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By: IBM     Published Date: Nov 03, 2017
Massive shifts within the digital business landscape are sparking immense opportunities and reshaping every sector. In some cases, complete upheaval is happening at lightning-fast speed. In other instances, digital undercurrents are stirring beneath the surface as organizations scramble to monetize vast volumes and variety of data in an effort to sharpen their competitive edge and not be blindsided by unforeseen events that completely upend existing business models. While long-standing industry leadership might be no match for the next cool app, agility, speed and the ability to harness more data than was ever imagined is fueling powerful possibilities for reinvention among companies of every size. Data is following rapidly from mobile devices and social networks, as well as from every connected product, machine and infrastructure. This data holds the potential for deep insights that can replace guesswork and approximations as to locations, behaviors, patterns and preferences. As the w
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digital business, data, data-driven enterprise, innovation, ibm
    
IBM
By: Vindicia     Published Date: Oct 04, 2017
The number of services available via online subscription is exploding—and the big bang of subscription is only gaining momentum. Companies want to turn one-time purchases into regularly recurring subscriptions or memberships. The siren song of subscription success is calling to executives in virtually every industry. Business leaders want ongoing revenue streams from an army of subscribers. But, as companies that plunged into recurring business models are realizing, subscriptions are more than just transactions that recur. Subscription success can be illusive. Too often, subscription models don’t generate the expected high, ongoing revenue streams.
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Vindicia
By: TIBCO Software     Published Date: Jul 22, 2019
FINANCIAL SERVICES’ HISTORY OF DISRUPTION 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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TIBCO Software
By: TIBCO Software     Published Date: Jul 22, 2019
The Insurance industry continues to undergo significant transformation, with new technologies, business models, and competitors entering the market at an increasing rate. To be successful in attracting and retaining the most valuable customers, insurance companies must innovate and increase the speed at which they respond to customer demands. Traditionally, the insurance software market was dominated by a handful of specialist vendors with products that were initially expensive, difficult to deploy, costly to maintain, and did not provide the speed needed for today’s market. Now there has been a shift away from these “black box” applications to platforms that allow insurers to make their algorithmic IP available to business users, allowing much faster response to business demands. The algorithmic platform approach also comes at a fraction of the cost of black box solutions, while delivering advanced analytical techniques like Machine Learning and Artificial Intelligence (AI).
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TIBCO Software
By: Boardwalktech     Published Date: Jun 05, 2018
Blockchain is reimagining the way enterprises operate. It’s a powerful new enterprise technology that can be used across vertical and application domains to realize greater efficiency, support new business models and revenue sources, and enhance the security and elevate the trust of enterprise transactions.
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Boardwalktech
By: Centrica Business Solutions     Published Date: Aug 14, 2018
It's demanding times for businesses. Facing risks as diverse as extreme weather and cyber-attacks, ?rms are focusing more intently on business continuity plans. At the same time, the digital revolution is changing lifestyles, disrupting business models and acting as a catalyst for change. Energy itself is undergoing a transformation, as generation moves towards renewables and decentralization, while technological advances present new opportunities. Energy now has the potential to deliver competitive advantage, to enhance sustainability and resilience, and to power innovative business models. But equally, the range of new options available is making the management of energy more complex. Read this thought leadership paper to find out how energy can drive your business strategy.
