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Published By: SAP     Published Date: May 11, 2012
Both business and IT leaders will learn: . Why midmarket organizations may have some requirements for BI and analytics programs . How to navigate the many options available, and the pros and cons of various deployment styles . Easy methods and techniques to get started with BI at midmarket organizations . Practical steps and best practices for midmarket BI programs
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midmarket, midmarket business intelligence, smb, small company, small business, medium sized company, medium sized business, intelligence trends
    
SAP
Published By: Adobe     Published Date: Apr 17, 2014
Discover new opportunities for maturing your data practices—and building your business results. You’ll learn how to move beyond mere web analytics to build a more comprehensive marketing analytics approach that includes mobile, social, and offline channels. And you’ll see how your current analytics capabilities compare to those of similar organizations and where you have opportunities for improvement.
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adobe, analytics, marketing, data collection, data automation, marketing automation, web analytics, marketing analytics
    
Adobe
Published By: CDW     Published Date: Apr 04, 2016
As organizations evolve from virtualized infrastructures to private clouds, IT management must measure success by the efficiencies of the infrastructure in providing end users with flexible and cost-effective services and service levels. NetApp® OnCommand® Insight storage resource management software provides service analytics that help enterprises manage the complexity of this transition. OnCommand Insight provides a holistic view into complex multivendor and multiprotocol storage services and powerful analytics to help organizations fully leverage the promise of cloud computing.
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cloud management, cloud services, cloud management, it infrastructure, cloud application, analytics
    
CDW
Published By: Teradata     Published Date: Jan 22, 2014
Today's agile businesses are seeking to expand analytics fast, gain flexible analytic deployment options and smooth cash flows. Download this paper to learn how Teradata understands these needs and has engineered a cloud solution that meets organizations' analytic needs.
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cloud analytics, hadoop, analytics, cloud-based analytics, data warehousing, discovery capabilities, data management
    
Teradata
Published By: Teradata     Published Date: Oct 19, 2015
Bruce Rogers, Forbes’ Chief Insights Officer, will discuss the key findings from a survey of senior-level IT and data executives on big data and analytics. He will be joined by Matt Ariker from McKinsey and Chris Twogood from Teradata and together they will present the differences between "leaders" and "laggards" when it comes to analytics initiatives and share how organizations can increase their odds of success with big data and analytics.
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big data, analytics, leaders, laggards, anlytics initiative
    
Teradata
Published By: CDW - NetApp     Published Date: Apr 07, 2016
As organizations evolve from virtualized infrastructures to private clouds, IT management must measure success by the efficiencies of the infrastructure in providing end users with flexible and cost-effective services and service levels. NetApp® OnCommand® Insight storage resource management software provides service analytics that help enterprises manage the complexity of this transition. OnCommand Insight provides a holistic view into complex multivendor and multiprotocol storage services and powerful analytics to help organizations fully leverage the promise of cloud computing.
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cloud management, cloud services, cloud management, it infrastructure, cloud application, analytics
    
