big data analytics

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Published By: NEC     Published Date: Aug 12, 2014
NEC has introduced the Express5800/A2000 Series Servers to accommodate enterprise application demands with peak server reliability, availability, and serviceability. The new server architecture and NEC engineering functionality align well with many of the most important IT priorities in 2014, including virtualization, big data and advanced data analytics. With the Express5800/A2000 Servers, NEC has the opportunity to educate application owners and work with system integrators to drive awareness and ultimately investment in its new server platform, which will help drive customer value. In this white paper, you will learn how these next-generation servers from NEC are ready to accommodate some of today’s demanding workloads.
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servers, virtualization, customer value, analytics, application owners, system integrators, big data, reliability
    
NEC
Published By: SAS     Published Date: Apr 16, 2015
We often hear about how the massive volumes of data the US government collects hold a treasure trove of answers to our most challenging questions – be it on population health, national security, education or how to recoup losses from tax fraud. If only the government could figure out how to make use of all that information. Texas is one example of a government that is using analytics to solve complex problems. As the case studies here demonstrate, agencies and academia in the Lone Star State are putting big data and analytics to work to eliminate waste, improve productivity and, in some cases, even enhance transparency.
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SAS
Published By: SAS     Published Date: Apr 20, 2015
After interviewing more than 50 companies, Tom Davenport reveals the true value of big data at work.
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SAS
Published By: Pentaho     Published Date: Aug 22, 2016
This white paper covers the many options available for modernizing a data warehouse.
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big data, data integration, bi systems, hadoop
    
Pentaho
Published By: Impetus Infotech     Published Date: Apr 05, 2011
This White Paper discusses the challenges companies may face while moving to big data solutions such as Hadoop and/or NoSQL.
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hadoop, nosql implementation strategy, hadoop implementation, apache hadoop, data analytics, nosql, nosql databases, what is nosql
    
Impetus Infotech
Published By: Verint     Published Date: Apr 18, 2013
The vision for Celent’s Model Bank research was to try to answer an apparently simple question: “What would it look like for a bank to do everything right with today’s technology?” Of course, the question is not nearly as simple as it appears. The terms “everything” and “right” will mean very different things to different banks depending on their size, the complexity of their operations and product sets, and their technological starting points. While there is no such thing as a “Model Bank”—every bank does some things well, and others not as well when it comes to technology—it is possible to conceive of a “Model Bank” through real world examples of effective usage of technology.
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banking, big data, technology, financial, effective use, analytics
    
Verint
Published By: Verint     Published Date: Apr 24, 2013
Commonwealth Bank of Australia, with over 1,000 branches and 50,000 employees was using spreadsheets to staff its branch network. When they looked to desktop process analytics from Verint they achieved major improvements in productivity and customer experience. Watch this case study to learn more about how desktop process analytics can use big data to help your organization.
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case study, big data, organization, desktop process analytics, process management, analytics, workforce management
    
Verint
Published By: IBM     Published Date: Oct 10, 2013
The growth of cloud, mobility, social business and big data mean that botnets, credit card theft, cyber criminals and other external threats pose increasing risk to business continuity, financial stability and brand reputation. Read the white paper, “Preventing security risks in real time,” to learn how this new reality is forcing the evolution of organizations’ defenses to become more intelligent—using global analytics to scale visibility across broad data sets, both diverse and complementary, in real time.
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cloud, mobility, social business, big data, external threats, business risks, business continuity, financial stability
    
IBM
Published By: Oracle     Published Date: Nov 14, 2016
Démo Data Visualization dans le domaine de la vente par Sylvain Fortier, Business Intelligence & Big Data Analytics Principal Sales Consultant d'Oracle France durant la matinée Oracle Cloud Café autour du thème de la Data Visualization et Analytics.
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Oracle
Published By: Oracle     Published Date: Nov 14, 2016
Démo Data Visualization dans le domaine de la vente par Sylvain Fortier, Business Intelligence & Big Data Analytics Principal Sales Consultant d'Oracle France durant la matinée Oracle Cloud Café autour du thème de la Data Visualization et Analytics.
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Oracle
Published By: IBM     Published Date: Jan 09, 2014
While some organizations are already utilizing Big Data or various large enterprise analytics techniques, many more are still working to grasp how these new usage models might help them. There’s a lot of undiscovered value in the vast amounts of data they currently have and the data that they can get from other sources. They know that they can somehow convert this data into insights that will let them ramp up efficiency and be ready for tomorrow today.
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big data, enterprise analytics, red hat, data analytics, powerlinux system, powervm virtualization
    
