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Published By: TIBCO Software     Published Date: Jul 22, 2019
The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and the need to rework faulty products. To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support. At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.
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TIBCO Software
Published By: TIBCO Software     Published Date: Jul 22, 2019
Over the past decade there has been a major transformation in the manufacturing industry. Data has enabled a paradigm shift, with real-time IoT sensor data and machine learning algorithms delivering new insights for process and product optimization. Smart Manufacturing, also known as Industry 4.0, has laid the groundwork for the next industrial revolution. Using a smart factory system, all relevant data is aggregated, analyzed, and acted upon. We call this Manufacturing Intelligence, which gives decision-makers a competitive edge to: Digitize the business Optimize costs Accelerate innovation Survive digital disruption Watch this webinar to understand use cases and their underlying technology that helped our customers become smart manufacturers.
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TIBCO Software
Published By: TIBCO Software     Published Date: Jul 22, 2019
Global producer of polycrystalline silicon for semiconductors, Hemlock Semiconductor needed to accelerate process optimization and eliminate cost. With TIBCO® Connected Intelligence, Hemlock achieved centralized, self-service, governed analysis; revenue gains; cost savings; and more. Fueled by double-digit growth in the markets it serves, Hemlock Semiconductor is adapting to the increasing commoditization within the polysilicon industry and better positioning itself to compete. A key factor in this plan is to equip process-knowledgeable personnel with the skills and tools to accelerate delivery of process optimizations and associated cost elimination. Hemlock turned to a TIBCO® Connected Intelligence solution to address the challenges. By implementing TIBCO Spotfire® and TIBCO® Streaming analytics, TIBCO® Data Science, and TIBCO® Data Virtualization, the company created more self-service analytics. Adding TIBCO BusinessWorks™ integration let the company realize the vision of connect
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TIBCO Software
Published By: Sage Software     Published Date: Nov 12, 2018
Sage Business Cloud Enterprise Management offers you a comprehensive, real-time solution that delivers accurate, up-to-date data that identifies and mitigates the consequences of product recalls and other supply chain issues. With Sage Business Cloud Enterprise Management, your food and beverage business will have a faster, simpler and flexible way to keep the costs and reputational damage of recalls to a minimum.
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Sage Software
Published By: Group M_IBM Q119     Published Date: Dec 18, 2018
Businesses are struggling with numerous variables to determine what their stance should be regarding artificial intelligence (AI) applications that deliver new insights using deep learning. The business opportunities are exceptionally promising. Not acting could potentially be a business disaster as competitors gain a wealth of previously unavailable data to grow their customer base. Most organizations are aware of the challenge, and their lines of business (LOBs), IT staff, data scientists, and developers are working to define an AI strategy.
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Group M_IBM Q119
Published By: Group M_IBM Q119     Published Date: Dec 18, 2018
IBM LinuxONE™ is an enterprise Linux server engineered to deliver cloud services that are secure, fast and instantly scalable. The newest member of the family, IBM LinuxONE Emperor™ II, is designed for businesses where the following may be required: • protecting sensitive transactions and minimizing business risk • accelerating the movement of data, even with the largest databases • growing users and transactions instantly while maintaining operational excellence • accessing an open platform that speeds innovation
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Group M_IBM Q119
Published By: Group M_IBM Q2'19     Published Date: Apr 01, 2019
Delivering personalized customer experience remains the top business challenge for communications service providers (CSPs). Ovum's recently published 2018 ICT Enterprise survey saw almost all CSP IT executives interviewed identify delivering personalized customer experience as one of their three most important business challenges for the next 18 months. This trend emphasizes the high priority CSPs place on how customer relationships are managed. However, several factors have an impact on CSPs' ability to identify and then deliver customers' core needs. These include understanding the data sets they should focus on; collecting, cleansing, and consolidating these data sets; and having the right expertise to mine the data sets.