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business, strategy, energy, efficiency, performance
    
Centrica Business Solutions
By: Amazon Web Services EMEA     Published Date: Sep 09, 2019
City leaders around the world are working to improve the quality of life and increase the number of useful services for their citizens, businesses, and visitors. Smart city initiatives are gaining attention in several countries, as administrators, industry, and citizens are working together to enhance their communities, cities, regions, and nations. After more than ten years of smart city hype, innovative projects have emerged, but few have provided a disruptive impact on the city system itself. Because every city is different, few of the existing models apply universally. An effective response requires leaders to consider a series of prerequisites: • Consider the local culture and the people • Prioritize basic needs and requests • Work at overcoming silos and encourage collaboration across departments • Consider each “smart” initiative as an integral part of an overall city system A smart city reflects a number of aspirations: efficiency, resiliency, connectedness, innovatio
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Amazon Web Services EMEA
By: Group M_IBM Q2'19     Published Date: May 31, 2019
In the Garage, you work side by side with IBM experts to create first-of-a-kind MVPs, experiment with emerging technologies and quickly learn from failures. In this webinar hear from Ed Forman, Partner of Cognitive Process Transformation at IBM. Ed has partnered with organizations across industries including automotive, life sciences, financial services and telecommunications. In his work with the IBM Garage he has helped shape an award-winning method for innovation that has transformed business models for Fortune 500 clients. Listen to this 30-minute presentation to find out more.
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Group M_IBM Q2'19
By: Group M_IBM Q3'19     Published Date: Jun 24, 2019
The advantages blockchain can bring to the automotive ecosystem, both in facilitating collaboration among participants and enabling capabilities for new mobility business models, have gotten the attention of automotive executives. In addition to enabling a single source of data, blockchain can facilitate device-to-device transactions, smart contracts, and real-time processing and settlement. For the automotive industry, this translates into improvements and operational efficiencies in areas such as supply chain transparency, financial transactions between ecosystem participants, authenticating access to cars, and customer experience and loyalty.
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Group M_IBM Q3'19
By: Group M_IBM Q3'19     Published Date: Jun 24, 2019
"Use Watson Marketing capabilities to discover insights previously unknown, and use them to create new business models and revenue streams. Download the white paper to discover how you can better target and engage with customers."
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Group M_IBM Q3'19
By: Group M_IBM Q3'19     Published Date: Aug 13, 2019
In the Garage, you work side by side with IBM experts to create first-of-a-kind MVPs, experiment with emerging technologies and quickly learn from failures. In this webinar hear from Ed Forman, Partner of Cognitive Process Transformation at IBM. Ed has partnered with organizations across industries including automotive, life sciences, financial services and telecommunications. In his work with the IBM Garage he has helped shape an award-winning method for innovation that has transformed business models for Fortune 500 clients. Listen to this 30-minute presentation to find out more.
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Group M_IBM Q3'19
By: Group M_IBM Q3'19     Published Date: Aug 13, 2019
In the Garage, you work side by side with IBM experts to create first-of-a-kind MVPs, experiment with emerging technologies and quickly learn from failures. In this webinar hear from Ed Forman, Partner of Cognitive Process Transformation at IBM. Ed has partnered with organizations across industries including automotive, life sciences, financial services and telecommunications. In his work with the IBM Garage he has helped shape an award-winning method for innovation that has transformed business models for Fortune 500 clients. Listen to this 30-minute presentation to find out more.
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Group M_IBM Q3'19
By: Workplace by Facebook     Published Date: Jun 24, 2019
The business landscape is changing fast. That much is obvious. Business models are being disrupted, industry mainstays unseated and old certainties upended on a near-daily basis. Technology is unlocking new possibilities. But it’s also creating new expectations - especially at work. By 2020, Gen Y will make up 35% of the workforce. This generation has grown up with open communication platforms like Facebook and Instagram. Now, they’re bringing that same expectation of openness to work. Download this report to uncover why understanding what Gen Y want in a job, as well as an employer, is key to future-proofing businesses.
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Workplace by Facebook
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
By: Domino Data Lab     Published Date: Feb 08, 2019
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform. This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
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Domino Data Lab
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
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: Virgin Media Business     Published Date: Aug 28, 2019
The world is now digital. From the explosive expansion in data-driven service delivery to digitally disruptive business models such as Uber and Netflix that have fundamentally changed the way we consume products, the digital evolution is unavoidable. As digital continues to advance, it’s crucial that UK businesses can be confident in their ability to keep up to date with the latest trends and technologies. But enhancing existing tools and models is just the beginning. Digital transformation is about taking advantage of new innovations that completely change the way businesses work, the experiences they offer their customers and the value they deliver within their market. To find out more download this whitepaper today.
