scholarly journals Manajemen Kualitas Data dan Informasi Berbantuan Sistem Informasi untuk Meningkatkan Kinerja Operasional Pabrik Pengolahan Kelapa Wawit

2017 ◽  
Vol 7 (2) ◽  
pp. 88
Author(s):  
Ahmad Fahmi Karami
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Organizational Performance ◽  
Information Management ◽  
Information Quality ◽  
Document Management ◽  
Quality Data ◽  
Strategic Decisions ◽  
Quality Of Data ◽  
Using Data

Organizational performance depends on strategic decisions taken by stakeholders in the organization, where strategic decisions of stakeholders depend on the quality of data and information available to the organization. Data and information quality called good when the data and quality has criteria that suits for users of data and information, where data and information user need on the organization will be different according to their aim and objectives, so that the criteria of data quality and information is not universal. The research aims to improve the quality management of data and information by utilizing information systems to produce good quality data and information and help improve the organization's performance on the Palm Oil Processing Factory in Indonesia. This research was conducted to know data and information quality management in producing data and information, and its contribution on the mill performance using interview methods with those who have a role in the implementation of data quality and information management, observation, and document management related to factory performance. This research resulted findings that still in the implementation of data quality and information management there are still procedures that are not undertaken, so the result of data and information not entirely suits with the user wishes. Although the procedure has not been fully implemented, using data and information production has helped data and information users in decision making and succeeded in lowering the mill breakdown by 0.10%.

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.

An Assessment of the Quality of Data From the EPI for Knowledge Management in Healthcare in Ghana

2021 ◽  
pp. 195-216
Author(s):  
Patrick Ohemeng Gyaase ◽  
Joseph Tei Boye-Doe ◽  
Christiana Okantey
Keyword(s):  
Knowledge Management ◽  
Data Quality ◽  
Information Management ◽  
Information Management System ◽  
Quality Data ◽  
Quality Of Data ◽  
District Health ◽  
Self Assessment ◽  
Immunization Programme

Quality data from the Expanded Immunization Programme (EPI), which is pivotal in reducing infant mortalities globally, is critical for knowledge management on the EPI. This chapter assesses the quality of data from the EPI for the six childhood killer diseases from the EPI tally books, monthly reports, and the District Health Information Management System (DHIMS II) using the Data Quality Self-Assessment (DQS) tool of WHO. The study found high availability and completeness of data in the EPI tally books and the monthly EPI reports. The accuracy and currency of data on all antigens from EPI tally books compared to reported number issued were comparatively low. The composite quality index of the data from the EPI is thus low, an indication poor supervision of the EPI programme in the health facilities. There is therefore, the need for effective monitoring and data validation at the point of collection and entry to improve the data quality for knowledge management on the EPI programme.

Evaluating the Data Quality of a National Sample of Young Sexual and Gender Minorities Recruited Using Social Media: The Influence of Different Design Formats

2020 ◽  
pp. 089443932092824 ◽  
Author(s):  
Michael J. Stern ◽  
Erin Fordyce ◽  
Rachel Carpenter ◽  
Melissa Heim Viox ◽  
Stuart Michaels ◽  
...  
Keyword(s):  
Social Media ◽  
Data Quality ◽  
National Sample ◽  
Quality Data ◽  
Quality Of Data ◽  
Gender Minorities ◽  
Population Recruitment ◽  
Youth Population ◽  
And Gender

Social media recruitment is no longer an uncharted avenue for survey research. The results thus far provide evidence of an engaging means of recruiting hard-to-reach populations. Questions remain, however, regarding whether the data collected using this method of recruitment produce quality data. This article assesses one aspect that may influence the quality of data gathered through nonprobability sampling using social media advertisements for a hard-to-reach sexual and gender minority youth population: recruitment design formats. The data come from the Survey of Today’s Adolescent Relationships and Transitions, which used a variety of forms of advertisements as survey recruitment tools on Facebook, Instagram, and Snapchat. Results demonstrate that design decisions such as the format of the advertisement (e.g., video or static) and the use of eligibility language on the advertisements impact the quality of the data as measured by break-off rates and the use of nonsubstantive responses. Additionally, the type of device used affected the measures of data quality.

scholarly journals Creating Informative Data Warehouses: Exploring Data and Information Quality through Data Mining

10.28945/2584 ◽  
2002 ◽  
Author(s):  
Herna L. Viktor ◽  
Wayne Motha
Keyword(s):  
Data Mining ◽  
Information Quality ◽  
Data Warehousing ◽  
Poor Quality ◽  
Quality Data ◽  
Data Warehouses ◽  
Quality Of Data ◽  
Poor Quality Data ◽  
Daunting Task

Increasingly, large organizations are engaging in data warehousing projects in order to achieve a competitive advantage through the exploration of the information as contained therein. It is therefore paramount to ensure that the data warehouse includes high quality data. However, practitioners agree that the improvement of the quality of data in an organization is a daunting task. This is especially evident in data warehousing projects, which are often initiated “after the fact”. The slightest suspicion of poor quality data often hinders managers from reaching decisions, when they waste hours in discussions to determine what portion of the data should be trusted. Augmenting data warehousing with data mining methods offers a mechanism to explore these vast repositories, enabling decision makers to assess the quality of their data and to unlock a wealth of new knowledge. These methods can be effectively used with inconsistent, noisy and incomplete data that are commonplace in data warehouses.

Depicting Data Quality Issues in Business Intelligence Environment through a Metadata Framework

2016 ◽  
Vol 7 (2) ◽  
pp. 20-31
Author(s):  
Te-Wei Wang ◽  
Yuriy Verbitskiy ◽  
William Yeoh
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Business Intelligence ◽  
Quality Data ◽  
Data Quality Management ◽  
Metadata Model ◽  
Modern Business ◽  
Quality Framework ◽  
Promising Solution ◽  
Quality Dimensions

Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize their analysis by proposing a BI data quality framework.