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Group M_IBM Q2'19
Published By: Group M_IBM Q2'19     Published Date: May 03, 2019
"Managing and securing endpoints with conventional mobile device management (MDM) or enterprise mobile management (EMM) solutions is time-consuming and ineffective. For this reason, global IT leaders are turning towards unified endpoint management (UEM) solutions to consolidate their management of smartphones, tablets, laptops and IoT devices into a single management console. To increase operational efficiency, maximize data security and deliver on their digital transformation goals, they’ll need a UEM platform that does more than just promise success. The answer is a smarter solution, built for today, that brings new opportunities, threats, and efficiency improvements to the forefront. With Watson™, IBM® MaaS360® UEM features cognitive insights, contextual analytics, and cloud-sourced benchmarking capabilities. It helps you make sense of daily mobile details while managing your endpoints, users, apps, document, and data from one platform."
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Group M_IBM Q2'19
Published By: Visier     Published Date: Jan 25, 2019
Global competition for talent, outsourcing labor, compliance legislation, remote workers, aging populations—these are just a few of the daunting challenges faced by HR organizations today. Yet the most commonly monitored workforce metrics do very little to deliver true insight into these topics. Leaders need to graduate from metrics to people analytics in order to uncover the important connections and patterns in their data that lead to better workforce decisions.
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Visier
Published 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
Published 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
Published By: CyrusOne     Published Date: Jul 05, 2016
Data centers help state and federal agencies reduce costs and improve operations. Every day, government agencies struggle to meet critical cost controls with lower operational expenses while fulfilling the Federal Data Center Consolidation Initiative’s (FDCCI) goal. All too often they are finding themselves constrained by their legacy in-house data centers and connectivity solutions that fail to deliver exceptional data center reliability and uptime.
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data center, best practices, competitive advantage, productivity
    
CyrusOne
Published By: Selligent Marketing Cloud     Published Date: Mar 07, 2018
Context can make or break the communication – and, ultimately, the relationship – between a consumer and a brand. Today’s consumers expect relevant communications that speak directly to their needs in the moment. We have the technology today to deliver such messages – but there are significant barriers to developing relevant, contextual programs of this kind. Some of the development challenges represent new versions of old challenges. Take data as an example: it has always been hard to harness data from different sources and to leverage insights in real time. But today, there are additional opportunities – if not expectations – for marketers to use contextual data to better reach and engage customers through the optimal channel(s).
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data driven marketing, contextual marketing, cmo, omnichannel, multichannel, automation, loyalty, crm, marketing, personlisation, campaign management, customer marketing, retention marketing, marketing cloud, marketing solution, marketing platform, artificial intelligence, prediction learning, product recommendation
    
Selligent Marketing Cloud
Published By: MarkLogic     Published Date: Nov 07, 2017
Business demands a single view of data, and IT strains to cobble together data from multiple data stores to present that view. Multi-model databases, however, can help you integrate data from multiple sources and formats in a simplified way. This eBook explains how organizations use multi-model databases to reduce complexity, save money, lessen risk, and shorten time to value, and includes practical examples. Read this eBook to discover how to: Get unified views across disparate data models and formats within a single database Learn how multi-model databases leverage the inherent structure of data being stored Load as is and harmonize unstructured and semi-structured data Provide agility in data access and delivery through APIs, interfaces, and indexes Learn how to scale a multi-model database, and provide ACID capabilities and security Examine how a multi-model database would fit into your existing architecture
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MarkLogic
Published By: Citrix     Published Date: Jun 15, 2018
The world of IT is undergoing a digital transformation. Applications are growing fast, and so are the users consuming them. These applications are everywhere—in the datacenter, on virtual and/or microservices platforms, in the cloud, and as SaaS. More and more apps are now being moved out of datacenters to a cloud-based infrastructure. In order for an optimized and secure delivery of these applications, IT needs specific network appliances called Application Delivery Controllers (ADCs). These ADCs come in hardware, virtual, and containerized form factors, and are sized by Network Administrators based on the current and future usage of applications. The challenge with this is that it’s hard to foresee sizing or scalability requirements for these ADCs since users are constantly increasing, and applications are consistently evolving, as well as moving out of datacenters. Complicating matters, most ADCs are fixed-capacity network appliances that provide zero or minimum expansion capability
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Citrix
Published By: Datarobot     Published Date: May 14, 2018
The DataRobot automated machine learning platform captures the knowledge, experience, and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods
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Datarobot
Published By: Sitecore     Published Date: Aug 08, 2019
The Global Trends in Personalization Study is a collaboration between SoDA and Sitecore to assess investment plans, adoption of emerging technology, organizational priorities and key challenges relative to delivering personalized digital consumer experiences. Data was collected in January and February of 2019 from 351 marketing leaders and C-level executives across North American, Europe and APAC
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Sitecore
Published By: Oracle OMC     Published Date: Nov 30, 2017
Lead nurturing is about helping buyers along in their educational journey. Thus, it’s most effective when triggered by prospect activity or behaviors. Lead management technologies are often used to automate such real-time marketing. This type of software makes it possible to track leads and automate content delivery while simultaneously collecting behavioral data and triggering corresponding actions.
