The Experts below are selected from a list of 21 Experts worldwide ranked by ideXlab platform
David Loshin - One of the best experts on this subject based on the ideXlab platform.
-
18 – Building the Data Quality Practice
Enterprise Knowledge Management, 2001Co-Authors: David LoshinAbstract:This concluding chapter brings together the methods and processes that were discussed in this book, augmented with some of the operational details needed in building a successful Data Quality Practice. Data Quality is not an unclear concept but something that can be quantified, measured, and improved, all with a strict focus on return on investment. The first step in building a successful Data Quality Practice is problem recognition. The other steps involved in this process include management support and the Data ownership policy, Data Quality awareness, mapping the information chain, Data Quality scorecard, current state assessment, requirements assessment, project selection (choosing a Data Quality problem), team selection, defining the metaData model, defining the Data Quality rules, archaeology/Data mining, supplier management, executing the solution and measure improvement. The success of any small project only contributes to the greater success of the entire program. Therefore, each small success should be used as leverage with the senior-level sponsors to gain access to bigger and better problems. The goal of becoming an organization that leverages its Data resource into a source of enterprise knowledge can be achieved but not without a firm base of high-Quality Data.
-
Enterprise knowledge management: the Data Quality approach
2000Co-Authors: David LoshinAbstract:1. Introduction 2. Who Owns Information? 3. Data Quality in Practice 4. Economic Framework of Data Quality and the Value Proposition 5. Dimensions of Data Quality 6. Statistical Process Control and the Improvement Cycle 7. Domains, Mappings, and Enterprise Reference Data 8. Data Quality Assertions and Business Rules 9. Measurement and Current State Assessment 10. Data Quality Requirements 11. MetaData, Guidelines, and Policy 12. Rule-Based Data Quality 13. MetaData and Rule Discovery 14. Data Cleansing 15. Root Cause Analysis and Supplier Management 16. Data Enrichment/Enhancement 17. Data Quality and Business Rules in Practice 18. Building the Data Quality Practice
David Loshi - One of the best experts on this subject based on the ideXlab platform.
-
enterprise knowledge management the Data Quality approach
2000Co-Authors: David LoshiAbstract:Preface Chapter 1 - Introduction Chapter 2 - Who Owns Information? Chapter 3 - Data Quality in Practice Chapter 4 - Economic Framework of Data Quality and the Value Proposition Chapter 5 - Dimensions of Data Quality Chapter 6 - Statistical Process Control and the Improvement Cycle Chapter 7 - Domains, Mappings, and Enterprise Reference Data Chapter 8 - Data Quality Assertions and Business Rules Chapter 9 - Measurement and Current State Assessment Chapter 10 - Data Quality Requirements Chapter 11 - MetaData, Guidelines, and Policy Chapter 12 - Rule-Based Data Quality Chapter 13 - MetaData and Rule Discovery Chapter 14 - Data Cleansing Chapter 15 - Root Cause Analysis and Supplier Management Chapter 16 - Data Enrichment/Enhancement Chapter 17 - Data Quality and Business Rules in Practice Chapter 18 - Building the Data Quality Practice
Dov Biran - One of the best experts on this subject based on the ideXlab platform.
-
REDEFINING INTEGRATION FOR Data Quality Practice:IMPLEMENTING INTEGRATION TECHNOLOGY FORDISTRIBUTED BUSINESS STRATEGY
2002Co-Authors: Yang W. Lee, Dov BiranAbstract:Implementing integration technology is accepted as a common strategy for transforming a disconnected and rigid business Practice into an agile and well-coordinated Practice. Research on integration technology reports incongruities between strategy and implementation, while organizations continue to experience costly endeavors with anticipated benefits of improved business performance. This research examines strategy development and implementation of integration technology using a new lens, Data Quality Practice perspective. Specifically, we aim to redefine integration by examining how organizations define and solve integration problems using Data Quality Practices as a feedback mechanism. We study integration Practices in twelve organizations to redefine integration by analyzing their development and implementation of integration strategies.
Yang W. Lee - One of the best experts on this subject based on the ideXlab platform.
-
REDEFINING INTEGRATION FOR Data Quality Practice:IMPLEMENTING INTEGRATION TECHNOLOGY FORDISTRIBUTED BUSINESS STRATEGY
2002Co-Authors: Yang W. Lee, Dov BiranAbstract:Implementing integration technology is accepted as a common strategy for transforming a disconnected and rigid business Practice into an agile and well-coordinated Practice. Research on integration technology reports incongruities between strategy and implementation, while organizations continue to experience costly endeavors with anticipated benefits of improved business performance. This research examines strategy development and implementation of integration technology using a new lens, Data Quality Practice perspective. Specifically, we aim to redefine integration by examining how organizations define and solve integration problems using Data Quality Practices as a feedback mechanism. We study integration Practices in twelve organizations to redefine integration by analyzing their development and implementation of integration strategies.
Kassahun Alemu - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of Malaria Data Quality Practice and Its Potential Factors in Nedjo Woreda, West Wellega Zone, Oromia Regional State, West Ethiopia
Research and reviews: journal of medical and health sciences, 2019Co-Authors: Sarbessa Dereje, Adamu Birhanu, Kassahun AlemuAbstract:Introduction: Malaria is one of the most common infectious diseases and a great public health problem worldwide, particularly in Africa and south Asia. Even though Quality Data on malarial disease is critical for planning, decision making and assessment of the efficiency and effectiveness of the intervention on malaria control program, the available Data are of poor Quality. Objective: The aim of this study was to assess malaria Data Quality and its potential factors in Nedjo, west Wellega zone, Oromia regional state, West Ethiopia, 2013. Methods: Institutional based cross-sectional study triangulated by qualitative study design was employed. The methods include observation of Data management Practices, review of existing documents and in-depth interview with key informants. All (49 kebeles) in the woreda were clustered in to 5 clusters. Three clusters were randomly selected. All the health posts and all health centers in the selected cluster were included. Result: Findings of the result revealed out that all the assessed Data Quality dimensions are far below standard set by federal ministry of health. The mean percentage of timeliness was 69.7% and completeness was 73.7% compared to the standard set by federal ministry of health (90%). The ratio of recounted to reported Data over six months was 0.766 with the mean discrepancy value of 54.5 (23.34%). problem of forgetting to transfer Data from notebook to registration book, Lack of independent registration book for malaria, Lack of commitment and attention from health professionals to cross check registration book before reporting, Data filling by Estimation assuming as valueless were among some factors affecting Data accuracy as explained by key informants.