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Virgin Media Business
By: Amazon Web Services     Published Date: Feb 01, 2018
Machine learning is proving its power across virtually every industry in ways that add actionable insight and efficiency. But one can look at the rise of this transformative paradigm with a more focused lens to see AI technologies as a business tool of the highest order, one that improves processes and inspires new models. AI, in other words, has a big role to play on the balance sheet. Two leading brands in very different spaces — Capital One in financial services, John Deere in agriculture — are seeing efforts that stretch back decades come to fruition with the launch of cloud-based AI platforms. Capital One is developing digital products and experiences using machine learning to help millions of customers with their financial lives; John Deere’s Precision Agriculture solution helps farmers gain precise information about their machines and crops. In both instances, AI and a cloud platform combine to enable transformation.
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digital, technologies, optimization, amazon
    
Amazon Web Services
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
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
By: Oracle OMC     Published Date: Nov 30, 2017
The headwinds facing traditional and online retailers in 2017 are numerous and well documented. Chief among the challenges: a sustained assault on established business models driven by changing consumer behavior and the competitive might of e-commerce giant Amazon. Large big-box players, such as Walmart and Target, are responding to the threat - and embracing the opportunity of e-commerce - by boosting online spending by hundreds of millions or even billions of dollars annually. Yet even the largest and most well-resourced players can’t simply spend their way to success. Competing with Amazon and adjusting to the changing demands of consumers means taking a hard look at the engines driving e-commerce and digital marketing operations, scouring for missed opportunities, and investing in the technologies that will fuel the innovations of tomorrow.
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Oracle OMC
By: Oracle OMC     Published Date: Nov 30, 2017
The headwinds facing traditional and online retailers in 2017 are numerous and well documented. Chief among the challenges: a sustained assault on established business models driven by changing consumer behavior and the competitive might of e-commerce giant Amazon. Large big-box players, such as Walmart and Target, are responding to the threat - and embracing the opportunity of e-commerce - by boosting online spending by hundreds of millions or even billions of dollars annually. Yet even the largest and most well-resourced players can’t simply spend their way to success. Competing with Amazon and adjusting to the changing demands of consumers means taking a hard look at the engines driving e-commerce and digital marketing operations, scouring for missed opportunities, and investing in the technologies that will fuel the innovations of tomorrow.
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Oracle OMC
By: SAS     Published Date: Sep 05, 2019
Envision this situation at a growing bank. Its competitive landscape demands an agile response to evolving customer needs. Fortunately, analytically minded professionals in different divisions are seeing results that positively affect the bottom line. • A data scientist in the business development team analyzes data to create customized • experiences for premium customers. • A digital marketer tracks and influences the customer journey for prospective • mortgage customers. • A risk analyst builds risk models for the bank’s loan portfolios. • A data analyst examines data about local customers. • A technical architect defines a new system to protect bank data from internal and • external cyberthreats. • An application developer builds a new mobile app for online customer portfolio • management. Between them, these employees might be using more than a dozen packages for analytics and data management.
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SAS
By: MicroStrategy     Published Date: Aug 29, 2019
Dresner Advisory Services’ eighth annual Cloud Computing and Business Intelligence Market Study in 2019 shows user confidence at an all-time high, with cloud offerings outnumbering traditional on-premises offerings. Sentiment toward cloud and cloud BI turned sharply positive in 2018, with current and future user plans both trending up. Results from our 2019 Dresner Advisory Services New Year User Survey reinforced those findings with ongoing momentum. Sentiment is at an all-time high, and the majority of organizations—51 percent—use cloud BI in 2019. Cloud-based BI vendor products and services are now more plentiful than traditional onpremises products. Still, vendors support any deployment option the customer chooses: public, private, or hybrid, at multiple levels of scale and price. Customers benefit from these choices, which reduce the chance for vendor lock-in. Users have not settled on deployment models but are migrating toward subscription and the adoption of managed services. Ou
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MicroStrategy
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