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Oracle OMC
Published By: Informatica     Published Date: Oct 28, 2011
In this White Paper, Bloor Research director Philip Howard discusses how Data Replication can help you deliver active data warehousing for analytics.
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Informatica
Published By: SAS     Published Date: Apr 16, 2015
Big data has made quite an impression on organizations embarking on data journeys, hoping to glean valuable insights ranging from process optimization to customer-facing improvements. This research paper explores proven best practices that can help organizations overcome obstacles to deliver on big data potential.
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SAS
Published By: MicroStrategy     Published Date: Jun 06, 2019
A&BI platforms are transitioning from delivering simple, manual self-service to supporting more advanced, automated analytic use cases via growing, augmented, ML-driven capabilities. Data and analytics leaders should enable broader use cases to increase their investments’ business impact.
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MicroStrategy
Published By: MicroStrategy     Published Date: Jun 06, 2019
A&BI platforms are transitioning from delivering simple, manual self-service to supporting more advanced, automated analytic use cases via growing, augmented, ML-driven capabilities. Data and analytics leaders should enable broader use cases to increase their investments’ business impact.
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MicroStrategy
Published By: MicroStrategy     Published Date: Jun 11, 2019
The use of analytics has exploded across business, and the value it already has delivered has heightened executives' expectations. Now data can be processed in real time to meet a constantly widening range of analytic needs. How your organization utilizes them in the next decade will be essential to your success.
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MicroStrategy
Published By: MicroStrategy     Published Date: Jun 11, 2019
The use of analytics has exploded across business, and the value it already has delivered has heightened executives' expectations. Now data can be processed in real time to meet a constantly widening range of analytic needs. How your organization utilizes them in the next decade will be essential to your success. These developments come at an opportune time. Organizations are being over-whelmed by the rivers of data generated by applications and systems on-premises or flowing in via the cloud. At the same time, the cost of computational power has declined dramatically, making it practical to apply analytics to and generate information on just about anything. But no advance comes without challenges. While the widespread availability of analytics has created seemingly valuable insights, executives and managers are finding that those insights are not easily linked to steps that will improve business outcomes or optimize actions. Furthermore, analytics are not always easy for line of b
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MicroStrategy
Published By: MicroStrategy     Published Date: Aug 21, 2019
Ready or not, the future is here. For enterprise organizations, it must be a data-driven one. Whoever can use technology to transform the customer experience, and be the first to discover and deliver on new business models, will be the disruptor. Those who can’t, the disrupted in this period known as the “era of Digital Darwinism.” The future belongs to the Intelligent Enterprise which anticipates constantly evolving regulatory, technological, market, and competitive challenges and turns them into opportunity and profit. It delivers a single version of the truth and agility. It connects to any data and distributes reports to thousands. The Intelligent Enterprise goes beyond business intelligence, delivering transformative insight to every user, constituent and partner. Are most organizations there yet? As brands hone and focus their 2020 (and even 2030) vision, MicroStrategy has surveyed 500 enterprise analytics professionals on the state of their organization’s analytics initiatives.
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MicroStrategy
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