A Data Governance Framework - The Foundation for Data Management Excellence

2019 ◽  
Author(s):  
Mohamed Akoum ◽  
Hayfa Bu Hazzaa
Keyword(s):  
Data Management ◽  
Data Governance ◽  
Governance Framework

scholarly journals Analysis and Design of Data Governance at the Financial Services Authority

InFestasi ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. InPres
Author(s):  
Desta Arisandi ◽  
Tubagus Muhamad Yusuf Khudri
Keyword(s):  
Data Management ◽  
Financial Services ◽  
Data Governance ◽  
Governance Framework ◽  
Services Sector ◽  
Analysis And Design ◽  
Management Association ◽  
Tools And Techniques ◽  
Management Capabilities

This research is a case study conducted at the Financial Services Authority on the implementation of a data governance framework based on the model from The Data Management Association in 2017. The purpose of this study is to produce a data governance framework in managing integrated Financial Services Sector data. This study uses a qualitative approach in describing data governance activities. The research instruments were interviews, questionnaires, and content analysis. The results show that data governance frameworks provide guidelines for various parties to act in accordance with the strategies that have been developed. Data governance program at the Financial Services Authority requires further improvements in the form of establishing a data governance charter, assessing the maturity level of data management capabilities, defining the operational framework, adjusting the roadmap, establishing a change management team, creating mechanisms and procedures for handling data problems, and developing tools and techniques which supports the entire data governance program.

scholarly journals Erfolgsfaktor Data Governance

2021 ◽  
Vol 2021 (3) ◽  
pp. 24-26
Author(s):  
Christiana Klingenberg ◽  
◽  
Kristin Weber
Keyword(s):  
Data Management ◽  
Data Governance ◽  
Governance Framework ◽  
Master Data ◽  
Master Data Management

Von Master Data Management (MDM) versprechen sich Unternehmen Effizienz, Transparenz und Risikominimierung im Umgang mit ihren Stammdaten. MDM soll dazu beitragen, Stammdaten als „Asset“ im Unternehmen zu bewirtschaften. Der vorliegende Beitrag liefert praktische Tipps, wie MDM-Implementierungen nachhaltig gestaltet werden können, damit die Daten einen Beitrag zum Unternehmenserfolg leisten. Er stellt das qualitätsorientierte Data Governance Framework vor. Das Framework stellt sicher, dass bei einer Implementierung alle Aspekte von MDM adressiert werden inkl. strategischer und organisatorischer Fragestellungen. Die konsequente Ausrichtung an der Datenqualität sorgt dafür, dass alle Unternehmensbereiche Stammdaten nutzenstiftend einsetzen können.

scholarly journals Caracterización de marcos de referencia que apoyan la implementación del gobierno de datos propuesto por MinTIC para entidades públicas

2021 ◽  
Vol 9 (2) ◽  
pp. 42-58
Author(s):  
Mauricio Garcés Ordóñez ◽  
Mayda Patricia González Zabala
Keyword(s):  
Data Management ◽  
Open Group ◽  
Data Governance ◽  
Governance Framework ◽  
Body Of Knowledge ◽  
Architecture Framework ◽  
Management Association

El gobierno de datos (GD) asegura que los datos cumplan con las expectativas del negocio, reduzca costos de gestión, protección y desarrolle los datos como un activo estratégico y de valor. En Colombia, el Ministerio de Tecnologías de la Información y las Comunicaciones (MinTIC) ha entregado una herramienta conceptual para que las entidades públicas puedan adoptar la Arquitectura TI Colombia, definiendo lineamientos y guías para facilitar su entendimiento y aplicación. Este artículo tiene como Objetivo: Caracterizar el alcance de los marcos de referencia para facilitar la implementación del GD establecido por MinTIC para las entidades públicas. Metodología: El proceso consistió en: 1) identificar marcos de referencia, 2) caracterizar relación de marcos de referencia con lineamientos y elementos, 3) identificar alineación de los ámbitos y aportes de los marcos de gestión y GD para la implementación del GD, y 4) analizar los resultados de la investigación. Resultados: El proceso permitió identificar los elementos que cada marco de referencia aporta a la implementación del GD de acuerdo a los lineamientos establecidos por el MinTIC. Conclusiones: El Data Management Body of Knowledge del Data Management Association (DAMA) entrega herramientas y técnicas que facilitan la implementación de lo estipulado por el ministerio, el cual puede complementarse con elementos del Data Governance Framework del Data Governance Institute (DGI) y The Open Group Architecture Framework (TOGAF).

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.

Das qualitätsorientierte Data Governance Framework

2020 ◽  
pp. 35-67
Author(s):  
Kristin Weber ◽  
Christiana Klingenberg
Keyword(s):  
Data Governance ◽  
Governance Framework

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.

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