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Published By: Cisco     Published Date: May 24, 2019
Mobile Global Workforce is predicted to surge to 1.87 billion by 2022. Be prepared for the wireless expansion with Mobility Express. Cisco’s Mobility Express* is designed for small businesses that want a controllerless solution with Wi-Fi management functions embedded in the Cisco Aironet® access point itself. Offering SMB’s Cisco’s industry-leading enterprise capabilities at a fraction of the enterprise cost.
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Cisco
Published By: SAS     Published Date: Jan 17, 2018
The Internet of Things can bring big benefits. But what exactly is IoT, and how are different industries taking advantage of it? This TDWI e-book explores in detail what IoT and the Industrial IoT (IIoT) do for retailers, the automotive industry, state and local governments working with utilities firms, and the manufacturing industry. Common themes include connectedness, data-driven insights, predictive capabilities and transformation.
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SAS
Published By: KPMG     Published Date: Dec 05, 2018
Some HR leaders are confidently harnessing the disruptive technologies that will transform the HR function. How ready are you for the digital workplace? Read this report – which includes insights from HR leaders in some of the world’s most successful organisations – to understand: the anticipated impact of artificial intelligence and disruptive technologies the growing importance of the employee experience the potential of predictive analytics the changes required in workplace culture and capabilities for successful transformation.
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KPMG
Published By: Schneider Electric     Published Date: May 31, 2019
Gartner predicts that by 2020, 90 percent of organizations will adopt hybrid infrastructure management capabilities. However, this isn’t easy. With a hybrid IT environment can come complexity, confusion, and even infrastructure fragmentation. And, if you leave this poorly managed, your users, business, and most of all customers will certainly start to notice. In this eBook, we’ll explore the evolution of the enterprise data center, impacts of cloud-powered digital solutions, and how to manage hybrid IT solutions.
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hybrid, digital solutions, schneider electric
    
Schneider Electric
Published By: HP Inc.     Published Date: Dec 06, 2017
3D printing taking steps to make impact in the marketplace. McKinsey & Company predicted the impact of 3D printing and direct product manufacturing could hit $500 billion annually 2025. But first, as John Hauer writes, the infrastructure for 3D printing – such as software, color capabilities, and affordable printing materials – needs to keep advancing.
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printing, industry, manufacturing, marketplace, color capabilities, software
    
HP Inc.
Published By: Splunk     Published Date: Aug 17, 2018
IT organizations are now responsible for delivering seamless customer experiences while preventing outages and managing an increasing number of systems. With growing responsibility placed on IT, there is an opportunity to drive strategy for company-wide business processes and operations. Companies using machine data powered platforms like Splunk collect disparate data types to quickly troubleshoot and monitor systems. By adding predictive capabilities, IT can glean critical insights for the business and develop strategic initiatives on issues that matter. Download the white paper “Embracing the Strategic Opportunity of IT” to learn how to: Enable a business aware IT organization Unlock operational efficiencies Solve problems with predictive analytics
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it event management, it event management tool, event logs, aiops platform, what is aiops, aiops vendor, market guide for aiops platforms, guide for aiops platforms, monitor end to end, itoa, aiops, predictive analysis, machine learning, event correlation, event management, it operations analytics, it analytics, ibm watson, hp monitoring, hp operations manager
    
Splunk
Published By: HP Inc.     Published Date: Jun 13, 2018
McKinsey & Company predicted the impact of 3D printing and direct product manufacturing could hit $500 billion annually 2025. But first, as John Hauer writes, the infrastructure for 3D printing – such as software, color capabilities, and affordable printing materials – needs to keep advancing. Check it out
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HP Inc.
Published By: Dassault Systèmes     Published Date: Jul 21, 2017
Project management relies primarily on past performance to predict future results, however many companies still lack forward-looking capabilities to predict project outcomes and ensure success. Enhancing project management with PLM analytics offers the opportunity to switch from task-based activities to performance-driven ones to improve success rates. Use PLM Analytics to: • Gain actionable insight and valuable intelligence • Dramatically boost business value and improve project management performance • Reduce error-prone behavior like manual data collection • Leverage big-data capabilities and project intelligence Learn how to extend the value of your PLM investment and improve business performance for your company.
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product solutions, lifecycle management, tech products, data management tools, pdm, plm, process automation, product development speed, manufactures
    
