Business Intelligence Strategy

Total Page:16

File Type:pdf, Size:1020Kb

Business Intelligence Strategy Business Intelligence Strategy A Practical Guide for Achieving BI Excellence John Boyer Bill Frank Brian Green Tracy Harris Kay Van De Vanter MC Press Online, LLC Ketchum, ID 83340 Business Intelligence Strategy A Practical Guide for Achieving BI Excellence John Boyer, Bill Frank, Brian Green, Tracy Harris, Kay Van De Vanter First Edition First Printing—September 2010 © 2010 IBM Corporation. All rights reserved. The following terms are trademarks or registered trademarks of International Business Machines Corporation in the United States, other countries, or both: IBM, the IBM logo, and Cognos. A current list of IBM trademarks is available on the Web at http://www.ibm.com/legal/copytrade.shtml. Other company, product, or service names may be trademarks or service marks of others. While every attempt has been made to ensure that the information in this book is accurate and complete, some typographical errors or technical inaccuracies may exist. IBM does not accept responsibility for any kind of loss resulting from the use of information contained in this book. The information contained in this book is subject to change without notice. The publisher, authors, and IBM do not guarantee the accuracy of the book and do not assume responsibility for information included in or omitted from it. Printed in Canada. All rights reserved. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. MC Press offers excellent discounts on this book when ordered in quantity for bulk purchases or special sales, which may include custom covers and content particular to your business, training goals, marketing focus, and branding interest. MC Press Online, LLC Corporate Offices P.O. Box 4886 Ketchum, ID 83340-4886 USA For information regarding sales and/or customer service, please contact: MC Press P.O. Box 4300 Big Sandy, TX 75755-4300 USA For information regarding permissions or special orders, please contact: [email protected] ISBN: 978-158347-362-7 Acknowledgements We would all like to thank the IBM team and their extensive list of subject-matter experts who contributed content, joined our discussions, and helped to provide expertise that was critical to making this book possible: Thank you to Michael Dziekan and Forrest Palmer, who spent many hours with our team providing their deep expertise and guidance and contributing significant content and graphics, particularly on organizational structures and competency centers. And to Meg Dussault, who helped enlighten our team to the “sweet spots” of information. Thank you to Kevin Cohen and Derek Lacks for their assistance with and debates on the many levels of value within an organization. And to Andreas Coucopoulos, for his many hours delving into the technology strategy that is so critical to a strate- gic initiative. We would also like to thank Rob Ashe, Eric Yau, Harriet Fryman, and Mychelle Mollot for their support in making this book possible. We also want to thank the MC Press team—Merrikay Lee, Victoria Mack, Thomas Stockwell, and Katie Tipton—for their patience and expertise, as well as Susan Visser from the IBM Information Management team at IBM. From John Boyer I would like to thank my chain of management—Oussama Warwar, Jeff Henry, and Dirk Izzo—who contributed to the content and helped me understand how the corporate vision is translated to strategy. I am also deeply indebted to the BICoE Team—Mangai, Robert, Ram, Venkat, Guru, and KT—who make my job easy. You put the “E” in the BICoE. Thank you, too, to Lynn Moore; best of luck on your book, my friend. From Bill Frank I would like to thank all of the IBM Cognos team who made themselves available in our BI journey over the years, the Cognos Innovation Center, Tracy Harris, and the “cheese kids.” There are so many individuals from J&J that have been my sounding board, taken initiative to turn ideas into reality, shaped my thinking, collaborated with me, and put up with my endless evangelizing about standards, practices, and the need for a BICC. I’m sure I’d miss someone in the short space I have here if I started listing names. I would like to thank the SSB BI Domain, the BITS team, the BICC leads, and the entire J&J BI COP for their efforts that have led to successes in trans- forming, improving, and creating innovation with BI solutions across the enterprise. Thanks also to the members of my immediate management as well as senior execu- tives who have encouraged, mentored, and supported me. From Brian Green On behalf of BlueCross BlueShield of Tennessee, I would like to acknowledge the dedication and contributions of the entire Data Management and Information Delivery team. I would also like to acknowledge Frank Brooks for his tremen- dous leadership, vision, and support. Thanks also to the members of my immediate management as well as senior executives who have encouraged, mentored, and