CDW - NetApp
Published By: AWS     Published Date: Nov 28, 2018
Financial institutions run on data: collecting it, analyzing it, delivering meaningful insights, and taking action in real time. As data volumes increase, organizations demand a scalable analytics platform that can meet the needs of data scientists and business users alike. However, managing an on-premises analytics environment for a large and diverse user base can become time-consuming, costly, and unwieldy. Tableau Server on Amazon Web Services (AWS) is helping major Financial Services organizations shift data visualization and analytics workloads to the cloud. The result is fewer hours spent on manual work and more time to ask deeper questions and launch new data analyses, with easily-scalable support for large numbers of users. In this webinar, you’ll hear how one major asset management company made the shift to cloud data visualization with Tableau Server on AWS. Discover lessons learned, best practices tailored to Financial Services organizations, and starting tactics for scalable analytics on the cloud.
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AWS
Published By: SAS     Published Date: Apr 25, 2017
Artificial intelligence and related forms of advanced analytics hold enormous potential for marketers to expand and deepen customer intelligence, improve business processes, and deliver engaging customer experiences. But many marketing organizations are just getting their feet wet in leveraging these technologies. To learn about the opportunities and challenges, IIA spoke with Analise Polsky, Business Solutions Manager, SAS Best Practices and Jonathan Moran, Principal Product Marketing Manager, SAS Customer Intelligence Solutions.
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SAS
Published By: Gigaom     Published Date: Sep 17, 2019
Today’s data volumes, combined with the credo of data-driven everything, make for an analytics landscape radically different from that of years past. Delays involved in moving and shaping operational data into separate data warehouses, data lakes and standalone BI platforms can thwart effective operational analytics. In fact, for some organizations, such segregation of infrastructure and process may not work at all. Just because OLTP (Online Transactional Processing) and OLAP (Online Analytical Processing) are distinct workloads doesn’t mean they should take place on separate platforms. Sure, dedicated server nodes may make sense to optimize the performance of operational and analytical tasks, but they need to operate on the same data, in a coordinated fashion. Modern databases – serving the needs of both business analysts and application developers – are great platforms for implementing such business-forward architectures. To learn more, join us for this free 1-hour webinar from Gig
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Gigaom
Published By: IBM     Published Date: Mar 28, 2016
Big data. We've heard the phrase for quite some time, but how can human resource leaders get into the action? One way is through the development and implementation of talent analytics strategies. Talent analytics is fundamentally changing the way organizations and practitioners are thinking about the role of HR and organizations uncovering never before seen insights.
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ibm, smarter workforce, talent management, talent analytics
    
IBM
Published By: IBM     Published Date: Jun 13, 2016
Big data. We've heard the phrase for quite some time, but how can human resource leaders get into the action? One way is through the development and implementation of talent analytics strategies. Talent analytics is fundamentally changing the way organizations and practitioners are thinking about the role of HR and organizations uncovering never before seen insights.
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ibm, talent acquisition, talent acquisition technology, human resources, recruiting
    
IBM
Published By: SAP     Published Date: Apr 07, 2011
There is growing evidence of the competitive value of BI and analytics solutions. An IDC study of North American and European organizations found that the median ROI of BI and analytics projects was 112%.
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business intelligence, competitive advantage, analytics solutions, idc, roi
    
SAP
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: IBM     Published Date: Apr 15, 2016
This report examines the current state of self-service analytics across all industries and company sizes. It also highlights the technology decisions and analytical performance of organizations that reported high levels of self-service in their analytical use base.
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ibm, analytics, self-service analytics, business analytics, analytical performance
    
IBM
Published By: IBM     Published Date: Jul 01, 2015
This white paper discusses how enterprise analytics systems can assist provider organizations in building sustainable healthcare systems and achieving their vision for accountable care
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healthcare analytics, data blueprint, enterprise analytics systems, sustainable healthcare systems, mission-critical analysis, integrated data model, operational analytics, information technology
    
IBM
Published By: IBM     Published Date: Mar 30, 2016
This white paper discusses how enterprise analytics systems can assist provider organizations in building sustainable healthcare systems and achieving their vision for accountable care—from near-term demands for regulatory and quality reporting to transforming care delivery.
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ibm, healthcare analytics, healthcare, big data, enterprise analytics systems, information technology
    
