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

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.

scholarly journals Application of Data Integrity Algorithm for Geotechnical Data Quality Management

2018 ◽  
Vol 12 (8) ◽  
pp. 85 ◽  
Author(s):  
Tarik Chafiq ◽  
Mohammed Ouadoud ◽  
Hassane Jarar Oulidi ◽  
Ahmed Fekri
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Processing Time ◽  
Research Work ◽  
Data Integrity ◽  
Data Quality Management ◽  
Sequential Approach ◽  
Geotechnical Data ◽  
Geotechnical Database ◽  
Manual Verification

The aim of this research work is to ensure the integrity and correction of the geotechnical database which contains anomalies. These anomalies occurred mainly in the phase of inputting and/or transferring of data. The algorithm created in the framework of this paper was tested on a dataset of 70 core drillings. In fact, it is based on a multi-criteria analysis qualifying the geotechnical data integrity using the sequential approach. The implementation of this algorithm has given a relevant set of values in terms of output; which will minimalize processing time and manual verification. The application of the methodology used in this paper could be useful to define the type of foundation adapted to the nature of the subsoil, and thus, foresee the adequate budget.

Scene-Based Big Data Quality Management Framework

2018 ◽  
pp. 122-139
Author(s):  
Xinhua Dong ◽  
Heng He ◽  
Chao Li ◽  
Yongchuan Liu ◽  
Houbo Xiong
Keyword(s):  
Big Data ◽  
Quality Management ◽  
Data Quality ◽  
Data Quality Management ◽  
Management Framework

scholarly journals 2. National Clinical Database: Its Use and Data Quality Management Efforts

2016 ◽  
Vol 21 (1) ◽  
pp. 27-35
Author(s):  
Hiraku KUMAMARU ◽  
Arata TAKAHASHI ◽  
Eriko FUKUCHI ◽  
Naoaki ICHIHARA ◽  
Norimichi HIRAHARA ◽  
...  
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Clinical Database ◽  
Data Quality Management ◽  
National Clinical Database

Educational data quality management: lessons learned from a public university in Indonesia

2021 ◽  
Vol 30 (2) ◽  
pp. 201
Author(s):  
Syukron Ma' ◽  
N.A. mun ◽  
Achmad Nizar Hidayanto ◽  
Ayed Said Alwadain ◽  
Dedi Iskandar Inan ◽  
...  
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Public University ◽  
Lessons Learned ◽  
Data Quality Management
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