Dassault Systèmes
Published By: TIBCO Software     Published Date: Jul 22, 2019
Connected Intelligence in Insurance Insurance as we know it is transforming dramatically, thanks to capabilities brought about by new technologies such as machine learning and artificial intelligence (AI). Download this IDC Analyst Infobrief to learn about how the new breed of insurers are becoming more personalized, more predictive, and more real-time than ever. What you will learn: The insurance industry's global digital trends, supported by data and analysis What capabilities will make the insurers of the future become disruptors in their industry Notable leaders based on IDC Financial Insights research and their respective use cases Essential guidance from IDC
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TIBCO Software
Published By: Red Hat     Published Date: Dec 15, 2015
New Gartner research predicts that 75% of IT organizations will have a bimodal capability by 2017. Bimodal is a critical capability that combines the solid conventional capabilities of IT alongside a capability to respond to the level of uncertainty and the need for agility required for a digital transformation. According to Gartner, half of IT organizations that have a bimodal capability will make a mess. Learn more about Gartner's predictions for common mistakes that CIOs will make and how to avoid them.
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Red Hat
Published By: Group M_IBM Q1'18     Published Date: Feb 15, 2018
See how you can turn data into actionable insights with predictive analytics. Take our brief assessment to learn which analytical capabilities will enable you to find the greatest value in your data and make confident, accurate business decisions.
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analytics assessment, business decisions, predictive analytics, analytics
    
Group M_IBM Q1'18
Published By: Domino Data Lab     Published Date: Feb 08, 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
Published 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
Published By: MicroStrategy     Published Date: Aug 21, 2019
To survive and thrive in an era of accelerating digital disruption, organizations require accessible data, actionable insights, continuous innovation, and disruptive business models. It’s no longer enough to prioritize and implement analytics – leaders are being challenged to stop doing analytics just for analytics’ sake and focus on defined business outcomes. In addition, these leaders are being challenged to bring predictive capabilities and even prescriptive recommended actions into production at scale. As AI and accelerated growth and transformation become top of mind, many enterprises are realizing that their current segmented analytics approach isn’t built to last, and that real transformation will require proper endto- end data management, data security, and a data processing platform company-wide. The year 2019 will be a turning point for many organizations that realize being data-driven doesn’t guarantee future success.
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MicroStrategy
Published By: Epicor     Published Date: Aug 01, 2017
Like many manufacturers, your shop floor operations may be challenged with inefficient standard processes, unpredictable downtime, and difficulties with machinery maintenance. A manufacturing execution system (MES) can help make the most of your shop floor’s capabilities, with tools that can identify under-performing and high-performance machines, and then optimize asset utilization accordingly. Engineers can design, refine, and implement better work processes, and operations professionals can improve preventive maintenance and response times. The result is less downtime, less scrap, more productivity, and lower cost. With the real-time information of an MES, you can transform the shop floor into an important contributor that drives the strategic direction of your business, differentiates your brand, and positions your company as a market leader.
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enterprise resource planning (erp), manufacturing execution system (mes), manufacturing, mattec mes
    
Epicor
Published By: Epicor     Published Date: Jan 08, 2018
Like many manufacturers, your shop floor operations may be challenged with inefficient standard processes, unpredictable downtime, and difficulties with machinery maintenance. A manufacturing execution system (MES) can help make the most of your shop floor’s capabilities, with tools that can identify under-performing and high-performance machines, and then optimize asset utilization accordingly. Engineers can design, refine, and implement better work processes, and operations professionals can improve preventive maintenance and response times. The result is less downtime, less scrap, more productivity, and lower cost. With the real-time information of an MES, you can transform the shop floor into an important contributor that drives the strategic direction of your business, differentiates your brand, and positions your company as a market leader.
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enterprise resource planning (erp), manufacturing execution system (mes), manufacturing, mattec mes
    
