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JITM-Data Quality 2018 : Journal of Information Technology Management- Special Issue on Data Quality

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Link: https://jitm.ut.ac.ir/contacts?_action=loginForm
 
When Aug 1, 2018 - Nov 15, 2018
Where Tehran
Submission Deadline Nov 15, 2018
Categories    information technology managem   data quality   data value and data governan   master data management
 

Call For Papers

The Journal of Information Technology Management (JITM) invites researchers and scholars to contribute to their Special Issue on Data Quality.

There is a full Call for Papers at below and also (Call for Paper link at Website) which includes a description of what is required, dates and submission requirements.
JITM is accredited by several organisations and as also index by SCOPUS.
If you are interested in submitting a manuscript for review you may submit it at https://jitm.ut.ac.ir/contacts?_action=loginForm

If this announcement is not of interest to you personally we would be obliged if you would be kind enough to pass it on to any colleagues or associates who might be interested.

About Journal:
Journal of Information Technology Management (JITM) (ISSN: 2423-5059) is a peer-reviewed quarterly journal devoted to the field of IT Management with the aim of developing administration knowledge, identifying the management problems in organizations and presenting the solutions. The submitted papers will be published after special review as well as the approval of the editorial board. Journal was established by the faculty of Management, University of Tehran in 2009. The honorable professors and researchers are highly appreciated if they visit this site, register, submit and set up their papers based on authors guidelines. Therefore, visiting in person or calling the journal office are not recommended, so all connections with authors and honorable reviewers are done through the website.
Aim and Scopes:
For many years researchers and practitioners have discussed and addressed data and information quality challenges, and there is no doubt that the quality of information and data is a crucial aspect of data management. Over the last decades the field has made substantial contributions, providing a range of frameworks, tools, methodologies, measurement and management approaches as well as provided a theoretical foundation. In addition, with the recent technological advances in particular advances in Analytics, Big Data and Intent of Things, the significance of data and information quality has been highlighted in many publications. As the research domain has matured, the research field became increasingly interdisciplinary on the intersection between management and technology.
However, how to manage information and data quality is still challenging and many open research questions remain. One of the key challenges and subject of continuous debate focuses on how to achieve, measure, increase and manage information quality and the value it creates. It has been concern to researchers and management for many years. With this special issue we aim to revisit and build on the foundation of Information and Data Quality Management, with its aim to continuously providing the right information to the right people in order to improve decision-making. In the tradition of the seminal work from Richard Wang (1998) related to total data quality management and the many subsequent contributions, aim is to examine how to deliver high-quality information products in modern data rich environments.
With this special issue we aim to present cutting edge research related to information and data quality. We are interested in how information and data quality provide theory, guidelines, methods and techniques and tools to manage and improve its quality in enterprises. Submission may provide theoretical, conceptual or empirical contributions, and may study various aspects of its implication in a range of enterprises, both in the private and public sector. Original and unpublished high-quality research results are solicited to explore various challenging topics related to information and data quality management which include, but are not limited to:
• Models, Methods, Concepts, and Tools
• Related Case Studies
• Theoretical Foundations
• Review Articles
• Data Value and Data Governance
• Master Data Management
• Data and Information Quality Assessment
• Optimising Information Value
• Information Lifecycle Concepts
• Modelling and Visualising Information Manufacturing Systems
• Information Risk Management
• Data Integration and Data Quality
• Data Analytical and Data Quality


Editor in Chief: Prof. Babak Sohrabi, University of Tehran, Tehran, Iran.
Guest Editor: Prof. Marks Helfert, School of Computing - Dublin City, University Dublin, Ireland.

Publication date: 11/25/2018
End of Submission date: 11/15/2018
End of peer review date: 11/20/2018

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