Magic Quadrant for Business Intelligence and Analytics Platforms
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A Platform for Networked Business Analytics BUSINESS INTELLIGENCE
BROCHURE A platform for networked business analytics BUSINESS INTELLIGENCE Infor® Birst's unique networked business analytics technology enables centralized and decentralized teams to work collaboratively by unifying Leveraging Birst with existing IT-managed enterprise data with user-owned data. Birst automates the enterprise BI platforms process of preparing data and adds an adaptive user experience for business users that works across any device. Birst networked business analytics technology also enables customers to This white paper will explain: leverage and extend their investment in ■ Birst’s primary design principles existing legacy business intelligence (BI) solutions. With the ability to directly connect ■ How Infor Birst® provides a complete unified data and analytics platform to Oracle Business Intelligence Enterprise ■ The key elements of Birst’s cloud architecture Edition (OBIEE) semantic layer, via ODBC, ■ An overview of Birst security and reliability. Birst can map the existing logical schema directly into Birst’s logical model, enabling Birst to join this Enterprise Data Tier with other data in the analytics fabric. Birst can also map to existing Business Objects Universes via web services and Microsoft Analysis Services Cubes and Hyperion Essbase cubes via MDX and extend those schemas, enabling true self-service for all users in the enterprise. 61% of Birst’s surveyed reference customers use Birst as their only analytics and BI standard.1 infor.com Contents Agile, governed analytics Birst high-performance in the era of -
Revolution R Enterprise 6.1 README
Revolution R Enterprise 6.1 README Revolution R Enterprise 6.1 for 32-bit and 64-bit Windows and 64-bit Red Hat Enterprise Linux (RHEL 5.x and RHEL 6.x) features an updated release of the RevoScaleR package that provides fast, scalable data management and data analysis: the same code scales from data frames to local, high-performance .xdf files to data distributed across a Windows HPC Server cluster, Windows HPC Server Azure Burst cluster, or IBM Platform Computing LSF cluster. RevoScaleR also allows distribution of the execution of essentially any R function across cores and nodes, delivering the results back to the user. Installation instructions and instructions for getting started are provided in your confirmation e-mail. What’s New in Revolution R Enterprise 6.1 Big Data Decision Tree Models New RevoScaleR function rxDTree can be used to create decision tree models. It is based on a binning algorithm so that it can scale to huge data. Both classification and regression trees are supported. The model objects returned can be made to inherit from the rpart class of the rpart package, so that plot.rpart, text.rpart, and printcp can be used for subsequent analysis. Prediction for models fitted by rxDTree can be done using rxPredict. See Chapter 10 of the RevoScaleR User’s Guide for examples on how to create decision tree models with rxDTree. Additional information is available in the rxDTree help file, seen by entering ?rxDTree at the R command line. Support for Compression in .xdf Files RevoScaleR’s .xdf files can now be created using zlib compression. -
H-1B Petition Approvals for Initial Benefits by Employers FY07
NUMBER OF H-1B PETITIONS APPROVED BY USCIS FOR INITIAL BENEFICIARIES FY 2007 Approved Employer Petitions INFOSYS TECHNOLOGIES LIMITED 4,559 WIPRO LIMITED 2,567 SATYAM COMPUTER SERVICES LTD 1,396 COGNIZANT TECH SOLUTIONS US CORP 962 MICROSOFT CORP 959 TATA CONSULTANCY SERVICES LIMITED 797 PATNI COMPUTER SYSTEMS INC 477 US TECHNOLOGY RESOURCES LLC 416 I-FLEX SOLUTIONS INC 374 INTEL CORPORATION 369 ACCENTURE LLP 331 CISCO SYSTEMS INC 324 ERNST & YOUNG LLP 302 LARSEN & TOUBRO INFOTECH LIMITED 292 DELOITTE & TOUCHE LLP 283 GOOGLE INC 248 MPHASIS CORPORATION 248 UNIVERSITY OF ILLINOIS AT CHICAGO 246 AMERICAN UNIT INC 245 JSMN INTERNATIONAL INC 245 OBJECTWIN TECHNOLOGY INC 243 DELOITTE CONSULTING LLP 242 PRINCE GEORGES COUNTY PUBLIC SCHS 238 JPMORGAN CHASE & CO 236 MOTOROLA INC 234 MARLABS INC 229 KPMG LLP 227 GOLDMAN SACHS & CO 224 TECH MAHINDRA AMERICAS INC 217 VERINON TECHNOLOGY SOLUTIONS LTD 213 THE JOHNS HOPKINS MED INSTS OIS 205 YASH TECHNOLOGIES INC 202 ADVANSOFT INTERNATIONAL INC 201 UNIVERSITY OF MARYLAND 199 BALTIMORE CITY PUBLIC SCHOOLS 196 PRICEWATERHOUSECOOPERS LLP 192 POLARIS SOFTWARE LAB INDIA LTD 191 UNIVERSITY OF MICHIGAN 191 EVEREST BUSINESS SOLUTIONS INC 190 IBM CORPORATION 184 APEX TECHNOLOGY GROUP INC 174 NEW YORK CITY PUBLIC SCHOOLS 171 SOFTWARE RESEARCH GROUP INC 167 EVEREST CONSULTING GROUP INC 165 UNIVERSITY OF PENNSYLVANIA 163 GSS AMERICA INC 160 QUALCOMM INCORPORATED 158 UNIVERSITY OF MINNESOTA 151 MASCON GLOBAL CONSULTING INC 150 MICRON TECHNOLOGY INC 149 THE OHIO STATE UNIVERSITY 147 STANFORD UNIVERSITY 146 COLUMBIA -
Frequently Asked Questions About Rcpp
