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business intelligence

Results 201 - 225 of 919Sort Results By: Published Date | Title | Company Name
By: Looker     Published Date: Dec 03, 2015
Everywhere you look, companies are using external-facing analytics to maximize the value derived from their data assets, by moving customers up the value chain, increasing stickiness, and offering a more competitive product on the marketplace. Listen to learn about embedding BI software, including; • Top uses cases for embedding business intelligence software • Case studies from different companies currently embedding BI • Build vs buy considerations • Evaluating ROI
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Looker
By: Looker     Published Date: Dec 03, 2015
The focus of modern business intelligence has been self-service; pushing data into the hands of end users more quickly with more accessible user interfaces so they can get answers fast and on their own. This has helped alleviate a major BI pain point: centralized, IT-dominated solutions have been too slow and too brittle to serve the business. What has been masked is a lack of innovation in data modeling. Data modeling is a huge, valuable component of BI that has been largely neglected. In this webinar, we discuss Looker’s novel approach to data modeling and how it powers a data exploration environment with unprecedented depth and agility. Topics covered include: • A new architecture beyond direct connect • Language-based, git-integrated data modeling • Abstractions that make SQL more powerful and more efficient
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Looker
By: SAP     Published Date: Jul 18, 2016
As your business transitions to a digital enterprise, you can start to give your employees, partners, and customers immediate, data-driven, and even predictive insight into what’s going on – in a way that’s relevant for them. Strategic use of on premise and cloud-based analytics accelerates this process. Read the solution brief to see how SAP is continuing to invest in on premise solutions as part of their effort to reimagine analytics.
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SAP
By: SAP     Published Date: Jul 18, 2016
Traditional business intelligence tools provide limited explanations of why something happened, because most BI solutions are geared more for reporting and dashboarding workflows.
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SAP
By: SAP     Published Date: Jul 18, 2016
To determine the status of Analytics and Business Intelligence in the Cloud, Enterprise Management Associates (EMA) embarked on an end-user research study to look at the current state of cloud-based analytics. Read the white paper to discover panelists’ insights on cloud-based analytics, business intelligence strategies, and implementation practices.
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SAP
By: IBM     Published Date: May 03, 2016
The new frontier for personalized customer experience: IBM Predictive Customer Intelligence This paper introduces the IBM Predictive Customer Intelligence solution, which is designed to help your company create personalized, relevant experiences for individual customers with a focus on driving new revenue. Along with explaining the architecture of the solution, this paper covers how the solution works.
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ibm, business analytics, ibm predictive customer intelligence, business intelligence, customer relationship management
    
IBM
By: IBM     Published Date: Jul 27, 2016
IBM i2 Enterprise Insight Analysis helps analysts and investigators turn large data sets into comprehensive intelligence, in near real-time. With the help of advanced analytics and visual analysis capabilities, analysts can uncover hidden connections, patterns and trends buried in disparate data. Equip analysts and those on the front line with the tools they need to generate actionable intelligence, with mission critical speed.
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ibm, cyber threat analysis, ibm i2 enterprise insight analysis, cyber security, business intelligence, security
    
IBM
By: IBM     Published Date: Jul 27, 2016
IBM's i2 Analyst’s Notebook offers a wide range of analysis and visualization capabilities that can aid in the identification of key actionable intelligence. Download this IBM White Paper to discover and deliver actionable intelligence to help identify, predict and prevent criminal, terrorist, and fraudulent activities with IBM i2 Analyst's Notebook.
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ibm, cyber threat analysis, ibm i2 analyst notebook, analyst, cyber security, business intelligence, security
    
IBM
By: IBM     Published Date: Jul 27, 2016
Learn how to counter and mitigate more attacks with cyber threat analysis.
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ibm, cyber threat analysis, ibm i2 enterprise insight analysis, cyber security, business intelligence, security
    
IBM
By: Veritas     Published Date: May 12, 2016
Nearly 70% of all stored data contains no legal, regulatory, or business value. Intelligence about your information’s age, location, and ownership provides the roadmap to effective decision-making.
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Veritas
By: CloudHealth by VMware     Published Date: Feb 13, 2019
Most organizations use public cloud to reduce costs, but end up spending way more than they expected. Google Cloud Platform is growing at a staggering rate for the business benefits and its rich features around Machine Learning, Artificial Intelligence, Big Data and Containers. However, it’s important to keep a tab on your spend while maximizing your cloud benefits. Reducing spend in GCP doesn’t need to be a process of trial and error -- there are proven ways to save money in your GCP environment without negatively impacting desired outcomes. Read this eBook to learn the 8 different ways to reduce your spend, including: -Terminating Zombie Assets -Deleting Unattached Persistent Disk -Rightsizing Compute Engine VMs Download to learn and practice these techniques to reduce spend in GCP.
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cloud management, google cloud platform, multicloud management
    
CloudHealth by VMware
By: IBM     Published Date: Jan 27, 2017
Learn to evaluate avenues best with democratized yet trusted analytic insights using IBM Cognos Analytics for your business decisions here.
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ibm, analytics, business intelligence, data, converged analytics, governed analytics, data management
    