Depicting Data Quality Issues in Business Intelligence Environment Through a Metadata Framework

2019 ◽  
pp. 291-304
Author(s):  
Te-Wei Wang ◽  
Yuriy Verbitskiy ◽  
William Yeoh
Keyword(s):  
Quality Management ◽  
Data Quality ◽  
Business Intelligence ◽  
Quality Data ◽  
Data Quality Management ◽  
Metadata Model ◽  
Modern Business ◽  
Quality Framework ◽  
Promising Solution ◽  
Quality Dimensions

Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize the analysis by proposing a BI data quality framework.

Evaluating the Quality and Usefulness of Data Breach Information Systems

2013 ◽  
pp. 225-240
Author(s):  
Benjamin Ngugi ◽  
Jafar Mana ◽  
Lydia Segal
Keyword(s):  
Information Systems ◽  
Data Quality ◽  
Quality Data ◽  
Quality Of Data ◽  
Law Enforcement Agencies ◽  
Data Breach ◽  
Security Breaches ◽  
Key Stakeholders ◽  
The U.S

As the nation confronts a growing tide of security breaches, the importance of having quality data breach information systems becomes paramount. Yet too little attention is paid to evaluating these systems. This article draws on data quality scholarship to develop a yardstick that assesses the quality of data breach notification systems in the U.S. at both the state and national levels from the perspective of key stakeholders, who include law enforcement agencies, consumers, shareholders, investors, researchers, and businesses that sell security products. Findings reveal major shortcomings that reduce the value of data breach information to these stakeholders. The study concludes with detailed recommendations for reform.

scholarly journals Decision making in the context of business intelligence and data quality

2009 ◽  
Vol 11 (2) ◽  
Author(s):  
L. Marshall ◽  
R. De la Harpe
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Business Intelligence ◽  
Information Quality ◽  
Poor Quality ◽  
Quality Of Data ◽  
Literature Study ◽  
Business Confidence ◽  
The Right

[email protected] Making decisions in a business intelligence (BI) environment can become extremely challenging and sometimes even impossible if the data on which the decisions are based are of poor quality. It is only possible to utilise data effectively when it is accurate, up-to-date, complete and available when needed. The BI decision makers and users are in the best position to determine the quality of the data available to them. It is important to ask the right questions of them; therefore the issues of information quality in the BI environment were established through a literature study. Information-related problems may cause supplier relationships to deteriorate, reduce internal productivity and the business' confidence in IT. Ultimately it can have implications for an organisation's ability to perform and remain competitive. The purpose of this article is aimed at identifying the underlying factors that prevent information from being easily and effectively utilised and understanding how these factors can influence the decision-making process, particularly within a BI environment. An exploratory investigation was conducted at a large retail organisation in South Africa to collect empirical data from BI users through unstructured interviews. Some of the main findings indicate specific causes that impact the decisions of BI users, including accuracy, inconsistency, understandability and availability of information. Key performance measures that are directly impacted by the quality of data on decision-making include waste, availability, sales and supplier fulfilment. The time spent on investigating and resolving data quality issues has a major impact on productivity. The importance of documentation was highlighted as an important issue that requires further investigation. The initial results indicate the value of

scholarly journals Calidad de Datos en Sistemas de Gestión Académica Universitaria basado en ISO/IEC 25012

Interfases ◽  
2021 ◽  
Author(s):  
Marleny Peralta Ascue Peralta Ascue
Keyword(s):  
Data Quality ◽  
Management System ◽  
Evaluation Model ◽  
Quality Of Data ◽  
Proposed Model ◽  
Academic Management ◽  
Main Input ◽  
Using Data ◽  
Primary Basis ◽  
Exploration Exploitation

This article proposes a data quality evaluation model developed on the primary basis of the ISO / IEC 25012 standard, applied to a University Academic Management System, to improve data quality. The proposed model is developed from the perspective of the data consumer and the vision of inherent data quality. The sample consisted of the data stored in the Academic Management System of the Universidad Nacional Micaela Bastidas, Apurímac, Perú, with 22 tables, 154 attributes, and 319,685 records. The model begins with data quality requirements as the main input for its evaluation and ends with an improvement plan, which is automatically implemented using data cleaning tools and SQL code. The characteristics that affect data quality problems are accuracy, consistency, compliance, and timeliness. Finally, it is concluded that it is possible to improve the quality of data by applying the proposed model, which can be used to create and generate value through the exploration, exploitation, and analysis of data for the benefit of university academic

Data Quality Assessment

2009 ◽  
pp. 378-384 ◽  
Author(s):  
Juliusz L. Kulikowski
Keyword(s):  
Information Processing ◽  
Data Quality ◽  
Quality Assessment ◽  
Quality Data ◽  
Quality Of Data ◽  
High Quality ◽  
Data Quality Assessment ◽  
Systems Effectiveness ◽  
And Control ◽  
Education Science

For many years the fact that for a high information processing systems’ effectiveness high quality of data is not less important than high systems’ technological performance was not widely understood and accepted. The way to understanding the complexity of data quality notion was also long, as it will be shown below. However, a progress in modern information processing systems development is not possible without improvement of data quality assessment and control methods. Data quality is closely connected both with data form and value of information carried by the data. High-quality data can be understood as data having an appropriate form and containing valuable information. Therefore, at least two aspects of data are reflected in this notion: 1st - technical facility of data processing, and 2nd - usefulness of information supplied by the data in education, science, decision making, etc.

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