In praise of Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance

2014 ◽  
pp. vii
Keyword(s):  
Data Management ◽  
Data Governance ◽  
Data Stewardship

scholarly journals Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

2021 ◽  
Author(s):  
Arianna Dagliati ◽  
Alberto Malovini ◽  
Valentina Tibollo ◽  
Riccardo Bellazzi
Keyword(s):  
Health Informatics ◽  
Data Privacy ◽  
Lessons Learned ◽  
Data Governance ◽  
Data Interoperability ◽  
Data Stewardship ◽  
Data Infrastructures ◽  
Healthcare Processes ◽  
Day By Day ◽  
Open Issues

Abstract The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

scholarly journals Data Stewardship – addressing disciplinary data management needs

2018 ◽  
Author(s):  
Marta Teperek ◽  
Maria J. Cruz ◽  
Ellen Verbakel ◽  
Jasmin K. Böhmer ◽  
Alastair Dunning
Keyword(s):  
Data Management ◽  
Common Denominator ◽  
Management Support ◽  
Research Institutions ◽  
Multidisciplinary Research ◽  
University Of Technology ◽  
Address Data ◽  
Subject Specific ◽  
Data Stewardship ◽  
The Common

One of the biggest challenges for multidisciplinary research institutions which provide data management support to researchers is addressing disciplinary differences1. Centralised services need to be general enough to cater for all the different flavours of research conducted in an institution. At the same time, focusing on the common denominator means that subject-specific differences and needs may not be effectively addressed. In 2017, Delft University of Technology (TU Delft) embarked on an ambitious Data Stewardship project, aiming to comprehensively address data management needs across a multi-disciplinary campus. In this practice paper, we describe the principles behind the Data Stewardship project at TU Delft, the progress so far, we identify the key challenges and explain our plans for the future.

Coordinating Enterprise Services and Data

2013 ◽  
pp. 241-274
Author(s):  
Keith R. Worfolk
Keyword(s):  
Data Management ◽  
Portfolio Management ◽  
Best Practice ◽  
Maturity Model ◽  
Data Governance ◽  
Enterprise Information ◽  
Capability Maturity ◽  
Architecture Framework ◽  
Master Data Management ◽  
Enterprise Soa

The critical inter-dependencies between Enterprise Services and Enterprise Data are often not given due consideration. With the advent of Cloud Computing, it is becoming increasingly important for organizations to understand the relationships between them, in order to formulate strategies to jointly manage and coordinate enterprise services and data to improve business value and reduce risk to the enterprise. Enterprise Services encompass Service-driven applications deployed on-premises in the enterprise data centers as well as in the Cloud for the “extended enterprise.” Enterprise Data Management encompasses the cross-application enterprise-level perspective of data in an information-sharing enterprise, and the critical business data that is created, maintained, enriched, and shared outside the traditional enterprise firewall. This chapter discusses and proposes best practice strategies for coordinating the enterprise SOA & EDM approaches for mutual success. Primary coordination aspects discussed include: Service & Data Governance, Master Data Management, Service-driven & EDM Architecture Roadmaps, Service Portfolio Management, Enterprise Information Architecture, and the Enterprise Data Model. It recommends a facilitative Service-driven Data Architecture Framework & Capability Maturity Model to help enterprises evaluate and optimize overall effectiveness of their coordinated Service-driven & EDM strategies.

Challenges of Data Management in Always-On Enterprise Information Systems

2010 ◽  
pp. 443-462
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

Challenges of Data Management in Always-On Enterprise Information Systems

2011 ◽  
pp. 1695-1714 ◽  
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

scholarly journals Big data and analytics in tourism and hospitality: opportunities and risks

2020 ◽  
Vol 6 (3) ◽  
pp. 257-262 ◽  
Author(s):  
Anca Yallop ◽  
Hugues Seraphin
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Governance ◽  
Privacy And Security ◽  
Content Type ◽  
Data Ethics ◽  
Technological Advances ◽  
International Tourist ◽  
Tourism And Hospitality Industry ◽  
Tourism And Hospitality

Purpose The purpose of this paper is to examine and provide insights into one of the most influential technologies impacting the tourism and hospitality industry over the next five years, i.e. big data and analytics. It reflects on both opportunities and risks that such technological advances create for both consumers and tourism organisations, highlighting the importance of data governance and processes for effective and ethical data management in both tourism and hospitality. Design/methodology/approach This paper is based on a review of academic and industry literature and access to trends data and information from a series of academic and industry databases and reports to examine how big data and analytics shape the future of the industry and the associated risks and opportunities. Findings This paper identifies and examines key opportunities and risks posed by the rising technological trend of big data and analytics in tourism and hospitality. While big data is generally regarded as beneficial to tourism and hospitality organisations, there are extensively held ethical, privacy and security concerns about it. Therefore, the paper is making the case for more research on data governance and data ethics in tourism and hospitality and posits that to successfully use data for competitive advantage, tourism and hospitality organisations need to solely expand compliance-based data governance frameworks to frameworks that include more effective privacy and ethics data solutions. Originality/value This paper provides useful insights into the use of big data and analytics for both researchers and practitioners and offers new perspectives on the debate on data governance and ethical data management in both tourism and hospitality. Because forecasts from the UNWTO indicate a significant increase in international tourist arrivals (1.8 billion tourist arrivals by 2030), the ways tourism and hospitality organisations manage customers’ data become important.

scholarly journals Data Governance Maturity Level at the National Archives of the Republic of Indonesia

2020 ◽  
Vol 10 (1) ◽  
pp. 27-40
Author(s):  
Sari Agustin Wulandari
Keyword(s):  
Data Management ◽  
Good Governance ◽  
Maturity Level ◽  
Data Governance ◽  
Management Planning ◽  
Governance Model ◽  
Master Data ◽  
National Archives ◽  
Grand Design ◽  
The Republic

The National Archives of the Republic of Indonesia (ANRI) as an institution given mandate to carry out state duty in the field of archives has vision as a pillar of good governance and nation’s collective memory. To implement it, the study of the grand design of the archival system arranged. That is very related to the data governance implementation. Therefore, ANRI needs to know the maturity level of the data governance function which had been held. The assessment was done by referring to the Stanford Data Governance Model. The result showed that data governance is still at an initial level. The foundational aspects are on an average of 1,2 which contains awareness, formalization, and metadata. While on project aspects are on average of 1,5 consisting of stewardship, data quality, and master data. In total, ANRI is at the level of 1,35. ANRI needs to make improvements for data management planning activities referring to Data Management Body of Knowledge (DMBOK) with a focus on people, policies, and capabilities dimensions in all aspects. This research is expected to be helpful for ANRI to make improvements corresponding to the recommendations thus ANRI could implement national data archival properly.

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