tasks. Planning, day-to-day operations and quality management must be seamlessly orchestrated, along with human resources functions like recruitment, learning and development, and employee scheduling. Read this executive brief to learn how to transition to an AI strategy that can take your team – and business results – to the next level. See how you can: Create an AI strategy with a single data model that includes routing, interaction analytics, forecasting/scheduling and predictive engagement Harness the power of your data to align customers with the best resource Drive employee effectiveness by ensuring you hire the right people and manage their performance to drive their success over the long term
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Genesys
By: Red Hat     Published Date: Jun 19, 2019
Technology has fundamentally changed the way we live. Access to data and information anytime, anywhere is no longer a luxury—it is a requirement, in both our personal and professional lives. For IT organizations, this means pressure has never been greater to deliver higher-quality applications more often, enabling companies to stay relevant and seize digital business opportunities. Cloud-native is an approach to building applications that takes advantage of cloud computing models and DevOps principles to make the delivery of new features and services faster and more flexible. With a cloud-native strategy, organizations can begin the culture, process, and technology changes needed to meet new demands and become an IT organization that can deliver business innovation faster. The following checklist will assess your needs and possible business impacts to help you choose a cloud-native platform that benefits the business, developers, and IT operations team.
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Red Hat
By: AWS     Published Date: Jun 11, 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: Jun 11, 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: Jun 11, 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: Forcepoint     Published Date: Jun 06, 2019
Things aren't what they used to be in the enterprise. Your employees are mobile and they're storing and accessing data in cloud apps—often in disparate networks. That presents a serious challenge for old-school threat-centric security models that force you to make decisions with little insight or broader context. Download our eBook to find out how a human-centric, risk-adaptive approach to data security can free up your overwhelmed security team to focus on investigations that really matter.
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Forcepoint
By: Domino Data Lab     Published Date: May 23, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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Domino Data Lab
By: Domino Data Lab     Published Date: May 23, 2019
Lessons from the field on managing data science projects and portfolios The ability to manage, scale, and accelerate an entire data science discipline increasingly separates successful organizations from those falling victim to hype and disillusionment. Data science managers have the most important and least understood job of the 21st century. This paper demystifies and elevates the current state of data science management. It identifies best practices to address common struggles around stakeholder alignment, the pace of model delivery, and the measurement of impact. There are seven chapters and 25 pages of insights based on 4+ years of working with leaders in data science such as Allstate, Bayer, and Moody’s Analytics: Chapters: Introduction: Where we are today and where we came from Goals: What are the measures of a high-performing data science organization? Challenges: The symptoms leading to the dark art myth of data science Diagnosis: The true root-causes behind the dark art m
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Domino Data Lab
By: Domino Data Lab     Published Date: May 23, 2019
This paper introduces the practice of Model Management, an organizational capability to develop and deliver models that create a competitive advantage. Today, the best-run companies run their business on models, and those that don’t face existential threat. The paper explains why companies that fail to run on models are falling for the Model Myth—the assumption that models can be managed like software or data. Models are different and need a new organizational capability: Model Management. What’s inside: Defining a model Why models matter for businesses Why companies fall for the Model Myth A framework for Model Management Practical steps to get started The paper is intended for anyone in a data science organization, or anyone who hopes to use data science as a key source of competitive advantage for their business.
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Domino Data Lab
By: IBM APAC     Published Date: May 14, 2019
Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it. In this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company. This information helps both business and technical leaders learn how to apply machine learning to anticipate and predict the future. You will find topics like: - What is machine learning? - Explaining the business imperative - The key machine learning algorithms - Skills for your data science team - How businesses are using machine learning - The future of machine learning
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IBM APAC
By: Infinidat EMEA     Published Date: May 14, 2019
Infinidat has developed a storage platform that provides unique simplicity, efficiency, reliability, and extensibility that enhances the business value of large-scale OpenStack environments. The InfiniBox® platform is a pre-integrated solution that scales to multiple petabytes of effective capacity in a single 42U rack. The platform’s innovative combination of DRAM, flash, and capacity-optimized disk, delivers tuning-free, high performance for consolidated mixed workloads, including object/Swift, file/Manila, and block/Cinder. These factors combine to cut direct and indirect costs associated with large-scale OpenStack infrastructures, even versus “build-it-yourself” solutions. InfiniBox delivers seven nines (99.99999%) of availability without resorting to expensive replicas or slow erasure codes for data protection. Operations teams appreciate our delivery model designed to easily drop into workflows at all levels of the stack, including native Cinder integration, Ansible automation pl
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Infinidat EMEA
By: DataCore     Published Date: Apr 23, 2019
