Management of inspection data quality of the Transport Agency's structures

2012 ◽  
pp. 2064-2069
Author(s):  
M Söderqvist
Keyword(s):  
Data Quality ◽  
Inspection Data

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

scholarly journals Response Behavior and Quality of Survey Data: Comparing Elderly Respondents in Institutions and Private Households

2021 ◽  
pp. 004912412199553
Author(s):  
Jan-Lucas Schanze
Keyword(s):  
Data Quality ◽  
The Elderly ◽  
Response Behavior ◽  
Quality Of Data ◽  
Social Surveys ◽  
Private Households ◽  
Confounding Variables ◽  
Health Related ◽  
Survey Interviews

An increasing age of respondents and cognitive impairment are usual suspects for increasing difficulties in survey interviews and a decreasing data quality. This is why survey researchers tend to label residents in retirement and nursing homes as hard-to-interview and exclude them from most social surveys. In this article, I examine to what extent this label is justified and whether quality of data collected among residents in institutions for the elderly really differs from data collected within private households. For this purpose, I analyze the response behavior and quality indicators in three waves of Survey of Health, Ageing and Retirement in Europe. To control for confounding variables, I use propensity score matching to identify respondents in private households who share similar characteristics with institutionalized residents. My results confirm that most indicators of response behavior and data quality are worse in institutions compared to private households. However, when controlling for sociodemographic and health-related variables, differences get very small. These results suggest the importance of health for the data quality irrespective of the housing situation.

scholarly journals Thinking about police data: Analysts’ perceptions of data quality in Canadian policing

2021 ◽  
pp. 0032258X2110214
Author(s):  
Christopher D O’Connor ◽  
John Ng ◽  
Dallas Hill ◽  
Tyler Frederick
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Data Quality ◽  
Research Culture ◽  
Police Services ◽  
Police Data ◽  
Data Collection And Analysis ◽  
Quality Issues

Policing is increasingly being shaped by data collection and analysis. However, we still know little about the quality of the data police services acquire and utilize. Drawing on a survey of analysts from across Canada, this article examines several data collection, analysis, and quality issues. We argue that as we move towards an era of big data policing it is imperative that police services pay more attention to the quality of the data they collect. We conclude by discussing the implications of ignoring data quality issues and the need to develop a more robust research culture in policing.

Research on the Validation Method of Product Data Quality

2014 ◽  
Vol 989-994 ◽  
pp. 1631-1634
Author(s):  
Ping Wang ◽  
Bin Wang
Keyword(s):  
Data Quality ◽  
High Efficiency ◽  
Engineering Analysis ◽  
Validation Process ◽  
Software Environment ◽  
Product Data ◽  
Validation Method ◽  
Source Data ◽  
Cnc Programming

Product data is the source data of product lifecycle in manufacturing enterprise. The quality of product data largely determines the effect of the application of Engineering analysis, simulation assembly and CNC programming work and so on. In order to solve the problems of the existing product data quality, such as validation custom trival, lack of high efficiency and flexibility, etc. The validation method of product data quality (PDQ) based on class was proposed in NX software environment, the representation of validation rules classes of product data quality, validation rules customization and implementation of validation process were introduced in detail in this study. Finally, an application case was employed to verify the practicability and effectiveness of the proposed method.

Inline Inspection: Both Effective Data Collection and Interpretation Needed to Achieve High Quality Reporting Results

2010 ◽  
Author(s):  
James R. Walker ◽  
Paul Mallaburn ◽  
Derek Balmer
Keyword(s):  
Financial Risk ◽  
Service Providers ◽  
Holistic Approach ◽  
Poor Quality ◽  
International Standards ◽  
Quality Reporting ◽  
Certification Standard ◽  
Consensus Standard ◽  
Inspection Data

Historically, pipeline operators have tended to place more weight on inline inspection tool specifications than on the inherent design and reporting capabilities of the service providers themselves. While internal collection of integrity data is very important, it’s imperative that vendors, also, have high levels of expertise and effective quality control systems in place to successfully analyze exceedingly high volumes of inspection data. The quality of inspection information is vital to assessing if a pipeline is fit for purpose now and/or into the future. Integrity managers attempting to reduce overall operating risk by making decisions based on inaccurate or poor quality reporting are in fact exposing their networks to greater safety and financial risk. Recognizing these risks and that inline inspection (ILI) is an overall system that needs to be formally qualified, operators and ILI service providers have collaborated to develop several international standards. The most recent is the umbrella API-1163 industry consensus standard, which is now being widely adopted, primarily in USA. This standard provides requirements and recommended practices for qualification of the entire ILI process. Two companion standards: ASNT In-line Personnel Qualification and Certification Standard No. ILI-PQ and NACE Recommended Practice In-Line Inspection of Pipelines RP0102 combine to address specific requirements for personnel who operate and analyze the results of ILI systems. In Europe, the Pipeline Operators Forum (POF) has, also, established specific requirements for ILI reporting processes and data formats. However, these standards do not define how operators and vendors must meet these requirements. To follow will be a story about how an ILI service provider embraced a holistic approach to address these standards’ requirements, in particular in the areas of data analysis, reporting, and dig verification due to their significant importance in assuring the final quality of its deliverables. A key outcome desired will be to provide operators with greater insight into what best practices and technologies ILI service providers should have embraced and invested in to insure reliable service delivery.

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