IBM
Published By: SAS     Published Date: Mar 06, 2018
Known for its industry-leading analytics, data management and business intelligence solutions, SAS is focused on helping organizations use data and analytics to make better decisions, faster. The combination of self-service BI and analytics positions you for improved productivity and smarter business decisions. So you can become more competitive as you use all your data to take better actions. Instead of depending on hunch-based choices, you can make decisions that are truly rooted in discovery and analytics. And you can do it through an interface that anyone can use. At last, your business users can get close enough to the data to manipulate it and draw their own reliable, fact-based conclusions. And they can do it in seconds or minutes, not hours or days. Equally important, IT remains in control of data access and security by providing trusted data sets and defined processes that promote the valuable, user-generated content for reuse and consistency. But, they are no longer forced
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SAS
Published By: SAS     Published Date: Mar 06, 2018
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da
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SAS
Published By: SAS     Published Date: Mar 06, 2018
The Internet of Things enables retailers to do three basics better and faster: 1) Sensing who customers are and what they’re doing, 2) Understanding customer behavior and preferences, and 3)Acting on that insight to create a more engaging customer experience. - There are high-potential IoT applications in supply chain, in “smart store” operations, and especially in providing an engaging experience to the “connected customer.” IoT data can anticipate where the customer is headed and how to meet her there. - Much of the IoT ground, in both data management and analytics, may be unfamiliar. Retailers and their IT organizations have to be realistic about the technological challenges, their own capabilities, and where they need assistance. - To differentiate through IoT, focus on the analytics. Devices and their data — and even their platforms — are commodities. Advantage goes to the retailer who does the most with the data to engage the connected customer.
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SAS
Published By: SAS     Published Date: Apr 04, 2018
Location analytics is the process of integrating geographical data into business intelligence (BI) and analytics-led decision making. Location analytics creates meaningful insight from relationships found in geospatial data to solve a broad variety of business and social problems. Location data is found everywhere – with an item or a device, in a conversation or behavior, in machines or sensors, tied to a customer or competitor, attached to a database record or recorded from vehicles or other moving objects. Organizations want to take advantage of location data to improve decisions, create better customer engagement and experiences, reduce risks and automate business processes.
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SAS
Published By: SAS     Published Date: Jun 06, 2018
Competitive advantage from analytics is changing, and for the better. For the first time in four years, MIT Sloan Management Review found an increasing ability to strategically innovate with analytics based on interviews with more than 2,600 practitioners and scholars globally. Learn more about key findings, including: Wider use of analytics, better knowledge of its benefits and greater focus on applications have reversed a trend on the benefits of analytics. Return on investment for analytics stems from the governing and sharing of data throughout the organization. Machine learning enables organizations to discover more insight from their data, allowing employees to focus on other critical responsibilities.
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SAS
Published By: SAS     Published Date: Jun 06, 2018
Today’s consumers expect immediate, personalized interactions. To meet these expectations, companies must differentiate their brands through timely, targeted and tailored customer experiences based on real-time data analytics. This report, sponsored by SAS, Intel and Accenture and conducted by Harvard Business Review Analytic Services, looks at how businesses are using advanced customer data analytics, along with real-time analytics and real-time marketing, to enhance their customers’ experiences. Learn why organizations that place a high value on real-time capabilities still struggle to achieve them, what companies can do to ensure success as they adopt and implement real-time analytics solutions, and what benefits successful companies are already seeing.
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SAS
Published By: SAS     Published Date: Mar 01, 2012
This white paper reveals the results of a Bloomberg Businessweek Research Services survey of 930 respondents globally on the current state of business analytics within organizations. You'll discover how and why the use of analytics is growing!
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sas, analytics, business analytics, business intelligence, customer intelligence, data management, fraud & financial crimes, high-performance analytics
    
SAS
Published By: IBM     Published Date: Nov 08, 2013
Because all processes should be aligned to customer metrics, process improvement is an important goal for organizations in every industry. This paper illustrates the impact analytics can make on business processes through real-world examples based on IBM client experiences, and describes the steps organizations can take to refine quality, warranty, financial, inventory and other processes that are essential to achieving operational excellence.
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ibm, embedding business analytics, organizational processes, business analytics, building smarter processes, insight and alignment, consistent information, accurate information
    
IBM
Published By: IBM     Published Date: Feb 11, 2015
As cloud computing gains more traction, more businesses are beginning to align their security strategy to better manage the privacy and compliance challenges of this new deployment model. Indeed, cloud models are being used not only to add compute and storage resources, they are also becoming an imperative for data analytics and mobility. In this paper, we will look at how the growing adoption of cloud computing is changing the way organizations are implementing security.
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security strategies, ibm, deployment model, cloud security, data analytics
    
IBM
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