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By: IBM     Published Date: Apr 19, 2018
IBM DB2 with BLU Acceleration helps tackle the challenges presented by big data. It delivers analytics at the speed of thought, always-available transactions, future-proof versatility, disaster recovery and streamlined ease-of-use to unlock the value of data.
Tags : db2, data migration, ibm, oracle
     IBM
By: IBM     Published Date: Jun 04, 2018
"The appearance of your reports and dashboards – the actual visual appearance of your data analysis -- is important. An ugly or confusing report may be dismissed, even though it contains valuable insights about your data. Cognos Analytics has a long track record of high quality analytic insight, and now, we added a lot of new capabilities designed to help even novice users quickly and easily produce great-looking and consumable reports you can trust. Watch this webinar to learn: • How you can more effectively communicate with data. • What constitutes an intuitive and highly navigable report • How take advantage of some of the new capabilities in Cognos Analytics to create reports that are more compelling and understandable in less time. • Some of the new and exciting capabilities coming to Cognos Analytics in 2018 (hint: more intelligent capabilities with enhancements to Natural Language Processing, data discovery and Machine Learning)."
Tags : data analysis, data analytics, dashboards
     IBM
By: IBM     Published Date: Jun 04, 2018
"Today’s business users want to use all types of data to create compelling, shareable visualizations. But charts and graphs alone may not convey all the information, especially when they are part of a complex series. An audience can best understand analytic results when those results tell a story that connects all the pieces together. The right visuals can also reinforce the lessons buried in the data. Stories are powerful mechanism to communicate with people. Stories stick and make insights actionable, so it goes without saying that storytelling is a very powerful (soft) skill. In this webinar, you'll learn how to effectively apply storytelling best practices to get your message across. Especially in the world of BI, it is getting more and more important to effectively communicate business results. Watch this webinar to learn how to use IBM Cognos Analytics to: · Create the important elements of a good story · Put the data in context · Select the best type of ch
Tags : data analytics, data storytelling, business intelligence
     IBM
By: IBM     Published Date: Jun 04, 2018
"What would you do if you didn’t have to rely on disparate analytics solutions to meet the needs of business users while following the rules of IT? View this 'Charting Your Analytical Future' webinar to learn about a world of innovation and independence for users that does not limit the confidence and controls of IT. With the cognitive-guided self-service features available in IBM business analytics solutions, more users than ever before can get the answers they need. Next-generation business analytics capabilities make it possible to access relevant data, prepare it for analysis and understand performance. But it doesn’t stop there. Users can package the results in a visually-appealing format and share them throughout the organization. Don’t miss this opportunity to hear how you can: * Benefit from advanced analytics without the complexity * Operationalize insights and dashboards from a collection of trusted data sources * Tell your story with rich visualizations and geospati
Tags : business analytics, analytics solutions
     IBM
By: IBM     Published Date: Jul 02, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does. From an IT perspective, there is a fairly straightforward sequence of applications that businesses can adopt over time that will help put direction into this journey. IDC outlines this sequence to e
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     IBM
By: IBM     Published Date: Jul 05, 2018
Data is the lifeblood of business. And in the era of digital business, the organizations that utilize data most effectively are also the most successful. Whether structured, unstructured or semi-structured, rapidly increasing data quantities must be brought into organizations, stored and put to work to enable business strategies. Data integration tools play a critical role in extracting data from a variety of sources and making it available for enterprise applications, business intelligence (BI), machine learning (ML) and other purposes. Many organization seek to enhance the value of data for line-of-business managers by enabling self-service access. This is increasingly important as large volumes of unstructured data from Internet-of-Things (IOT) devices are presenting organizations with opportunities for game-changing insights from big data analytics. A new survey of 369 IT professionals, from managers to directors and VPs of IT, by BizTechInsights on behalf of IBM reveals the challe
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     IBM
By: IBM     Published Date: Aug 08, 2018
An IBM Cloud configuration completed a big data analytics workload in less time and with greater throughput than an AWS solution
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     IBM
By: Group M_IBM Q418     Published Date: Dec 18, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does.
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     Group M_IBM Q418
By: ServiceNow     Published Date: Sep 18, 2018
What is a performance based business? A performance-based business is an organization guided by data-driven decisions. It is proactive, self-aware, and highly competitive. Data isn’t siloed in a business analytics department. Instead, the right people have the right data at the right time and in the right context. The top 5 reasons to become a performance-based business: 1. Get better results. 2. Align your entire business. 3. Make data-driven decisions. 4. Manage change more effectively. 5. Spot trends faster. Discover how ServiceNow Performance Analytics could benefit your business by downloading this eBook.
Tags : performance, business, data, servicenow
     ServiceNow
By: ServiceNow     Published Date: Sep 18, 2018
Worldpay deployed ServiceNow Performance Analytics to replace multiple data tools and promote the use of analytics throughout its organization for improved decision making. Nucleus found the project enabled the company to boost analyst productivity, while simultaneously increasing data accessibility and engagement for hundreds of additional employees. Download this case study to learn more
Tags : nucleus, research, worldpay, servicenow
     ServiceNow
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
By: Group M_IBM Q119     Published Date: Dec 18, 2018
Digital transformation is not a buzzword. IT has moved from the back office to the front office in nearly every aspect of business operations, driven by what IDC calls the 3rd Platform of compute with mobile, social business, cloud, and big data analytics as the pillars. In this new environment, business leaders are facing the challenge of lifting their organization to new levels of competitive capability, that of digital transformation — leveraging digital technologies together with organizational, operational, and business model innovation to develop new growth strategies. One such challenge is helping the business efficiently reap value from big data and avoid being taken out by a competitor or disruptor that figures out new opportunities from big data analytics before the business does.