Frequently Asked Questions about Rcpp Dirk Eddelbuettel Romain François Rcpp version 0.12.7 as of September 4, 2016 Abstract This document attempts to answer the most Frequently Asked Questions (FAQ) regarding the Rcpp (Eddelbuettel, François, Allaire, Ushey, Kou, Chambers, and Bates, 2016a; Eddelbuettel and François, 2011; Eddelbuettel, 2013) package. Contents 1 Getting started 2 1.1 How do I get started ?.....................................................2 1.2 What do I need ?........................................................2 1.3 What compiler can I use ?...................................................3 1.4 What other packages are useful ?..............................................3 1.5 What licenses can I choose for my code?..........................................3 2 Compiling and Linking 4 2.1 How do I use Rcpp in my package ?............................................4 2.2 How do I quickly prototype my code?............................................4 2.2.1 Using inline.......................................................4 2.2.2 Using Rcpp Attributes.................................................4 2.3 How do I convert my prototyped code to a package ?..................................5 2.4 How do I quickly prototype my code in a package?...................................5 2.5 But I want to compile my code with R CMD SHLIB !...................................5 2.6 But R CMD SHLIB still does not work !...........................................6 2.7 What about LinkingTo ?...................................................6 -
I Am a Data Engineer, Specializing in Data Analytics and Business Intelligence, with More Than 8+ Years of Experience and a Wide Range of Skills in the Industry
Dan Storms [email protected] linkedin.com/in/danstorms1 | 1-503-521-6477 About me I am a data engineer, specializing in data analytics and business intelligence, with more than 8+ years of experience and a wide range of skills in the industry. I have a passion for leveraging technology to help businesses meet their needs and better serve their customers. I bring a deep understanding of business analysis, data architecture, and data modeling with a focus in developing reporting solutions to drive business insights and actions. I believe continuous learning is critical to remaining relevant in technology and have completed degrees in both Finance and Business Information Systems. I have also completed certifications in AWS and Snowflake. Born and raised in Oregon and currently living in Taiwan, I enjoy any opportunity to explore the outdoors and am passionate about travel and exploring the world. Past Employers NIKE Personal Abilities Hard work and honesty forms my beliefs Critical Thinker Problem Solver Logical Visioner Deep experience in business Developed dashboards using Developed data pipelines using analysis, data architecture, and Tableau and PowerBI Apache Airflow, Apache Spark, data modeling and Snowflake Work Ethic Loyalty and Persistence Persuasive Leader Migrated Nike Digital Global Ops Experience streaming and Led analysis and development of BI to Snowflake transforming application data in KPIs in both healthcare and Nike Kafka and Snowflake digital Education & Credentials I believe that personal education never ends Bachelor -
BARC Score Enterprise BI and Analytics Platforms
BARC Score Enterprise BI and Analytics Platforms Authors: Larissa Seidler, Christian Fuchs, Patrick Keller, Carsten Bange, Robert Tischler Publication: September 8th, 2017 Abstract This BARC document is the third edition of our BARC Score business intelligence vendor evaluation and ranking. This BARC Score evaluates enterprise BI and analytics platforms that are able to fulfill a broad set of BI requirements within the enterprise. Based on countless data points from The BI Survey and many analyst interactions, vendors are rated on a variety of criteria, from product capabilities and architecture to sales and marketing strategy, financial performance and customer feedback. This document is not to be shared, distributed or reproduced in any way without prior permission of BARC Table of Contents Overview ...................................................................................................................................................3 Inclusion Criteria .......................................................................................................................................3 Evaluation Criteria ....................................................................................................................................4 Portfolio Capabilities...................................................................................................................... 4 Market Execution ........................................................................................................................... 7 Score -
Magic Quadrant for Business Intelligence and Analytics Platforms Published: 16 February 2017 ID: G00301340 Analyst(S): Rita L
