Pervasive Business Intelligence Techniques and Technologies to Deploy BI on an Enterprise Scale
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Relationship Between Business Intelligence and Supply Chain Management for Marketing Decisions
Universal Journal of Industrial and Business Management 2(2): 31-35, 2014 http://www.hrpub.org DOI: 10.13189/ ujibm.2014.020202 Relationship between Business Intelligence and Supply Chain Management for Marketing Decisions Neven Šerić1, *, Ante Rozga1, Ante Luetić2 1Faculty of Economics, University of Split, Cvite Fiskovića 5 2Split Shipyard,Put Supavla 21. *Corresponding Author: [email protected] Copyright © 2014 Horizon Research Publishing All rights reserved. Abstract The companies that apply the concept of to support marketing managers in making strategic and tactic business intelligence in their marketing decisions in some of decisions. The purpose of this study was to capture the the following ways were included in this study: at the level of companies that apply the concept of business intelligence in the entire system or specific marketing strategy of companies their business. While researching on the orientation on (e.g. marketing department, research development, supply chain and supply chain management, companies commercial, etc.), apply business intelligence only in certain selected were those which have been able to confirm that marketing processes or projects, used in business from the they have at least one link in the supply chain in order to be technology and platform for data warehouse, data mining, included in the sample. Also, they were supposed to be active OLAP tools, using advanced analytical techniques of when applying business intelligence for marketing function. simulation and visualization applications. Variables examined were categorized into four groups: business intelligence, supply chain management, information 2. Sample Selection and Questionnaire visibility and integration. Factor analysis was used to facilitate the connection of these groups of variables, i.e. -
LEVERAGING BUSINESS INTELLIGENCE in SECURITY STRATEGY Oday, Nothing Is More Critical to Security and HARNESS BIG DATA Loss Prevention Operations Than Meaningful Data
LEVERAGING BUSINESS INTELLIGENCE IN SECURITY STRATEGY oday, nothing is more critical to security and HARNESS BIG DATA loss prevention operations than meaningful data. Every department within the operation Today, the sources and volume of data collected have bears the responsibility to not only provide exploded. Security operations collect every event and T incident from every transaction from various sources useful data, but to continually improve the value of that data. Business Intelligence (BI) is used to help including telephone/radio calls, alarms, environmen- companies gain insight into their operations; segment tal sensors, intrusion-detection systems, and video and target information to improve customer security, surveillance. safety, and experience while finding anomalies in the The modern security department uses a set of pro- heaps of data to run more efficiently and effectively. cesses and supporting technologies for data manage- This white paper explores how to change the game ment to allow security practitioners greater flexibility for companies. Learn how to collect and leverage in cobbling together disparate systems into a unified data to achieve effective loss prevention, risk mitiga- security information system that enables Security Di- tion, efficient fraud detection, incident analysis, and rectors to know exactly what’s going on, in real-time monitoring. while providing analysis to generate actionable items that can give security operations the agility it needs CAPITALIZING ON BIG DATA: Strategies outperforming companies are taking to deliver results Leaders are And are % % 75 166 2.2x of Leaders cite more likely to more likely to growth as the make most have formal key source of decisions career path value from based on data for analytics analytics 80% 60% 85% Leaders measure Leaders have Leaders have the impact predictive some form of of analytics analytics shared analytics investments capabilities resources Leveraging Business Intelligence in Security Strategy in times of crises. -
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 -
6 Levels of Business Intelligence Value
6 Levels of Business Intelligence Value In today’s hyper-competitive market environment, business intelligence continues to be an area of investment and interest for businesses. The ability to turn raw data into meaningful and useful information that can impact business performance 6 Levels of is a powerful value proposition. And, with technology and ease-of-use advances, BI Value business intelligence solutions today are more accessible to everyone across an organization – from supply chain managers to the vice president of sales to the C-suite. Yet many businesses are only scratching the surface of what’s possible with business intelligence. To realize the potential of business intelligence and take its value to the next level in your organization, you need a solid understanding of where you are, what you want to achieve, and what’s possible. From generating reports and charts that depict business performance, to implementing a truly transformative solution that uses powerful advanced analytics to predict behaviors and outcomes, business intelligence can be a strategic weapon that significantly impacts your bottom-line. The following 6 Levels of Business Intelligence Value depict types of analytical capabilities and their related value. The potential is huge. What level of value is your organization achieving today? Where will you take it tomorrow? High • What will happen? Machine Learning Prescriptive • When will it happen? Potential outcomes based • Why will it happen? on complex interactions Predictive • Based on historical data, -