IBM
Published By: IBM     Published Date: Aug 06, 2014
Big data and analytics help insurance companies identify the next best action for customers. With the right solutions, companies can extract, integrate and analyze a large volume and variety of data, from call-center notes and voice recordings to web chats, telematics and social media
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ibm, insurance, data, big data, analytics, solutions
    
IBM
Published By: IBM     Published Date: Aug 06, 2014
As executives witness data’s proven impact on performance and innovation and recognize its strategic significance, they also realize the growing need for a leader whose primary role is to understand and advocate on behalf of data: The Chief Data Officer.
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ibm, banking, data, big data, analytics, chief data officer
    
IBM
Published By: IBM     Published Date: Aug 06, 2014
With IBM analytics for big data with a smart mobile strategy, banks can increase wallet share and assets under management while lowering the organization’s operating ratio by using more efficient channels.
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ibm, banking, digital, data, big data, analytics, mobile
    
IBM
Published By: Cisco     Published Date: Apr 08, 2015
This document will identify the essential capabilities you should seek in an advanced malware protection solution, the key questions you should ask your advanced malware protection vendor, and shows you how Cisco combats today’s advanced malware attacks using a combination of four techniques: ? Big data analytics ? Collective global security intelligence ? Enforcement across multiple form factors (networks, endpoints, mobile devices, secure gateways, and virtual systems) ? Continuous analysis and retrospective security
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protection, analytics, global security, intelligence, virtual, gateway, attacks, malware
    
Cisco
Published By: HP     Published Date: Apr 13, 2014
With growth in the volume and variety of data needed to monitor and manage complex IT systems, IT leaders have had to acquire new technologies to manage this data. This will require that a single, architected platform be procured to improve root-cause analysis and short-term predictive analysis.
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ops analytics, operations analytics, gartner, big data, analytics, predictive analytics, bsm, business service management
    
HP
Published By: HP     Published Date: Apr 23, 2014
With growth in the volume and variety of data needed to monitor and manage complex IT systems, IT leaders have had to acquire new technologies to manage this data. This will require that a single, architected platform be procured to improve root-cause analysis and short-term predictive analysis.
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ops analytics, operations analytics, gartner, big data, analytics, predictive analytics, bsm, business service management
    
HP
Published By: NetApp     Published Date: Mar 05, 2018
The proliferation of business unit cloud use is primarily motivated by digital transformation's need for innovation and agility in big data and real-time analytics, rather than cost optimization, which causes overspending. CIOs should use three moves to gain business influence and optimize costs.
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netapp, database performance, flash storage, data management, cost challenges
    
NetApp
Published By: IBM     Published Date: May 19, 2015
Predictive analytics is within easy reach for all enterprises if they choose the right big data predictive analytics solution to meet their needs.
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predictive analytics, predictive analytics solutions, big data, enterprise. advanced analytics, application development
    
IBM
Published By: FICO     Published Date: Jun 07, 2016
As any line of business (LOB) leader knows, making customer level decisions that balance risk and profit just keeps getting harder. And even when you think you have the right decisions, turning them into actions can be even trickier. You also need to consider the factors that make smart decisions difficult. Big data. Regulations. Customers who want an offer, fast, or else you’re going to lose them.
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FICO
Published By: IBM     Published Date: Jul 20, 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: IBM     Published Date: Oct 13, 2016
In the era of big data, you must accommodate a rapidly increasing volume, variety and velocity of data while extracting actionable business insight from that data, faster than ever before. Download this white paper to learn how.
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database, big data, analytics, infrastructure
    
IBM
Published By: AWS     Published Date: Sep 04, 2018
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics.
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AWS
Published By: AWS     Published Date: Sep 04, 2018
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications. Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios. Since starting to work with this technology
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AWS
Published By: IBM     Published Date: Oct 16, 2017
This white paper examines how some of the ways organizations use big data make their infrastructures vulnerable to attack. It presents recommended best practices organizations can adopt to help make their infrastructures and operations more secure. And it discusses how adding advanced security software solutions from IBM to their big-data environment can fill gaps that big-data platforms by themselves do not address. It describes how IBM® Security Guardium®, an end-to- end solution for regulatory compliance and comprehensive data security, supports entitlement reporting; user-access and activity monitoring; advanced risk analytics and real-time threat detection analytics; alerting, blocking, encryption and other data protection capabilities, as well as automated compliance workflows and reporting capabilities, to stop threats.
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security, big data, ibm, data protection
    
IBM
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