2/21/2017 Gartner Reprint (https://www.gartner.com/home) LICENSED FOR DISTRIBUTION Magic Quadrant for Business Intelligence and Analytics Platforms Published: 16 February 2017 ID: G00301340 Analyst(s): Rita L. Sallam, Cindi Howson, Carlie J. Idoine, Thomas W. Oestreich, James Laurence Richardson, Joao Tapadinhas Summary The business intelligence and analytics platform market's shift from IT-led reporting to modern business-led analytics is now mainstream. Data and analytics leaders face countless choices: from traditional BI vendors that have closed feature gaps and innovated, to disruptors continuing to execute. Strategic Planning Assumptions By 2020, smart, governed, Hadoop/Spark-, search- and visual-based data discovery capabilities will converge into a single set of next-generation data discovery capabilities as components of modern BI and analytics platforms. By 2021, the number of users of modern BI and analytics platforms that are differentiated by smart data discovery capabilities will grow at twice the rate of those that are not, and will deliver twice the business value. By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern BI platforms. By 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be autogenerated. By 2020, organizations that offer users access to a curated catalog of internal and external data will realize twice the business value from analytics investments than those that do not. Through 2020, the number of citizen data scientists will grow five times faster than the number of data scientists. Market Definition/Description Visual-based data discovery, a defining feature of the modern business intelligence (BI) platform, began in around 2004 and has since transformed the market and new buying trends away from IT- centric system of record (SOR) reporting to business-centric agile analytics. -
Customer Engagement Summit 2016
OFFICIAL CUSTOMER EVENT ENGAGEMENT GUIDE SUMMIT 2016 TUESDAY, 8 NOVEMBER 2016 WESTMINSTER PARK PLAZA, LONDON TECHNOLOGY THE GREAT ENABLER PLATINUM GOLD ORGANISED BY: CustomerEngagementSummit.com @EngageCustomer #EngageSummits Find out more: www.verint.com/customer-engagement THE TEAM WELCOME Steve Hurst Editorial Director E: [email protected] T: 01932 506 304 Nick Rust WELCOME Sales Director E: [email protected] T: 01932 506 301 James Cottee Sponsorship Sales E: [email protected] T: 01932 506 309 Claire O’Brien Sponsorship Sales E: [email protected] T: 01932 506 308 Robert Cox A warm welcome to our fifth Customer Engagement Sponsorship Sales E: [email protected] Summit, now firmly established as Europe’s premier T: 01932 302 110 and most highly regarded customer and employee Katie Donaldson Marketing Executive engagement conference. E: [email protected] T: 01932 506 302 Our flagship Summit has gained a reputation for delivering leading-edge world class case-study led content in a business-like yet informal atmosphere which is conducive to delegate, speaker Claire Poole and sponsor networking. It’s a place both to learn and to do business. The overarching theme of Conference Producer this year’s Summit is ‘Technology the Great Enabler’ as we continue to recognise the critical E: [email protected] role that new technologies and innovations are playing in the fields of customer and employee T: 01932 506 300 engagement. Dan Keen We have several new elements to further improve this year’s Summit – not least our move to Delegate Sales E: [email protected] this new bigger venue, the iconic London Westminster Bridge Park Plaza Hotel following on T: 01932 506 306 from four hugely successful Summits at the London Victoria Park Plaza. -
Revolution R Enterprise™ 7.1 Getting Started Guide
Revolution R Enterprise™ 7.1 Getting Started Guide The correct bibliographic citation for this manual is as follows: Revolution Analytics, Inc. 2014. Revolution R Enterprise 7.1 Getting Started Guide. Revolution Analytics, Inc., Mountain View, CA. Revolution R Enterprise 7.1 Getting Started Guide Copyright © 2014 Revolution Analytics, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of Revolution Analytics. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the Government is subject to restrictions as set forth in subdivision (c) (1) (ii) of The Rights in Technical Data and Computer Software clause at 52.227-7013. Revolution R, Revolution R Enterprise, RPE, RevoScaleR, RevoDeployR, RevoTreeView, and Revolution Analytics are trademarks of Revolution Analytics. Other product names mentioned herein are used for identification purposes only and may be trademarks of their respective owners. Revolution Analytics. 2570 W. El Camino Real Suite 222 Mountain View, CA 94040 USA. Revised on March 3, 2014 We want our documentation to be useful, and we want it to address your needs. If you have comments on this or any Revolution document, send e-mail to [email protected]. We’d love to hear from you. Contents Chapter 1. What Is Revolution R Enterprise? .................................................................... -
Pervasive Business Intelligence Techniques and Technologies to Deploy BI on an Enterprise Scale