supported me. From Tracy Harris A big thanks to Leah MacMillan, Meg Dussault, and Harriet Fryman for their guidance, insight, and vision and for recognizing the critical need for this endeavor. I would also like to thank Debbie Ng and Jacqueline Coolidge for helping to initially bring this “excellent” group together to make this book possi- ble. Thank you also to Jennifer Schmitz, Becky Smith, Jennifer Hanniman, Rebecca Wormleighton, Brent Winsor, Catherine Frye, and Nina Sandy for their significant contributions to this endeavor. From Kay Van De Vanter I would like to thank my fellow Boeing Business Intelligence Competency Center team members and management for their insightful comments and discus- sions. Special thanks to our BICC executive manager for his careful reviews and attention to detail and to our communications focal for working closely with me to obtain the permissions and approvals needed to participate in this endeavor. Finally, we would like to thank the various thought leaders and analysts in the industry who continue to provide critical research in this area that allows organi- zations to better understand how they can achieve success. In particular, we would like to acknowledge the teams at BIScorecard, Nucleus Research, Busi- ness Applications Research Center, Gartner, Forrester, and TDWI. And we would like to thank Roland Mosimann, Patrick Mosimann, and Jack Musgrove at BII, PMSI, and Aline for their research, support, vision, and foundational contri- butions to this effort. About the Authors The content for this book was prepared by the IBM Cognos BI Excellence Advi- sory Board, a team of representatives from leading enterprise organizations who research, advise, and share best practices for achieving excellence in Business Intelligence and Performance Management initiatives. John Boyer John Boyer is manager of the BI Advisory Team at The Niel- sen Company. There, he oversees adoption, enablement, and internal consulting for all things BI. Before joining Nielsen, John spent several years as a BI architect and trusted advisor at IBM. After graduating from medical school, his aptitude, pas- sion, and bedside manner took him first to a healthcare clinic, where he rose to Director of Finance and Information Systems. John has spent the past 15 years consulting in software development, business intelligence, and data warehousing. John is chair of the Illinois Cognos User Group. As a conference speaker, he has been invited to speak at a number of national events, including Information on Demand, Cognos Forum, and the Composite Software User Group. About The Nielsen Company The Nielsen Company (www.nielsen.com) is a leading global information and media company providing essential integrated marketing and media measurement information, analytics, and industry expertise to clients around the world. Through its broad portfolio of products and services, Nielsen tracks sales of consumer prod- ucts, reports on television viewing habits in countries representing more than 60 percent of the world’s population, and measures Internet audiences. Nielsen also produces trade shows, print publications, and online newsletters. The company is active in approximately 100 countries, with headquarters in New York City. About the Authors Bill Frank Bill Frank is the Manager, ITGF BI Practice, at Johnson & Johnson. He has more than 25 years of experience in decision support and business intelligence and is a certified Project Management Professional (PMP). Bill has worked in several major companies, including AT&T, Time Warner, and most recently Johnson & Johnson. At J&J, Bill has played a key role in the development of BI solutions, organizational model, governance, and practices and in evangelizing BI across the enterprise. Bill is a founding member of the J&J BI Center of Practice and co-leads this 300- member internal group focused on leverage, communication, and sharing proven practices. Bill also serves as the liaison to the IBM Cognos executive, marketing, and technology teams. He teamed with other J&J teams to lead the creation of the IBM Cognos enterprise agreement and the shared environments that are key to supporting J&J’s standardization and consolidation efforts. Currently, Bill is developing enterprise data warehouse and BI strategies to support the J&J Global Finance organization. He is also a member of the IBM BI Excellence Advisory Board and several other external organizations focused on BI technologies. About Johnson & Johnson Johnson & Johnson (www.jnj.com) is a Fortune 100™ company, encompassing the world’s premier consumer health company, the world’s largest and most diverse medical devices and diagnostics company, fourth-largest biologics com- pany, and eighth-largest pharmaceuticals company. J&J has more than 250 operating companies in 60 countries and employs approximately 114,000 people. The company is headquartered in New Brunswick, New Jersey.
Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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,
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 021-2012: How to Create a Business Intelligence Strategy
    SAS Global Forum 2012 Applied Business Intelligence Paper 021-2012 How to create a Business Intelligence Strategy Guy Garrett, Achieve Intelligence Limited, Brighton, East Sussex, United Kingdom ABSTRACT You may wonder how some organizations are so astute at managing to keep abreast of changing customer behavior in the market. There are undoubtedly many factors - however central to the answer is that they recognize the value of their information assets and alter their strategic vision with new perspective. A Business Intelligence Strategy is a roadmap that enables businesses to measure their performance and seek out competitive advantages and truly "listen to their customers" using data mining and statistics. INTRODUCTION In this paper, you'll discover the crucial questions to answer when aligning a BI strategy to your organization’s overall direction, an overview of the artifacts to deliver, and how SAS® software can underpin your strategy throughout the process. WHAT IS A BUSINESS INTELLIGENCE STRATEGY In 1958, IBM researcher Hans Peter Luhn summarized “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal” as Business Intelligence. Today, would he be surprised to know that many companies fail to create a BI Strategy, resulting in misalignment of their strategic vision versus it's execution. In a performance related culture we should be clear about what we measure, as organizations, and how to drive improvement from our analyses. Statistics are used throughout the media and in day-to-day life to try to make sense of why events have happened the way they have and to try and predict how they may happen in the future.
    [Show full text]
  • The Use of Business Intelligence Tools to Drive Asset Performance Management Improvements
    The Use of Business Intelligence Tools to Drive Asset Performance Management Improvements By Mark Brennan Philippe Loustau Alesio Lanzara Engineering Manager Principal Engineer Specialist Consulting Engineer Vibration, dynamics and noise (formerly BETA Machinery Analysis) Wood Group Presented at: Gas Machinery Conference 2017 Pittsburgh, PA 1. Introduction A wealth of data is held by organizations in disparate systems such as maintenance management, enterprise resource planning (ERP), process control, process historians, condition monitoring, asset integrity management and a plethora of spreadsheets. Typically these data sets are stored and analyzed independently, however, maintenance and reliability practitioners frequently have to bring data together from multiple sources to support decision making or to identify improvement opportunities. And these processes can often be challenging, inefficient and time-consuming, with the effectiveness of the outcome influenced by the spreadsheet skills and determination of the individual. The latest generation of business intelligence (BI) tools, often already available and being used within an organization, are an efficient, repeatable way to draw information together and present a more complete and timely picture of asset performance and asset health. Whereas these BI tools can open up endless possibilities for data analysis, data visualization and the calculation of performance metrics that are potentially “interesting”, challenges lie in the inconsistency of data across systems, gaps in data sets, inaccuracies in data and differing thoughts on how to use the data to address organizational imperatives. Using case studies from a variety of gas machinery and industrial applications, this paper explores the challenges and benefits of the BI tools approach to analyzing asset performance and asset management programs.
    [Show full text]
  • Business Intelligence for Creating an Inclusive Model of Contracting and Procurement in the City of Durham
    FRANK HAWKINS KENAN INSTITUTE OF PRIVATE ENTERPRISE REPORT BUSINESS INTELLIGENCE FOR CREATING AN INCLUSIVE MODEL OF CONTRACTING AND PROCUREMENT IN THE CITY OF DURHAM JAMES H. JOHNSON, JR., Ph.D. URBAN INVESTMENT STRATEGY CENTER BUSINESS INTELLIGENCE RESEARCH BRIEF • KENAN INSTITUTE REPORT INTRODUCTION Gentrifying cities increasingly are adopting inclusive and equitable development policies, strategies, tools, and regulatory practices to minimize, if not altogether eliminate, the demographic and economic dislocations that often accompany their growing attractiveness as ideal places to live, work, and play for a creative class of young people and well-resourced retirees who are predominantly white (Delgado, 2014; Liu, 2016; Baux, 2018; Coffin, 2018; McFarland, 2016; Parilla, Joseph, 2017). Creating greater opportunities for historically under-utilized businesses to grow and prosper through enhanced local government contracting and procurement is one mechanism through which gentrifying cities are trying to generate greater equity and shared prosperity (Brichi, 2004; Edelman and Azemati, 2017; Robinson, 2017). Specifically, local officials in such cities are moving aggressively to transform their existing procurement systems into fully automated supply chain management systems, with the overarching goal of making their entrepreneurial/ business ecosystems—the major job generators and sources of wealth creation--more transparent, accessible, equitable, and inclusive for diverse suppliers of goods and services ((Florida, 2017; Lohrentz, 2016; McKoy and Johnson, 2018; Fairchild and Rose, 2018) . For cities like Durham that are struggling with how to respond to gentrification-induced demographic and economic dislocations (DeMarco and Hunt, 2018; Vaughan and Brosseau, 2018), the concept of an inclusive supply chain management system may be difficult to comprehend (Willets, 2017).
    [Show full text]