data analytics

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By: IBM     Published Date: Sep 02, 2014
Whether in high-performance computing, Big Data or analytics, information technology has become an essential tool in today’s hyper-competitive business landscape. Organizations are increasingly being challenged to do more with less and this is fundamentally impacting the way that IT infrastructure is deployed and managed. In this short e-book, learn the top ten ways that IBM Platform Computing customers are using technologies like IBM Platform LSF and IBM Platform Symphony to help obtain results faster, share resources more efficiently, and improve the overall cost-effectiveness of their global IT infrastructure.
Tags : ibm, ibm platform computing, save money
     IBM
By: IBM     Published Date: Sep 02, 2014
In today’s stringent financial services regulatory environment with exponential growth of data and dynamic business requirements, Risk Analytics has become integral to businesses. IBM Algorithmics provides very sophisticated analyses for a wide range of economic scenarios that better quantify risk for multiple departments within a firm, or across the enterprise. With Algorithmics, firms have a better handle on their financial exposure and credit risks before they finalize real-time transactions. But this requires the performance and agility of a scalable infrastructure; driving up IT risk and complexity. The IBM Application Ready Solution for Algorithmics provides an agile, reliable and high-performance infrastructure to deliver trusted risk insights for sustained growth and profitability. This integrated offering with a validated reference architecture delivers the right risk insights at the right time while lowering the total cost of ownership.
Tags : ibm, it risk, financial risk analytics
     IBM
By: IBM     Published Date: May 20, 2015
Whether in high-performance computing, Big Data or analytics, information technology has become an essential tool in today’s hyper-competitive business landscape. Organizations are increasingly being challenged to do more with less and this is fundamentally impacting the way that IT infrastructure is deployed and managed. In this short e-book, learn the top ten ways that IBM Platform Computing customers are using technologies like IBM Platform LSF and IBM Platform Symphony to help obtain results faster, share resources more efficiently, and improve the overall cost-effectiveness of their global IT infrastructure.
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     IBM
By: IBM     Published Date: May 20, 2015
IBM Platform Symphony - accelerate big data analytics – This demonstration will highlight the benefits and features of IBM Platform Symphony to accelerate big data analytics by maximizing distributed system performance, fully utilizing computing resources and effectively harnessing the power of Hadoop.
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     IBM
By: IBM     Published Date: May 20, 2015
Every day, the world creates 2.5 quintillion bytes of data and businesses are realizing tangible results from investments in big data analytics. IBM Spectrum Scale (GPFS) offers an enterprise class alternative to Hadoop Distributed File System (HDFS) for building big data platforms and provides a range of enterprise-class data management features. Spectrum Scale can be deployed independently or with IBM’s big data platform, consisting of IBM InfoSphere® BigInsights™ and IBM Platform™ Symphony. This document describes best practices for deploying Spectrum Scale in such environments to help ensure optimal performance and reliability.
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     IBM
By: IBM     Published Date: May 20, 2015
According to our global study of more than 800 cloud decision makers and users are becoming increasingly focused on the business value cloud provides. Cloud is integral to mobile, social and analytics initiatives – and the big data management challenge that often comes with them and it helps power the entire suite of game-changing technologies. Enterprises can aim higher when these deployments are riding on the cloud. Mobile, analytics and social implementations can be bigger, bolder and drive greater impact when backed by scalable infrastructure. In addition to scale, cloud can provide integration, gluing the individual technologies into more cohesive solutions. Learn how companies are gaining a competitive advanatge with cloud computing.
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     IBM
By: IBM     Published Date: May 20, 2015
There is a lot of hype around the potential of big data and organizations are hoping to achieve new innovations in products and services with big data and analytics driving more concrete insights about their customers and their own business operations. To meet these challenges, IBM has introduced IBM® Spectrum Scale™. The new IBM Spectrum Scale storage platform has grown from GPFS, which entered the market in 1998. Clearly, IBM has put significant development into developing this mature platform. Spectrum Scale addresses the key requirements of big data storage - extreme scalability for growth, reduced overhead of data movement, easy accessibility , geographic location independence and advanced storage functionality. Read the paper to learn more!
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     IBM
By: IBM     Published Date: Sep 16, 2015
Building applications for handling big data requires laser-like focus on solutions that allow you to deliver scalable, reliable and flexible infrastructure for fast-growing analytics environments. This paper provides 6 best practices for selecting the “right” infrastructure—one that is optimized for performance, flexibility and long-term value.