IBM
By: IBM     Published Date: Jan 27, 2017
The report argues top-down and bottom-up BI are flip sides of same coin that needs an harmony. This also describes the rise of data discovery tools as a bottom-up reaction to heavy handed BI and have crushed the top-down camp's monopoly of BI, that has unleashed a bevy of data silos.
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ibm, analytics, business intelligence, data, data discovery, data management
    
IBM
By: IBM     Published Date: Jan 27, 2017
Analytics relies on BI, Big Data, and data discovery to provide reporting, trend what-if analysis. Analytics is transforming data into insight.
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ibm, analytics, business intelligence, data, data management
    
IBM
By: IBM     Published Date: Aug 23, 2017
To compete in today’s fast-paced business climate, enterprises need accurate and frequent sales and customer reports to make real-time operational decisions about pricing, merchandising and inventory management. They also require greater agility to respond to business events as they happen, and more visibility into business activities so information and systems are optimized for peak efficiency and performance. By making use of data capture and business intelligence to integrate and apply data across the enterprise, organizations can capitalize on emerging opportunities and build a competitive advantage.
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ibm, data replication, inventory management, competitive advantage
    
IBM
By: IBM     Published Date: Aug 24, 2017
Data governance is all about managing data, by revising that data to standardize it and bring consistency to the way it is used across numerous business initiatives. What’s more, data governance ensures that critical data is available at the right time to the right person, in a standardized and reliable form. A benefit that fuels better organization of business operations, resulting in improved productivity and efficiency of that organization. Thus, the importance of proper data governance cannot be understated. The concepts of data governance have evolved, where the first iteration of data governance, often referred to as version 1.0, focused on three simplistic elements: objectives, structure and processes; having a limited focus and scope due to its tactical usage. The opportunity from the growing value of data in the realm of analytics, business intelligence, and generating insights was left unrealized. Today, organizations are moving towards what can be called Data Governance 2.0,
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ibm, unified governance strategy, data management, data governance
    
IBM
By: IBM     Published Date: Sep 11, 2017
This book is written for readers who have varying levels of familiarity with ODM. It doesn’t focus on any particular vendor’s offering; instead, it talks about the features of ODM as a model for managing operational decision-making. This book isn’t about offline business intelligence systems. While those systems are very valuable, the focus of this book is on automated decisions that can be executed in real time in conjunction with your business applications and processes.
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odm, management, operation-decision making, intelligence systems
    
IBM
By: IBM     Published Date: Oct 17, 2017
Every day, torrents of data inundate IT organizations and overwhelm the business managers who must sift through it all to glean insights that help them grow revenues and optimize profits. Yet, after investing hundreds of millions of dollars into new enterprise resource planning (ERP), customer relationship management (CRM), master data management systems (MDM), business intelligence (BI) data warehousing systems or big data environments, many companies are still plagued with disconnected, “dysfunctional” data—a massive, expensive sprawl of disparate silos and unconnected, redundant systems that fail to deliver the desired single view of the business. To meet the business imperative for enterprise integration and stay competitive, companies must manage the increasing variety, volume and velocity of new data pouring into their systems from an ever-expanding number of sources. They need to bring all their corporate data together, deliver it to end users as quickly as possible to maximize
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IBM
By: IBM     Published Date: Oct 19, 2017
The Smarter Process platform is IBM’s solution for reinventing business operations in a way that infuses every process with intelligence and expertise to deliver greater customer centricity, which in turn fuels top-line growth. It incorporates Business Process Management, Case Management, Operational Decision Management and Process Analytics, along with Process Discovery and Design with an objective of ensuring that customers find it easy to do business and that every interaction includes positive touch points. Within the context of this new imperative, accessing cloud efficiencies, leveraging mobile for greater engagement, mining big data for insights, and enhancing customer relationships via social media, are proving to be critical and interrelated strategies.
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customer engagement, mobile, social, bpm, case management
    
IBM
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: 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
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data analytics, data storytelling, business intelligence
    
IBM
By: IBM     Published Date: Jul 02, 2018
Cloud has evolved from a technological innovation to an integral part of business. Companies in every industry are investing in Digital Transformation initiatives to evolve and grow; often, cloudbased platforms are foundational elements of these transformations, as businesses increasingly seek the flexibility and agility to roll out new software services in days or weeks, versus months or years. As Digital Transformation initiatives unfold, one key challenge is to modernize the data center to facilitate rapid delivery of new applications and services—while still ensuring that existing missioncritical applications remain high performing, available, and secure. Another challenge relates to new requirements for accelerating the analysis of organizational data to near real time, much faster than previously possible with earlier incarnations of Business Intelligence (BI). Agile businesses are demanding faster access to the information contained within operational and business data stores to
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IBM
By: IBM     Published Date: Jul 02, 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. IDC believes that this emerging environment is to date still highly undefined, even as businesses must make critical decisions. Should businesses develop in-house or use VARs, systems integrators, or consultants? Should they deploy on-premise, in the cloud, or in some hybrid form? Can they use existing infrastructure, or do AI applications and deep learning require new servers with new capabilities? We believe that many of these questions can be
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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: Group M_IBM Q418     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 Q418
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