big data analytics

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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, brand reputation, protecting reputation, prevent theft, organizational defenses, global analytics, broad data, real time data, security risks, prevent risks, security
    
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, business technology
    
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, data management
    
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, big data, security, data management
    
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, enterprise applications, data 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, enterprise applications, data 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, human resource technology
    
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, data management, data center, data science, data storage, data visualization
    
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
Published By: Dassault Systèmes     Published Date: Jun 19, 2018
This white paper outlines a framework that emphasizes digitization and business transformation and the new opportunities pull processes bring. The mechanism of “Pull” processes—those triggered by an actual event instead of a forecast—is nothing new. It is at the heart of many successful manufacturing strategies. Recent technological advances in digitization, including the harnessing of Big Data analytics, the use of the cloud, Business Process Management (BPM), social media, IIoT, and mobility, have extended the power of Pull beyond Lean manufacturing. In the wake of the current technological innovation wave, it is not uncommon for manufacturers to not know what next step to take. In light of these new developments, this white paper will focus on the mechanism of business transformation enabled by these technologies, which can be attributed to two major forces: the power of Pull and digitization. Nine practical applications are detailed, showing how innovative manufacturers can better
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Dassault Systèmes
Published By: IBM     Published Date: Jul 05, 2018
Scalable data platforms such as Apache Hadoop offer unparalleled cost benefits and analytical opportunities. IBM helps fully leverage the scale and promise of Hadoop, enabling better results for critical projects and key analytics initiatives. The end-to- end information capabilities of IBM® Information Server let you better understand data and cleanse, monitor, transform and deliver it. IBM also helps bridge the gap between business and IT with improved collaboration. By using Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of- impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time. Since its inception, Information Server has been a massively parallel processing (MPP) platform able to support everything from small to very large data volumes to meet your requirements, regardless of complexity. Information Server can uniquely support th
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IBM
Published By: IBM     Published Date: Jul 09, 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
Published By: TIBCO Software APAC     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes.
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TIBCO Software APAC
Published By: TIBCO Software APAC     Published Date: May 31, 2018
Predictive analytics, sometimes called advanced analytics, is a term used to describe a range of analytical and statistical techniques to predict future actions or behaviors. In business, predictive analytics are used to make proactive decisions and determine actions, by using statistical models to discover patterns in historical and transactional data to uncover likely risks and opportunities. Predictive analytics incorporates a range of activities which we will explore in this paper, including data access, exploratory data analysis and visualization, developing assumptions and data models, applying predictive models, then estimating and/or predicting future outcomes. Download now to read on.
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TIBCO Software APAC
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