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     IBM
By: TIBCO     Published Date: Nov 09, 2015
As one of the most exciting and widely adopted open-source projects, Apache Spark in-memory clusters are driving new opportunities for application development as well as increased intake of IT infrastructure. Apache Spark is now the most active Apache project, with more than 600 contributions being made in the last 12 months by more than 200 organizations. A new survey conducted by Databricks—of 1,417 IT professionals working with Apache Spark finds that high-performance analytics applications that can work with big data are driving a large proportion of that demand. Apache Spark is now being used to aggregate multiple types of data in-memory versus only pulling data from Hadoop. For solution providers, the Apache Spark technology stack is a significant player because it’s one of the core technologies used to modernize data warehouses, a huge segment of the IT industry that accounts for multiple billions in revenue. Spark holds much promise for the future—with data lakes—a storage repo
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     TIBCO
By: Storiant     Published Date: Mar 16, 2015
Read this new IDC Report about how today's enterprise datacenters are dealing with new challenges that are far more demanding than ever before. Foremost is the exponential growth of data, most of it unstructured data. Big data and analytics implementations are also quickly becoming a strategic priority in many enterprises, demanding online access to more data, which is retained for longer periods of time. Legacy storage solutions with fixed design characteristics and a cost structure that doesn't scale are proving to be ill-suited for these new needs. This Technology Spotlight examines the issues that are driving organizations to replace older archive and backup-and-restore systems with business continuity and always-available solutions that can scale to handle extreme data growth while leveraging a cloudbased pricing model. The report also looks at the role of Storiant and its long-term storage services solution in the strategically important long-term storage market.
Tags : storiant, big data, analytics implementations, cloudbased pricing model, long-term storage services solution, long-term storage market
     Storiant
By: Impetus     Published Date: Feb 04, 2016
This white paper explores strategies to leverage the steady flow of new, advanced real-time streaming data analytics (RTSA) application development technologies. It defines a thoughtful approach to capitalize on the window of opportunity to benefit from the power of real-time decision making now, and still be able to move to new and emerging technologies as they become enterprise ready.
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     Impetus
By: Impetus     Published Date: Mar 15, 2016
Streaming analytics platforms provide businesses a method for extracting strategic value from data-in-motion in a manner similar to how traditional analytics tools operate on data-at rest. Instead of historical analysis, the goal with streaming analytics is to enable near real-time decision making by letting companies inspect, correlate and analyze data even as it flows into applications and databases from numerous different sources. Streaming analytics allows companies to do event processing against massive volumes of data streaming into the enterprise at high velocity.
Tags : impetus, guide to stream analytics, real time streaming analytics, streaming analytics, real time analytics, big data analytics
     Impetus
By: TIBCO     Published Date: Sep 02, 2014
In this Guide you will learn how predictive analytics helps your organization predict with confidence what will happen next so that you can make smarter decisions and improve business outcomes. It is important to adopt a predictive analytics solution that meets the specific needs of different users and skill sets from beginners, to experienced analysts, to data scientists.
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     TIBCO
By: EMC     Published Date: Jun 13, 2016
A Data Lake can meet the storage needs of your Modern Data Center. Check out the Top 10 Reasons your organization should adopt scale-out data lake storage for Hadoop Analytics on EMC Isilon.
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     EMC
By: IBM     Published Date: Nov 14, 2014
Every day, the world creates 2.5 quintillion bytes of data and businesses are realizing tangible results from investments in big data analytics. IBM Spectrum Scale (GPFS) offers an enterprise class alternative to Hadoop Distributed File System (HDFS) for building big data platforms and provides a range of enterprise-class data management features. Spectrum Scale can be deployed independently or with IBM’s big data platform, consisting of IBM InfoSphere® BigInsights™ and IBM Platform™ Symphony. This document describes best practices for deploying Spectrum Scale in such environments to help ensure optimal performance and reliability.
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     IBM
By: IBM     Published Date: Feb 13, 2015
In the age of "big data," it's essential for any organization to know how to analyze and manage their ever increasing stores of information. Packed with everything you need to know about Hadoop analytics, this handy guide provides you with a solid understanding of the critical big data concepts and trends, and suggests ways for you to revolutionize your business operations through the implementation of cost-effective, high performance Hadoop technology.
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     IBM
By: Dell and Intel®     Published Date: Aug 24, 2015
In today’s digitally driven world, the success of a business is increasingly tied to its ability to extract value from data. Exploiting the untapped value of your data is now the pathway to success. By putting data-driven decision making at the heart of the business, your organization can harness a wealth of information to gain an unparalleled competitive advantage. In a future-ready enterprise, you must make a fundamental shift from a focus on technology to a strategic business focus. Data-driven insights can guide everything from the formulation of top-level corporate strategies to connected devices that monitor and enable immediate critical decisions, to the creation of personalized customer interactions. Data is the foundation for enabling business transformation and innovation.
