Using data quality measures in decision-making algorithms

IEEE Expert ◽  
1992 ◽  
Vol 7 (6) ◽  
pp. 63-72 ◽  
Author(s):  
R.A. Dillard
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Quality Measures ◽  
Using Data

scholarly journals Promoting Sustainable Data-Based Decision-Making in the Korean Educational Information Disclosure System

2020 ◽  
Vol 12 (17) ◽  
pp. 6762
Author(s):  
Young Hyeo Joo
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Information Disclosure ◽  
School Context ◽  
Data Use ◽  
Institutional Support ◽  
School Teachers ◽  
Using Data ◽  
Educational Information ◽  
Quality School

This study investigates the Korean Educational Information Disclosure System (KEIDS) and suggests sustainable development policies for KEIDS to improve school-level data-based decision-making (DBDM) from the educational administration’s perspective. It also raises the following questions: What are the barriers impeding effective data use by the KEIDS? How do school teachers, who are directly involved in using data, effectively prepare for DBDM using the KEIDS? How can the KEIDS be improved for DBDM concerning quality data, school context, and institutional support? To answer these questions, the study reviewed KEIDS-related documents and interviewed 24 school teachers through an interpretive case study approach while using a research framework of data quality, school contexts, and institutional support. Its results highlight important issues with the KEIDS and sustainable DBDM, in other words, teachers and administrators are not always conscious of the need for using data; the lack of data use understanding creates issues among principal leadership and teachers’ involvement and cooperation; the quality of the student data in the Schoolinfo system is questionable; and the central education authority focuses on simply disclosing student data rather than pursuing the goal of the KEIDS. The study suggests facilitating DBDM through the KEIDS in terms of data quality, school context, and institutional support.

scholarly journals Use of EHR-Based Pediatric Quality Measures: Views of Health System Leaders and Parents

2019 ◽  
Vol 35 (2) ◽  
pp. 177-185
Author(s):  
David M. Hartley ◽  
Susannah Jonas ◽  
Daniel Grossoehme ◽  
Amy Kelly ◽  
Cassandra Dodds ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health System ◽  
Health Care Quality ◽  
Quality Measures ◽  
Care Quality ◽  
Quality Data ◽  
High Confidence ◽  
System Leaders ◽  
Using Data

Measures of health care quality are produced from a variety of data sources, but often, physicians do not believe these measures reflect the quality of provided care. The aim was to assess the value to health system leaders (HSLs) and parents of benchmarking on health care quality measures using data mined from the electronic health record (EHR). Using in-context interviews with HSLs and parents, the authors investigated what new decisions and actions benchmarking using data mined from the EHR may enable and how benchmarking information should be presented to be most informative. Results demonstrate that although parents may have little experience using data on health care quality for decision making, they affirmed its potential value. HSLs expressed the need for high-confidence, validated metrics. They also perceived barriers to achieving meaningful metrics but recognized that mining data directly from the EHR could overcome those barriers. Parents and HSLs need high-confidence health care quality data to support decision making.

A Method for Developing Data Quality Measures and Metrics for Primary Health Care

2016 ◽  
Vol 5 (1) ◽  
pp. 28-30
Author(s):  
Justin St-Maurice ◽  
Catherine M. Burns
Keyword(s):  
Primary Care ◽  
Data Quality ◽  
Quality Measures ◽  
Care Organization ◽  
Secondary Use ◽  
Local Primary Care ◽  
Potential Applications ◽  
Using Data ◽  
Future Work

The secondary use of primary care data has many potential applications. By using data from a local primary care organization, a method for developing data quality measures and metrics in primary care is presented as a case study. The method that was created included an exploratory meeting with a subject matter expert, the creation of a first draft of measures with the information management team, the discussion of the proposed measures with a focus group and a final data quality report encompassing all the collected feedback. The method was used to create rules and formulae to measures timeliness, completeness, accuracy and usefulness in the primary care ecosystem. Future work will involve completing a detailed quantitative analysis of the data quality measures calculated with the proposed metrics. In the future these measures can be broken down and compared on a monthly, professional and users basis to provide insight into the behavior and nuance of data quality in primary care.

