Data Quality Management Past, Present, and Future: Towards a Management System for Data

2012 ◽  
pp. 15-40 ◽  
Author(s):  
Thomas C. Redman
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Management System ◽  
Data Quality Management

Data quality management system (DQMS) for BAC detector in the ZEUS experiment at the HERA accelerator

2003 ◽  
Author(s):  
Zbigniew Luszczak ◽  
Tomasz Jezynski ◽  
Ryszard S. Romaniuk ◽  
Krzysztof T. Pozniak ◽  
Marcin Kuthan ◽  
...  
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Management System ◽  
Quality Management System ◽  
Data Quality Management

Foundations of Data Quality Management

2012 ◽  
Vol 4 (5) ◽  
pp. 1-217 ◽  
Author(s):  
Wenfei Fan ◽  
Floris Geerts
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Data Quality Management

The Integration of Fault Detection within Plant-Wide Data Quality Management

1994 ◽  
Vol 27 (5) ◽  
pp. 635-640
Author(s):  
M.P. Henry
Keyword(s):  
Quality Management ◽  
Fault Detection ◽  
Data Quality ◽  
Data Quality Management

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

2018 ◽  
Author(s):  
Suranga C. H. Geekiyanage ◽  
Dan Sui ◽  
Bernt S. Aadnoy
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Control System ◽  
Data Analysis ◽  
Quality Management ◽  
Data Quality ◽  
Incomplete Data ◽  
Laboratory Scale ◽  
Data Quality Management ◽  
Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

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