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 -
Evaluating Business Intelligence / Business Analytics Software for Use in the Information Systems Curriculum
Information Systems Education Journal (ISEDJ) 13 (1) ISSN: 1545-679X January 2015 Evaluating Business Intelligence / Business Analytics Software for Use in the Information Systems Curriculum Gary Alan Davis [email protected] Charles R. Woratschek [email protected] Computer and Information Systems Robert Morris University Moon Township, PA 15108 USA Abstract Business Intelligence (BI) and Business Analytics (BA) Software has been included in many Information Systems (IS) curricula. This study surveyed current and past undergraduate and graduate students to evaluate various BI/BA tools. Specifically, this study compared several software tools from two of the major software providers in the BI/BA field. The participants in the study evaluated each software tool according to three key criteria: 1) functionality, 2) ease of use, and 3) learning effectiveness. The “learning effectiveness” criterion was used to determine which BI/BA tools provided the most effective learning of BI/BA concepts in the IS classroom. The three criteria were used to develop recommendations for including specific BI/BA software tools in the IS curriculum. Based on the findings of the study, the authors recommend that colleges and universities consider the use of the IBM-Cognos suite of tools as a viable means for teaching BI/BA concepts in their Information Systems curricula. The results of the study are relevant to any college or university that currently includes (or is considering the inclusion of) Business Intelligence / Business Analytics concepts in its Information Systems curriculum. Keywords: Business Intelligence, Business Analytics, Software Evaluation, Information Systems Curriculum 1. INTRODUCTION students who are attending/have attended BI/BA-related courses. -
A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business
sustainability Article A Model to Manage Cooperative Project Risks to Create Knowledge and Drive Sustainable Business Marco Nunes 1,* , António Abreu 2,3,* and Célia Saraiva 4 1 Department of Industrial Engineering, University of Beira Interior, 6201-001 Covilhã, Portugal 2 Department of Mechanical Engineering, Polytechnic Institute of Lisbon, 1959-007 Lisbon, Portugal 3 CTS Uninova, Centre of Technology and Systems, 2829-516 Caparica, Portugal 4 Department of Science and Technology, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; [email protected] * Correspondence: [email protected] or [email protected] (M.N.); [email protected] (A.A.) Abstract: Efficient cooperation between organizations across all the phases of a project lifecycle is a critical factor to increase the chances of project success and drive sustainable business. However, and according to research, despite the large benefits that efficient organizational cooperation provides to organizations, they are still often reluctant to engage in cooperative partnerships. The reviewed literature argues that the major reason for such a trend is due to the lack of efficient and actionable supportive models to manage organizational cooperative risks. In this work we propose a model to efficiently support the management of organizational cooperative risks in project environments. The model, MCPx (management of cooperative projects), was developed based on four critical scientific pillars, (1) project risk management, (2) cooperative networks, (3) social network analysis, and (4) business intelligence architecture, and will analyze in a quantitative way how project cooperative behaviors evolve across a bounded time period, and to which extent they can turn into a cooperative Citation: Nunes, M.; Abreu, A.; project risk (essentially potential threats). -
Conversational BI: an Ontology-Driven Conversation System for Business Intelligence Applications
Conversational BI: An Ontology-Driven Conversation System for Business Intelligence Applications Abdul Quamar1, Fatma Ozcan¨ 1, Dorian Miller2, Robert J Moore1, Rebecca Niehus2, Jeffrey Kreulen2 1IBM Research AI, 2IBM Watson Health 1ahquamar|fozcan|[email protected], 2millerbd|rniehus|[email protected] ABSTRACT Business intelligence (BI) applications play an important role in the enterprise to make critical business decisions. Conversational interfaces enable non-technical enterprise us- ers to explore their data, democratizing access to data signif- icantly. In this paper, we describe an ontology-based frame- Business Model work for creating a conversation system for BI applications (Cube Definition) termed as Conversational BI. We create an ontology from a business model underlying the BI application, and use this ontology to automatically generate various artifacts of the conversation system. These include the intents, entities, as well as the training samples for each intent. Our approach builds upon our earlier work, and exploits common BI ac- RDBMS cess patterns to generate intents, their training examples Figure 1: Traditional BI System Architecture and adapt the dialog structure to support typical BI op- erations. We have implemented our techniques in Health etc. Figure 1 shows a typical architecture of a BI stack. Insights (HI), an IBM Watson Healthcare offering, provid- The underlying data resides in a traditional RDBMS, and ing analysis over insurance data on claims. Our user study a business model is created in terms of an OLAP cube def- demonstrates that our system is quite intuitive for gaining inition [13] that describes the underlying data in terms of business insights from data. -
Performance Management Using Data Visualization, Mashups, and Mobile Intelligence