In our developing digital economy, IT is a strategic asset. By effectively leveraging data, businesses become more operationally efficient, create more differentiated customer experiences, and develop new products and business models. However, unlocking those benefits requires a higher degree of execution by IT. Simply keeping pace with demand is no longer good enough; IT needs to help drive the business’s digital pursuits. The increased pressure on IT has amplified complexity, as well—66% of IT decision makers surveyed by ESG say IT is more complex than it was just two years ago.1 Demands being placed on IT are scaling relentlessly, and the tools IT teams use are in a constant state of evolution. Integrating and optimizing those new infrastructure technologies while managing existing investments is a perpetual burden. IT organizations, therefore, have two choices: either increase their personnel and budgets enough to survive the evolution with just traditional tools, or redirect those
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software defined storage, hyperconverged, storage consolidation, storage management, data migration, storage migration
    
DataCore
By: Zaloni     Published Date: Apr 23, 2019
Although data and analytics are highlighted throughout the popular press as well as in trade publications, too many managers think the value of this data processing is limited to a few numerically intensive fields such as science and finance. In fact, big data and the insights that emerge from analyzing it will transform every industry, from “precision farming” to manufacturing and construction. Governments must also be alert to the value of data and analytics as the enabler for smart cities. Institutions that master available data will leap ahead of their less statistically adept competitors through many advantages: finding hidden opportunities for efficiency, using data to become more responsive to clients, and developing entirely new and unanticipated product lines. The average time spent by most companies on the S&P 500 Index has decreased from an average of 60 to 70 years to only 22 years. There are winners and losers in the changes that come with the evolution of both technology
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Zaloni
By: Oracle EMEA     Published Date: Apr 15, 2019
Oracle Autonomous Data Warehouse Cloud is more than just a new way to store and analyze data; it’s a whole new approach to getting more value from your data. Market leaders in every industry depend on analytics to reach new customers, streamline business processes, and gain a competitive edge. Data warehouses remain at the heart of these business intelligence (BI) initiatives, but traditional data-warehouse projects are complex undertakings that take months or even years to deliver results. Relying on a cloud provider accelerates the process of provisioning data-warehouse infrastructure, but in most cases database administrators (DBAs) still have to install and manage the database platform, then work with the line-of-business leaders to build the data model and analytics. Once the warehouse is deployed—either on premises or in the cloud—they face an endless cycle of tuning, securing, scaling, and maintaining these analytic assets. Oracle has a better way. Download this whitepaper to f
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Oracle EMEA
By: Group M_IBM Q2'19     Published Date: Apr 10, 2019
A new era of business reinvention is dawning. Organizations are facing an unprecedented convergence of technological, social and regulatory forces. As artificial intelligence, automation, Internet of Things, blockchain and 5G become pervasive, their combined impact will reshape standard business architectures. The “outside-in” digital transformation of the past decade is giving way to the “inside-out” potential of data exploited with these exponential technologies. We call this next-generation business model the Cognitive Enterprise.
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Group M_IBM Q2'19
By: Anaplan     Published Date: Apr 09, 2019
Connected organizations collaborate across business functions to dynamically steer business performance. Previous generations of planning software have fallen short of this vision, making collaboration difficult to achieve, scattering data across multiple sources, and providing inflexible planning models that require heavy IT support. This landscape motivated Anaplan to develop an innovative platform that enables Connected Planning across the entire enterprise. The FSN Innovation Showcase highlights three major innovations that support these objectives: • Anaplan’s proprietary Hyperblock® technology • The App Hub, a suite of 250+ industry-leading solutions • Developments in machine learning and artificial intelligence
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Anaplan
By: VMware     Published Date: Apr 09, 2019
To help their business clients unleash innovation and support growth, IT teams need to evolve to a modern data center that is virtualized, software defined and automated, with a consistent operational model for infrastructure and application delivery. In this collection of case studies, we reveal how companies are building more agile, innovative businesses based on VMware solutions.
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VMware
By: VMware     Published Date: Apr 09, 2019
Lower your data center costs with VMware vSan. In this infographic, we’ll show you how to reduce CAPEX and OPEX with a pay-as-you-grow model that enables rapid application deployment and easy ongoing management.
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VMware
By: Anaplan     Published Date: Apr 02, 2019
Connected organizations collaborate across business functions to dynamically steer business performance. Previous generations of planning software have fallen short of this vision, making collaboration difficult to achieve, scattering data across multiple sources, and providing inflexible planning models that require heavy IT support. This landscape motivated Anaplan to develop an innovative platform that enables Connected Planning across the entire enterprise. The FSN Innovation Showcase highlights three major innovations that support these objectives: • Anaplan’s proprietary Hyperblock® technology • The App Hub, a suite of 250+ industry-leading solutions • Developments in machine learning and artificial intelligence
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Anaplan
By: Here Technologies     Published Date: Apr 02, 2019
In this report, VSI applies HERE’s HD map data to a lane keeping application and examines performance of lane keeping with a map-based approach compared to a camera and computer vision-based approach. VSI tested the lane keeping system with and without map data on a local road in 3 scenarios: Lane lines expanding into a turn or exit lane An intersection without lane lines A widening in the lane The results show that in all scenarios, the computer-vision-only lane keeping systems got confused and made errors in a vehicle’s trajectory when lane markings were out of the ordinary or invisible. Faced with the same road conditions, the map-based lane keeping system stayed within the desired trajectory outperforming the compute- vision-only systems. This report proves that using a lane model from an HD map can solve common issues involved in computer-vision-only lane keeping.
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over the air technologies, location data, auto, mapping
    
Here Technologies
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