THIRD QUArtER 2008 TDWI BEST PRACtiCES REPORT PERVASIVE BUSINESS INTELLIGENCE Techniques and Technologies to Deploy BI on an Enterprise Scale By Wayne W. Eckerson www.tdwi.org Research Sponsors Business Objects, an SAP company Corda Technologies InetSoft Technology Corp. LogiXML Microsoft MicroStrategy SAS Strategy Companion third QUArtER 2008 TDWI BEST PRACtiCES REPORT PERVASIVE BUSINESS INTELLIGENCE By Wayne W. Eckerson T echniques and Technologies to Deploy BI on an Enterprise Scale Table of Contents Research Methodology . 3 Executive Summary . 4 Introduction . 5 BI Tool Adoption and Usage Rates . 6 Role-Based Adoption . 6 Adoption Obstacles . 7 Impediments to Usage . 8 Systems Theory and Business Intelligence . 11 The BI Tipping Point . 11 “Limits to Growth” Archetype . 12 Leverage Points . 14 Usability . 17 Design . 17 Support . 23 Architecture . 25 Change Management . 27 Project Management . 29 Recommendations . 31 www.tdwi.org 1 PERVasiVE busiNEss INTElligENCE About the Author WAYNE ECKERSON is the director of TDWI Research at The Data Warehousing Institute. Eckerson is an industry analyst, consultant, and educator who has served the DW and BI community since 1995. Among his numerous published works, Eckerson is author of the bestselling book Performance Dashboards: Measuring, Monitoring, and Managing Your Business. He is also the author of TDWI’s BI Maturity Model and Assessment Service, which enables organizations to benchmark their BI programs against industry norms. Eckerson speaks frequently at industry events and works closely with BI teams to optimize the agility and value of their BI initiatives. He can be reached at [email protected]. About TDWI TDWI, a division of 1105 Media, Inc., is the premier provider of in-depth, high-quality education and research in the business intelligence and data warehousing industry. -
Iotools: High-Performance I/O Tools for R by Taylor Arnold, Michael J
CONTRIBUTED RESEARCH ARTICLE 6 iotools: High-Performance I/O Tools for R by Taylor Arnold, Michael J. Kane, and Simon Urbanek Abstract The iotools package provides a set of tools for input and output intensive data processing in R. The functions chunk.apply and read.chunk are supplied to allow for iteratively loading contiguous blocks of data into memory as raw vectors. These raw vectors can then be efficiently converted into matrices and data frames with the iotools functions mstrsplit and dstrsplit. These functions minimize copying of data and avoid the use of intermediate strings in order to drastically improve performance. Finally, we also provide read.csv.raw to allow users to read an entire dataset into memory with the same efficient parsing code. In this paper, we present these functions through a set of examples with an emphasis on the flexibility provided by chunk-wise operations. We provide benchmarks comparing the speed of read.csv.raw to data loading functions provided in base R and other contributed packages. Introduction When processing large datasets, specifically those too large to fit into memory, the performance bottleneck is often getting data from the hard-drive into the format required by the programming environment. The associated latency comes from a combination of two sources. First, there is hardware latency from moving data from the hard-drive to RAM. This is especially the case with “spinning” disk drives, which can have throughput speeds several orders of magnitude less than those of RAM. Hardware approaches for addressing latency have been an active area of research and development since hard-drives have existed. -
Integrating R with Azure for High-Throughput Analysis Hugh Analysis Shanahan
Integrating R with Azure for High- throughput Integrating R with Azure for High-throughput analysis Hugh analysis Shanahan Hugh Shanahan Department of Computer Science Royal Holloway, University of London [email protected] @HughShanahan Hugh Shanahan Integrating R with Azure for High-throughput analysis Applicability to other domains Integrating R with Azure for High- throughput analysis This project started out doing something very specific Hugh for the domain I work in (Computational Biology). Shanahan I promise that there will be no Biology in this talk !! Realised can be extended to running high-throughput jobs in R. Contrast with MapReduce / R formalisms (HadoopStreaming, Rhipe, Revolution Analytics, ... ) - parallelisation happens outside of individual R script. Hugh Shanahan Integrating R with Azure for High-throughput analysis Applicability to other domains Integrating R with Azure for High- throughput analysis This project started out doing something very specific Hugh for the domain I work in (Computational Biology). Shanahan I promise that there will be no Biology in this talk !! Realised can be extended to running high-throughput jobs in R. Contrast with MapReduce / R formalisms (HadoopStreaming, Rhipe, Revolution Analytics, ... ) - parallelisation happens outside of individual R script. Hugh Shanahan Integrating R with Azure for High-throughput analysis Applicability to other domains Integrating R with Azure for High- throughput analysis This project started out doing something very specific Hugh for the domain I work in (Computational Biology). Shanahan I promise that there will be no Biology in this talk !! Realised can be extended to running high-throughput jobs in R. Contrast with MapReduce / R formalisms (HadoopStreaming, Rhipe, Revolution Analytics, ..