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     Dell and Intel®
By: Kx Systems     Published Date: Jan 16, 2015
?Kdb+ is a column-based relational database with extensive in-memory capabilities, developed and marketed by Kx Systems. Like all such products, it is especially powerful when it comes to supporting queries and analytics. However, unlike other products in this domain, kdb+ is particularly good (both in terms of performance and functionality) at processing, manipulating and analysing data (especially numeric data) in real-time, alongside the analysis of historical data. Moreover, it has extensive capabilities for supporting timeseries data. For these reasons Kx Systems has historically targeted the financial industry for trading analytics and black box trading based on real-time and historic data, as well as realtime risk assessment; applications which are particularly demanding in their performance requirements. The company has had significant success in this market with over a hundred major financial institutions and hedge funds deploying its technology. In this paper, however, we wa
Tags : kx systems, kdb+, relational database
     Kx Systems
By: snowflake     Published Date: Jun 09, 2016
Today’s data, and how that data is used, have changed dramatically in the past few years. Data now comes from everywhere—not just enterprise applications, but also websites, log files, social media, sensors, web services, and more. Organizations want to make that data available to all of their analysts as quickly as possible, not limit access to only a few highly-skilled data scientists. However, these efforts are quickly frustrated by the limitations of current data warehouse technologies. These systems simply were not built to handle the diversity of today’s data and analytics. They are based on decades-old architectures designed for a different world, a world where data was limited, users of data were few, and all processing was done in on-premises datacenters.
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     snowflake
By: Revolution Analytics     Published Date: May 09, 2014
As the primary facilitator of data science and big data, machine learning has garnered much interest by a broad range of industries as a way to increase value of enterprise data assets. Through techniques of supervised and unsupervised statistical learning, organizations can make important predictions and discover previously unknown knowledge to provide actionable business intelligence. In this guide, we’ll examine the principles underlying machine learning based on the R statistical environment. We’ll explore machine learning with R from the open source R perspective as well as the more robust commercial perspective using Revolution Analytics Enterprise (RRE) for big data deployments.
Tags : revolution analytics, data science, big data, machine learning
     Revolution Analytics
By: Adaptive Computing     Published Date: Feb 27, 2014
Big data applications represent a fast-growing category of high-value applications that are increasingly employed by business and technical computing users. However, they have exposed an inconvenient dichotomy in the way resources are utilized in data centers. Conventional enterprise and web-based applications can be executed efficiently in virtualized server environments, where resource management and scheduling is generally confined to a single server. By contrast, data-intensive analytics and technical simulations demand large aggregated resources, necessitating intelligent scheduling and resource management that spans a computer cluster, cloud, or entire data center. Although these tools exist in isolation, they are not available in a general-purpose framework that allows them to inter operate easily and automatically within existing IT infrastructure.
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     Adaptive Computing
By: Aberdeen     Published Date: Jun 17, 2011
Download this paper to learn the top strategies leading executives are using to take full advantage of the insight they receive from their business intelligence (BI) systems - and turn that insight into a competitive weapon.
Tags : aberdeen, michael lock, data-driven decisions, business intelligence, public sector, analytics, federal, state
     Aberdeen
By: Google     Published Date: Jan 08, 2018
Today’s smart computers can beat board game champions, master video games, and learn to recognize cats. No wonder artificial intelligence has captured the imaginations of business and IT leaders. And indeed, AI is starting to transform processes in established industries, from retail to financial services to manufacturing. Read this guide from Google Cloud and learn how you can unlock the transformational power of information and get useful insights from a vast and complex landscape of data.
Tags : data analytics, machine learning, big data, artificial intelligence
     Google
By: IBM     Published Date: Nov 14, 2017
Data is the hottest topic in business today. In discussions that range from understanding performance to predicting future outcomes, data is at the core. However, data has a bad reputation. Because businesses have been collecting data for decades, the amount that we must analyze can seem insurmountable. Simply saying “data” is enough to conjure images of someone poring over a thick stack of spreadsheets, manually going through row after row to identify performance, trends and figure out what to do with them. This intimidating view is all too common.
Tags : data, ibm, data insight, data analytics
     IBM
By: FICO     Published Date: Dec 04, 2017
Whether you’re onboarding new customers, cross- or up-selling, getting your supply chain or logistics right, or even collecting unpaid debt, making the best choice of decisions means weighing not just what’s right for your department – but what is best for the business overall. Not to mention what is optimal for your customers and partners. And let’s face it, even with the availability of business intelligence and other analytic tools, it’s hard to know what constitutes the right actions to take in an era where Big Data consistently throws you curveballs. Prescriptive Analytics can help – but for most organizations, there are more questions and concerns than answers about how to implement it successfully. Read our white paper on how Prescriptive Analytics can transform your business decisions and actions – leveraging your existing analytics investment and organizational DNA while helping you drive transparency, customer experience, and profits
Tags : business, results, optimal, customer, experience, tools, analytics, big data
     FICO
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