Epicor
Published By: Alteryx, Inc.     Published Date: Sep 06, 2017
Predictive analytics is on the verge of widespread adoption as enterprises become more interested in deploying predictive capabilities. In fact, a recent 2017 TDWI education survey, ranked predictive analytics the top analytics-related topic respondents wanted to learn about. The TDWI Navigator Report- Predictive Analytics provides a comprehensive overview of the state of the predictive analytics market. Download this report to better understand: Opportunities and obstacles of implementing predictive analytics Market forces and trends driving the adoption of predictive analytics Features and market landscapes that define predictive analytics Download this report today to get a better sense on how your organization can take advantage of predictive analytics to drive change in your business.
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Alteryx, Inc.
Published By: Red Hat     Published Date: May 05, 2015
New Gartner research predicts that 75% of IT organizations will have a bimodal capability by 2017. Bimodal is a critical capability that combines the solid conventional capabilities of IT alongside a capability to respond to the level of uncertainty and the need for agility required for a digital transformation. According to Gartner, half of IT organizations that have a bimodal capability will make a mess. Learn more about Gartner's predictions for common mistakes that CIOs will make and how to avoid them.
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red hat, gartner, bimodal it, cio, it architecture, hybrid, ntegration, devops, automation, sourcing, innovation management
    
Red Hat
Published By: BMC ASEAN     Published Date: Dec 18, 2018
From the impact of disruptive technologies to the imperative of digital transformation, businesses today must find new ways to innovate or risk being left behind. While data flowing rapidly between the Internet of Things and multi-cloud computing environments brings tremendous opportunity, there’s also a great deal of complexity. Artificial intelligence (AI) and machine learning (ML) are part of the new wave of solutions capturing the minds of enterprise leaders to respond to these new opportunities and complexities. Digitally literate leaders who are highly cognizant of this wave, are jumping in headfirst and applying AI and ML to solve real business challenges—making enterprise goals of enabling cost savings via smarter operations and decision making come to fruition. BMC Cognitive Service Management (CSM) addresses the complexities of multi-cloud computing by applying intelligence, automation, and predictive capabilities. CSM employs a differentiated approach with a more holistic
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BMC ASEAN
Published By: BMC     Published Date: Nov 19, 2018
Learn how BMC Cognitive Service Management (CSM) addresses the complexities of multi-cloud computing by applying intelligence, automation, and predictive capabilities
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bmc, cognitive service management, service management, itsm, automation, digital transformation
    
BMC
Published By: IBM     Published Date: Aug 01, 2012
Discover how using predictive analytics can help your company convert prospects and cross-sell to existing customers. The campaign optimization capabilities provided with predictive analytics offer an unprecedented level of targeting and coordination across all channels - quickly resulting in decreased costs and increased revenue.
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ibm, marketing, technology, campaign profitability, predictive analytics, analytics, roi, crm, customer, business, white paper
    
IBM
Published By: Adobe     Published Date: Aug 02, 2017
With Adobe Analytics and Adobe Audience Manager—both part of Adobe Marketing Cloud—media companies can overcome today’s audience intelligence challenges. Adobe Analytics is an industry-leading solution for applying real time analytics and detailed segmentation across all of your marketing channels. A unified platform and customer ID unlock powerful customer intelligence and help you discover and retain high-value audiences. Make forward-looking decisions with its predictive intelligence capabilities, and find out which of your marketing efforts are paying off with its attribution functionality.
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goals and kpis, data warehouse, resource management, insight loop, content and campaigns, audience optimisation
    
Adobe
Published By: CDW     Published Date: Aug 04, 2016
IT decision-makers at most enterprises understand the benefits that the cloud has to offer: increased agility and more predictable budgeting, among others. In many organizations, in fact, “cloud first” has become the mantra when seeking out new software solutions. But often, enterprises are less clear on the specific use cases of Infrastructure as a Service (IaaS) and how providers can help deliver IT capabilities that meet their needs and help them to achieve desired business outcomes.
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technology, cloud, data, productivity, enterprise, business management
    
CDW
Published By: Mintigo     Published Date: Sep 05, 2018
One of the most common use cases for AI in B2B is to make predictions about which accounts are most likely to buy and which leads are most likely to convert. However, use cases for AI are being extended beyond predictive account and lead scoring to include decision-making and process automation as well. Download this SiriusDecisions technology perspective on Predictive Analytics and Artificial Intelligence Technology to learn more. This paper will cover: • The benefits, evolution and capabilities of AI technology solutions for B2B organizations • The core and extended capability groups of AI • The business priorities supported by AI Fill out the form to get your free copy!
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Mintigo
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
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