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     Group M_IBM Q119
By: Group M_IBM Q119     Published Date: Jan 04, 2019
Digital transformation is poised to change the supply chain more profoundly than any other functional area and more dramatically than at any point in its history in terms of driving efficiency and resiliency to disruption. In the context of the challenges facing supply chains, both now and in the future, it becomes clear that the old ways of working will not suffice and that even best-in-class performance today is unlikely to be good enough in the future. It is the view of IDC that the supply chain must become a "thinking" supply chain, one that is intimately connected to all data sources, enabled with comprehensive and fast analytics, openly collaborative through cloud-based commerce networks, conscious of cyberthreats, and cognitively interwoven. According to IDC supply chain research, technology is emerging as a prime driver of change, particularly artificial intelligence, blockchain, and the Internet of Things (IoT).
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     Group M_IBM Q119
By: Group M_IBM Q119     Published Date: Jan 08, 2019
The discipline of data quality assurance ensures that data is "fit for purpose" in the context of existing business operations, analytics and emerging digital business scenarios. It covers much more than just technology. It includes program management, roles, organizational structures, use cases and processes (such as those for monitoring, reporting and remediating data quality issues). It is also linked to broader initiatives in the field of enterprise information management (EIM), including information governance and master data management (MDM)
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     Group M_IBM Q119
By: Group M_IBM Q119     Published Date: Feb 21, 2019
Mobile marketing platforms facilitate direct marketing on mobile devices by enabling mobile campaign management and activation. These platforms can operate as stand-alone solutions; however, they typically integrate with, or operate alongside, CRM, location/data management, and multichannel marketing hub (MMH) or email marketing platforms. Regardless of how a mobile marketing platform gets deployed, the native or third-party analytics supporting its audience targeting, campaign sequencing, personalization and performance measurement capabilities form the foundation of this technology.
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     Group M_IBM Q119
By: Group M_IBM Q119     Published Date: Mar 04, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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     Group M_IBM Q119
By: Group M_IBM Q119     Published Date: Mar 11, 2019
In this paper, we focus on the DWA and how it has evolved over the years since its introduction. The XDW architecture is then described, in which the need to maintain the data warehouse is documented while adding new components and capabilities to extend the analytical capabilities. This section also discusses the appropriate usage of appliances within the XDW. The rest of the paper covers the benefits from implementing the DWA, the selection considerations for them and what the future holds for them.
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     Group M_IBM Q119
By: Group M_IBM Q2'19     Published Date: Apr 02, 2019
There can be no doubt that the architecture for analytics has evolved over its 25-30 year history. Many recent innovations have had significant impacts on this architecture since the simple concept of a single repository of data called a data warehouse. First, the data warehouse appliance (DWA), along with the advent of the NoSQL revolution, selfservice analytics, and other trends, has had a dramatic impact on the traditional architecture. Second, the emergence of data science, realtime operational analytics, and self-service demands has certainly had a substantial effect on the analytical architecture.
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     Group M_IBM Q2'19
By: Group M_IBM Q2'19     Published Date: Apr 03, 2019
The growing need for data governance, risk and compliance, data analysis and data value still drives strategic requirements in metadata management and the growth of its solutions. Data and analytics leaders can use this vendor evaluation to find the most appropriate solution for their organization.
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     Group M_IBM Q2'19
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
By: Group M_IBM Q2'19     Published Date: May 28, 2019
However, big data and analytics solutions can have shortcomings. Proprietary and best-of-breed approaches can require valuable time and resources to build, integrate and maintain — while outsourcing data analytics can constrain reporting frequency and timeliness. In a world where operational efficiency and fast, reliable information is paramount, these limitations can put payers at a competitive disadvantage.
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     Group M_IBM Q2'19
By: Group M_IBM Q3'19     Published Date: Jun 27, 2019
Organizations continue to rush down the digital transformation path. Whether by modernizing their IT infrastructures, leveraging the cloud, or becoming data-centric and data-driven, organizations must become more agile in their business practices and within their IT infrastructure stack to effectively compete in today’s dynamic business environment. Between the speed and distributed nature of modern businesses, as well as the expectation of instantaneous access to data from everyday users, it’s not surprising that nearly one in three organizations are looking into ways to improve data analytics for real-time business intelligence and customer insight.
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     Group M_IBM Q3'19
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
By: Visier     Published Date: Jan 25, 2019
John Schwarz founded Visier to address what he saw as the major failing of business intelligence and big data analytics. He had a front row seat in this market while leading Business Objects, the largest global business intelligence provider (acquired by SAP). John and co-founder Ryan Wong’s vision was to completely reinvent the approach to analytics, providing instant and complete, domain-specific applications to business leaders, answering their important strategic questions and leading them to adopt best management practices. Their applied business analytics project is working. Today, more than a hundred blue chip companies have selected Visier as their people strategy platform and are achieving incredible results. And that’s just the beginning.
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     Visier
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
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