According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

The Social Psychology of Organizations.

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.

https://courses.washington.edu/geog482/resource/14_Beyond_Accuracy.pdf

Beyond accuracy: what data quality means to data consumers

How we Think

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.

/pdf/data-quality-assessment-2ibv84wxwd.pdf

Data quality assessment

http://web.cba.neu.edu/~ywlee/publication/AIMQ.pdf

AIMQ: a methodology for information quality assessment

ATA-QUALITY (DQ) PROBLEMS ARE INCREASINGLY EVIdent, particularly in organizational databases. Indeed, 50% to 80% of computerized criminal records in the U.S. were found to be inaccurate, incomplete, or ambiguous. The social and economic impact of poor-quality data costs billions of dollars. [5-7, 10]. Organizational databases, however, reside in the larger context of information systems (IS). Within this larger context, data is collected from multiple data sources and stored in databases. From this stored data, useful information is generated for organizational decision-making. A new study reveals businesses are defining data quality with the consumer in mind.

Data quality in context

1. Why Quality Information and Knowledge? Two Propositions. Create Knowledge with Quality Information. Book Organization. 2. Manage Information as a Product. Social and Business Impacts. Social Impacts. Business Impacts. Customer Service. Management Support. Bottom Line. Fundamental Concepts. Information versus Data. From Product to Information Manufacturing. Information Manufacturing Systems. The Information System Development Cycle. The Total Data Quality Management (TDQM) Cycle. Dimensions of Information Quality. The Information Product Manager. Managing Information as a Product-The Four Principles. Managing Information as a Byproduct Will Not Work. What Is Managed? How Is It Managed? Why Is It Managed? What Is Success? Who Manages It? Appoint the Information Product Manager. The De Facto Information Product Manager. Information Product Manager's Responsibilities. Establish an Information Quality Program. Conclusion. 3.Define Information Quality. The Information System Perspective. Data Deficiency. Design Deficiencies. Operation Deficiencies. Some Implications of Information Systems Design. The Information Consumer Perspective. Fitness for Use. Dimensions of IQ. Define IQ in Organizational Context. Method. Intrinsic IQ Pattern. Problem Analysis and Solutions. Accessibility IQ Pattern. Contextual IQ Pattern. Implications for IS Professionals. Conclusion. 4. Measure, Analyze, and Improve IQ. Measure IQ. Subjective IQ Metrics. Dimensional IQ Assessment. IQ Knowledge Assessment. Objective, Application-Independent Metrics. Application-Dependent IQ Metrics. Analyze IQ. IQ Assessment (IQA). Survey Results Analysis: A Case Study. Integrity Analyzera. Data Integrity. Frequency Checks. Improve IQ. Conclusion. Appendix: IQA Survey. 5. Create Organizational Knowledge. Organizational Alzheimer's Disease. Information and Experience Are Knowledge Sources. Information Contains Knowledge. Experience Manifests Knowledge. What Is Organizational Knowledge? Organizational Knowledge in Three Modes. Assess Organizational Knowledge. Why Create Organizational Knowledge? How to Create Organizational Knowledge. Eyewear Company Revisited. Conclusion. Appendix: IQK Survey. 6. Manage Knowledge as Assets. Power of Collective Knowledge. What Is Knowledge Management? Why Knowledge Management? How to Manage Knowledge Assets. Platform for Knowledge Management. Ten Strategies for Knowledge Management. Competency Networks. Conclusion. 7. Create Customized Solutions. Defining Intellectual Capital, Intellectual Asset, and Solution. Intellectual Capital. Intellectual Asset and Solution. Harvesting and Hardening Assets for Reuse. Knowledge Asset Reuse Process. Intellectual Capital from Project Information. Customized Solutions from Customer Knowledge. Knowledge Asset Development Process. Competency Leader. Asset Manager. Knowledge Architect. Asset Publisher. Asset Broker. Enterprise Knowledge Structure. Requirements for Asset Identification. Selection Criteria for Asset Hunting. Life Cycle Management. Quality of Intellectual Capital. Levels of Life Cycle. From Data to Knowledge. Data and Knowledge Mining. Knowledge Cockpit. Customer Information. Market Information. Business Experiences. Data Warehouses. Network Agents as Knowledge Intermediaries. Network Agents in Electronic Commerce. Verification Driven Agent. Discovery-Driven Agent. Text Mining. Information Filtering. Collaborative Filtering. Conclusion. 8. Network Knowledge Infrastructure. Corporate Knowledge Infrastructure. Knowledge Architecture for the Extended Enterprise. Design Requirements. The Internet, Intranets, and Extranets. Intranets for Knowledge Sharing and Collaboration. ICM AssetWeb. Competency Networks. Best Practice. Navigator. Idea Generation and Team Collaboration. Issue-Based Structured Collaboration. Shared Central Repository. Capture Team Dialog and Issue Resolution. Managing Team's Knowledge. Extranets for Customer Care Management. Net Car Dealerships. Groupware for Collaboration. The Internet and Intranets for Networked Communication. Security. Conclusion. 9. Prosper in the Digital Economy. Knowledge-based Economy. Competitive Drivers. Innovation. Responsiveness. Productivity. Competency. Competing for Intellectual Influence. Helping Customers to Win. Empowering Knowledge Workers to Execute. Configuring Business Partners to Team. A Web-based Advisor. Transforming the Future of Life. Networked Life. Knowledge Sharing and Mining. Being Virtual rather than Physical. Conclusion. Information Quality Bibliography. Knowledge Management Bibliography. Glossary. Index.

Quality information and knowledge

Data Quality provides an expos of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.

Yang W. Lee

Papers

Data quality assessment

AIMQ: a methodology for information quality assessment

Data quality in context

Quality information and knowledge

Data Quality