A Review of Failure Handling Mechanisms for Data Quality Measures

2012 ◽  
Author(s):  
Nurul A. Emran ◽  
Noraswaliza Abdullah ◽  
Nuzaimah Mustafa
Keyword(s):  
Data Quality ◽  
Quality Measures ◽  
Failure Handling

STRATEGI MAKSIMALISASI ANGGARAN BELANJA PADA BALAI PENELITIAN DAN PENGEMBANGAN LINGKUNGAN HIDUP DAN KEHUTANAN MANOKWARI

2016 ◽  
Vol 8 (2) ◽  
Author(s):  
Arif Hasan ◽  
Dedi Budiman Hakim ◽  
Irdika Mansur
Keyword(s):  
Decision Making ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Primary Data ◽  
Fiscal Year ◽  
The Third ◽  
Swot Matrix ◽  
Three Stages ◽  
Using Data

This study aims to analyze causes of the low uptake of the budget and formulate a strategy of maximizing the absorption of expenditure on Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari. Respondents involved are 20 people that consist of: treasury officials and holder output of activity. The data used were secondary data in the form of reports on budget realization (LRA) quarter I, II, III and IV of the fiscal year 2011 to 2015, and the primary data were in the form of interviews with the help of a questionnaire. While the analysis of the data used was descriptive analysis using data tabulation, and the analysis of the three stages strategy of the decision making used IFE and EFE matrix, SWOT matrix and QSPM matrix.The results showed that there are 19 factors causing low of budget absorption until the end of the third quarter, and there were 10 drafts of policy as a strategy for maximizing the absorption of the budget on Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari.ABSTRAKPenelitian ini bertujuan untuk menganalisis penyebab rendahnya penyerapan anggaran belanja dan merumuskan strategi maksimalisasi penyerapan anggaran belanja pada Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari. Responden yang terlibat adalah 20 orang yaitu pejabat perbendaharaan dan pemegang output kegiatan. Data yang digunakan adalah data sekunder berupa laporan realisasi anggaran (LRA) triwulan I, II, III dan IV tahun anggaran 2011 sampai 2015, dan data primer berupa wawancara dengan bantuan kuesioner. Sedangkan analisis data yang digunakan adalah analisis deskriptif menggunakan analisis tabulasi, dan analisis analisis strategi tiga tahap pengambilan keputusan menggunakan matriks IFE dan EFE, matriks SWOT dan matriks QSPM. Hasil penelitian menunjukkan bahwa terdapat 19 faktor penyebab rendahnya penyerapan anggaran belanja sampai akhir triwulan III, dan terdapat 10 rancangan kebijakan sebagai strategi maksimalisasi penyerapan anggaran belanja di Balai Penelitian dan Pengembangan Lingkungan Hidup dan Kehutanan Manokwari.

Influence of Confucian culture on entrepreneurial decision making using data from China's floating population

2020 ◽  
Vol 48 (7) ◽  
pp. 1-12
Author(s):  
Ran Xiong ◽  
Ping Wei
Keyword(s):  
Decision Making ◽  
Floating Population ◽  
Positive Role ◽  
Confucian Culture ◽  
Chinese Nation ◽  
Using Data ◽  
Entrepreneurial Decision ◽  
And Behavior ◽  
The Relationship

Confucian culture has had a deep-rooted influence on Chinese thinking and behavior for more than 2,000 years. With a manually created Confucian culture database and the 2017 China floating population survey, we used empirical analysis to test the relationship between Confucian culture and individual entrepreneurial choice using data obtained from China's floating population. After using the presence and number of Confucian schools and temples, and of chaste women as instrumental variables to counteract problems of endogeneity, we found that Confucian culture had a significant role in promoting individuals' entrepreneurial decision making among China's floating population. The results showed that, compared with those from areas of China not strongly influenced by Confucian culture, individuals from areas that are strongly influenced by Confucian culture were more likely to choose entrepreneurship as their occupation choice. Our findings reveal cultural factors that affect individual entrepreneurial behavior, and also illustrate the positive role of Confucianism as a representative of the typical cultures of the Chinese nation in the 21st century.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

Embracing School Counselors’ Situatedness: Data-Based Decision Making as Fulfillment of a Complex Identity

2021 ◽  
Vol 24 (1_part_3) ◽  
pp. 2156759X2110119
Author(s):  
Brett Zyromski ◽  
Catherine Griffith ◽  
Jihyeon Choi
Keyword(s):  
Decision Making ◽  
School Counselors ◽  
School Counselor ◽  
Use Of Data ◽  
Counselor Identity ◽  
Using Data ◽  
Definition Of ◽  
Support Students ◽  
Students Success ◽  
Data Based Decision Making

Since at least the 1930s, school counselors have used data to inform school counseling programming. However, the evolving complexity of school counselors’ identity calls for an updated understanding of the use of data. We offer an expanded definition of data-based decision making that reflects the purpose of using data in educational settings and an appreciation of the complexity of the school counselor identity. We discuss implications for applying the data-based decision-making process using a multifaceted school counselor identity lens to support students’ success.

scholarly journals Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

2021 ◽  
Vol 11 (2) ◽  
pp. 472
Author(s):  
Hyeongmin Cho ◽  
Sangkyun Lee
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Large Scale ◽  
High Dimensional Data ◽  
Quality Measures ◽  
Training Data ◽  
Measure Data ◽  
High Dimensional ◽  
Small Scale ◽  
Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

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