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An Introduction to Business Intelligence
Published in Deepmala Singh, Anurag Singh, Amizan Omar, S.B. Goyal, Business Intelligence and Human Resource Management, 2023
In the growing era of BI, data governance will become a priority for small and big organizations. Data governance allows the management of data in such a manner that complete, reliable, and secure data are delivered. A comprehensive data governance strategy helps in improving return on investment from the investment made in BI software. It also aids in maintaining a sound balance between the consistency and transparency of data. Data governance will help in getting access to accurate data that will help in making the right decision. It will also ensure the privacy and confidentiality of the data procured and will also prevent its use by unauthorized users.
Business Intelligence, Big Data and Data Governance
Published in Pedro Novo Melo, Carolina Machado, Business Intelligence and Analytics in Small and Medium Enterprises, 2019
Hélder Quintela, Davide Carneiro, Luís Ferreira
Data Governance is mandatory for a successful organization to achieve master data management, improve data quality, build BI [52], being compliant with regulatory policies, ensuring that data is of high quality, it is usable, it has integrity across all the systems of the Enterprise Information System, it is protecting the privacy of the data owners, and that it is secure. Data Governance can be defined as an organizational approach to data and information management that formalizes a set of policies and procedures to guide the full life cycle of data, from collection to visualization.
Manage Your Data Estate Like Your Finances
Published in Tom Lawry, AI in Health, 2020
Data governance is a set of processes that ensure that data assets are formally managed throughout the enterprise so that data can be trusted and that people can be made accountable for adverse events that happen because of low data quality. It’s the management of the availability, usability, integrity, and security of your organization’s data assets.7 The key focus areas of data governance include availability, usability, consistency, data integrity, and security.
Data Governance Model To Enhance Data Quality In Financial Institutions
Published in Information Systems Management, 2023
According to the Data Governance Institute (DGI, 2015), data governance is defined as a system that is executed according to agreed activities and methods and incorporates decision rights and accountabilities for data-related processes. Data governance ensures the allocation of decision-making rights and accountability for decision-making about data within the organization (Khatri & Brown, 2010; Lillie & Eybers, 2019). Data governance provides a control mechanism, such as processes, policies, organizational structures, and roles, which support decision-making about data and proper usage of data (Lee et al., 2018). According to the DAMA (2017), Data governance is comprised of practices that govern processes of planning, designing, acquisition, usage, and retrieval of data in an organization. Data governance helps to meet regulatory compliance requirements as defined in the standard BCBS 239 Principles for effective risk data aggregation and risk reporting published by the Basel Committee on Banking Supervision (Orgeldinger, 2018).
Critical Success Factors for Data Governance: A Theory Building Approach
Published in Information Systems Management, 2019
Ibrahim Alhassan, David Sammon, Mary Daly
Data governance has received much attention in both academic and practitioner communities. The concept has been developed over the last ten years whereby data are considered as valuable assets and as a strategic function within the organization’s structure and are thus placed under corporate governance (Vayghan et al., 2007; Wende, 2007). Data governance focuses on who holds the decision rights related to data assets in an organization (Khatri & Brown, 2010; Otto, 2011) in order to ensure the quality, consistency, usability, security, privacy, and availability of the data (Cohen, 2006; Panian, 2010).