c11PerformanceManagementusingDataVisualizationMashupsandMobileIntelligence.qxd 11/9/12 6:40 PM Page 333 Chapter Performance Management using Data 11 Visualization, Mashups, and Mobile Intelligence Quick Look Learning Outcomes Case 1, Opening Case: Data Viz iPad App Improves ᕡ Describe how data visualization applications, data discovery America First’s Performance systems, and interactive reports support organizational functions and decision making, and how visual analytics are helping managers improve financial performance. 11.1 Data Visualization and Data Discovery ᕢ Explain data mashup technology as an alternative self- service and end-user systems development approach. 11.2 Enterprise Data Mashups ᕣ Describe how enterprise dashboards and reporting support 11.3 Business Dashboards executive, managerial, and operational levels by leveraging real-time data and people’s natural ability to think visually. 11.4 Mobile Dashboards and Intelligence ᕤ Discuss why a mobilized workforce is best supported by a mobile approach to data, and explain the competitive Key Terms advantages of mobile intelligence. Chapter 11 Link Library Evaluate and Expand Your Learning • IT and Data Management Decisions • Questions for Discussion & Review • Online Activities • Collaborative Work Case 2, Visualization Case: Are You Ready for Some Football? Case 3, Video Case: Mashup-Driven Dashboards and Reporting Data Analysis & Decision Making: Know Your Facebook Fans with Mobile Intelligence References 333 c11PerformanceManagementusingDataVisualizationMashupsandMobileIntelligence.qxd 11/9/12 6:40 PM Page 334 QUICK LOOK at Chapter 11, Performance Management using Data Visualization, Mashups, and Mobile Intelligence In the past, managers usually did not have a convenient metrics and the ability to analyze data themselves.The term way to analyze and develop a good understanding of a lot enterprise mashup is used to differentiate business-related of data, or to compile data from diverse sources to get mashups from web mashups, as you read in Chapter 8. -
Business Intelligence (BI) Strategy
BUSINESS Planning & Business INTELLIGENCE Intelligence IT Services STRATEGY January 2015 A Business Intelligence Strategy for harnessing and exploiting the data in the University’s central business systems, external sources including social media, regulatory data and survey data in order to secure competitive advantage. 1 Executive Summary What are we trying to achieve? Enhancing the Business Intelligence capability of the University will ensure that SMG and staff have a clear and straightforward insight into both internal and external data, have top quality management and performance information and are able to better forecast future trends through the deployment of predictive analytics. This can be delivered through our IT networks to PCs, laptops, tablets and smartphones. In short, we will access, collate and combine our corporate information in new ways to improve the way we work. Timely access to trusted data will inform actionable decision making to improve alignment with University strategy, improve efficiencies and effectiveness, reduce operational costs, ensure statutory compliance and minimise risk. The key to BI success at UOG is achieving ease of use of BI reporting systems to ensure widespread acceptance and end user self- service. This will be achieved by the provision of delivered standard reporting at a detailed level as well as the ability to generate bespoke analyses. What is Business Intelligence? Business Intelligence (BI) is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information which can be used to enable more effective strategic, tactical, and operational insights and decision- making. Its end result should be to transform the way information is used to assist the University in moving forward. -
Erprise Resources Planning (ERP) Framework for Flexible Manufacturing Systems Using Business Intelligence (BI) Tools
International Journal of Supply and Operations Management IJSOM May 2016, Volume 3, Issue 1, pp. 1112-1125 ISSN-Print: 2383-1359 ISSN-Online: 2383-2525 www.ijsom.com An Integrated Enterprise Resources Planning (ERP) Framework for Flexible Manufacturing Systems Using Business Intelligence (BI) Tools Mehrdad Nouri Koupaei a, Mohammad Mohammadi a* and Bahman Naderi a a Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran Abstract Nowadays Business intelligence (BI) tools provide optimal decision making, analyzing, controlling and monitoring of operations in enterprise systems like enterprise resource planning (ERP) and mainly refer to strong decision making methods used in online analytical processing, reporting and data analysis, such as improving internal processes, analysis of resources, information needs analysis, costs reduction and revenue increase. The main purpose of the paper is creating a unified framework for the application of BI in ERP systems which results of value-added inflexible manufacturing systems (FMS). In this paper, business process system and interaction between technology and environment by applying BI in ERP systems of companies that use flexible manufacturing systems have been presented. This paper is a comprehensive review of recent literature that examined the effects of BI systems on the four levels of Tenhiala et al.' Model. This model based on cross-sectional data from 151 manufacturing plants proved that ERP is essential for the FMS. According to results of this paper, the answer to this question is important “How can we use the potential data and intelligence of BI in ERP systems for the effective flexible manufacturing systems?” This study has four hypotheses to answer this question and based on results, all four hypotheses were confirmed. -
Insurer's Choice
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