Data Governance and Data Management

2021 ◽  
pp. 145-159
Author(s):  
Scott Burk ◽  
David E. Sweenor ◽  
Gary Miner
Keyword(s):  
Data Management ◽  
Data Governance

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.

scholarly journals Data management aspects of public engagement with biodiversity documentation

2015 ◽  
Author(s):  
Alvaro Ortiz-Troncoso
Keyword(s):  
Quality Control ◽  
Text Mining ◽  
Environmental Monitoring ◽  
Data Management ◽  
Public Engagement ◽  
General Public ◽  
Museum Specimens ◽  
Data Governance ◽  
Technological Developments ◽  
Environmental Monitoring Data

Technological developments open up new opportunities for collaboration between biodiversity researchers and the general public. Three exemplary case studies were reviewed from literature: digitizing museum specimens, text-mining archived expedition journals and handling environmental monitoring data. Data management principles were applied to refine the ensuing requirements. Specific requirements were found to exist in three areas: collecting data, sharing data and improving data quality. Implications for data governance and quality control are discussed.

scholarly journals Leveraging best practices in data governance: An organization-wide data inventory and mapping project to support a five year data strategy

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Joseph Travers ◽  
Crystal Campitelli ◽  
Richard Light ◽  
Eric De Sa ◽  
Julie Stabile ◽  
...  
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Quality Data ◽  
Free Text ◽  
Data Mapping ◽  
Data Governance ◽  
Data Set ◽  
Staff Members ◽  
Workflow System ◽  
Data Elements

IntroductionThe professional regulation sector is moving toward risk-informed approaches that require high quality data. A key component of a corporate 2017 Data Strategy is the implementation of a data inventory and mapping project to catalogue, centralize, document and govern data assets that support regulatory decisions, programs and operations. Objectives and ApproachIn a data rich organization, the goals of the data inventory are to: enhance authoritative data that support programs; identify data duplications/gaps; identify data sources, owners and users; and, apply consistent data management and standards organizationally. Routinely used data assets outside the large enterprise workflow system (excel/word files; databases; paper collections) were catalogued. Using data governance principles and a facilitated questionnaire, departmental data stewards were interviewed about their generated data. Questions included data purpose/sources/types/formats/owners, retention rates, analytical products, gaps and visions for a desired data state. A data mapping methodology highlighted data set and variable connections within and across departments. ResultsTo date, over 40 staff members in 10 departments were identified as data content experts. In addition to data in the corporate enterprise system, over 80 unique datasets were identified. In 1 large department, over 2,000 data elements across 26 datasets were inventoried. Data mapping analysis revealed thematic data domains, including member demographics, outcomes, certifications, tracking and financial data, collected and held in multiple formats ((Microsoft Access, Excel, Word), SPSS, PDF, e-mails and paper documents). While 72% of the data elements were formatted numerically, approximately 8% were free text. Significant data redundancies across staff members and departments were revealed, as well as unstandardized variable naming conventions. Gaps analysis highlighted need for standardized, electronic data, where not available and data management training. Conclusion/ImplicationsCustomized data mapping reports to data users will facilitate the development of local, standardized departmental data hubs that will centrally link to a centralized data repository to facilitate seamless organization-wide analytics, improvements in current data management practices and greater data collaboration with the ultimate goal of supporting risk-informed approaches.

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