DAMA-DMBOK2 Framework

New Directions for …, 1987

Administrative use of the microcomputer has had a profound impact on college and university operations. In particular, administrative computing activities, once the sole responsibility of the central computing enterprise within an institution, are now widely dispersed throughout the organization. Powerful, easy-to-use desktop computers and user-friendly software have enabled professionals at virtually every operational level to participate in the collection, analysis, and reporting of data. As users request and acquire data, and become more proficient in manipulating those data, they assume a more active role in data-related operations. It is these expanding roles, new capabilities, and new expectations that have serious implications for effective data administration and management.

The data team intervention was designed to support schools' data use. The sustainability of schools’ data use was investigated by studying the schools' development of the ostensive and performative aspects of organizational routines for: engaging in the data team intervention, acting upon their data team’s improvement plan, and using data for school development and instruction. Six Dutch secondary schools participated in this longitudinal mixed-methods study. Data were collected through questionnaires, policy documents, and interviews. Results indicated that schools struggled to develop organizational routines for data use, especially the ostensive aspects. This illuminated the process by which schools did not sustain their use of data. The findings showed that interventions for data use should more clearly target the development of organizational routines.

In today's digital landscape, organizations face the challenge of managing vast amounts of data characterized by high volume, variety, and velocity. Without consistent data management practices, data becomes fragmented and unreliable, making it difficult to achieve high data quality and seamless integration. This paper addresses the role of Master Data Management (MDM) in overcoming these challenges, focusing on its impact on simplifying operations, enhancing strategic decision-making, and enabling scalability by showing the practical architecture of how MDM is created. This paper presents an approach to MDM implementation, showcasing practical architectures and real-world examples that bridge the gap between theoretical concepts and applied data management. The research introduces centralized data models and standardized quality control mechanisms, demonstrating how MDM serves as the backbone of modern data governance. The paper also explores the integration of MDM with Artificial Intelligence and Machine Learning for data quality optimization. The paper also examines the role of data stewards in supporting MDM initiatives. Findings indicate that well-implemented MDM strategies not only enhance operational efficiencies but also contribute to significant cost savings and improved regulatory compliance. By providing a roadmap for successful MDM implementation, this research offers valuable insight for data engineers, business leaders, and academics, contributing to the development of robust, scalable, and effective data management ecosystems in the ever-evolving digital era.

Main objectives of this joint paper are to prepare an analysis of the current situation at University of Bolton (UoB) Academic Centre Sri Lanka (ACSL) with regard to its data and information management techniques. Proposed new techniques, and show how best they can address the issues/problems identified in the said analysis and assess the benefits of implementing those new techniques at ACSL.

Computer, 2007

version (changing data into other forms, states, or products), or scrubbing (inspecting and manipulating, recoding, or rekeying data to prepare it for subsequent use). • Approximately two-thirds of organizational data managers have formal data management training; slightly more than two-thirds of organizations use or plan to apply formal metadata management techniques; and slightly fewer than one-half manage their metadata using computer-aided software engineering tools and repository technologies. 3 When combined with our personal observations, these results suggest that most organizations can benefit from the application of organization-wide data management practices. Failure to manage data as an enterprise-, corporate-, or organization-wide asset is costly in terms of market share, profit, strategic opportunity, stock price, and so on. To the extent that world-class organizations have shown that opportunities can be created through the effective use of data, investing in data as the only organizational asset that can't be depleted should be of great interest. Increasing data management practice maturity levels can positively impact the coordination of data flow among organizations, individuals, and systems. Results from a self-assessment provide a roadmap for improving organizational data management practices.

2018

Information Resources Management Journal, 1990

The importance of corporate data management cannot be overstated in today's volatile environment. Several recent studies of IS professionals have found computer-based data utilization to be one of the top five critical IS issues of the 1980s. A study was conducted with 394 end users in 21 Fortune 500 organizations. The results are alarming. Data pollution existed in every one of the firms we explored. Rekeying of corporate data was evident in half of the end users surveyed creating redundancy problems. Data management policies were not found to be widespread presenting a very real concern to practicing managers. Backup and security procedures were found to be loosely enforced on the average. Based on the findings, the authors recommend a contingency approach to managing corporate data. Recommendations are presented to IS managers which encourage a shift from computer management to data management.

Technological Forecasting and Social Change, 2021

Big Data is becoming ubiquitous-widely applied across organisations, industry sectors and society. However, the opportunities and risks it presents are not yet fully understood. In this paper we identify and explore the tensions that Big Data can create at multiple levels, focusing on the need for organisations to meet the challenges that can arise. We draw on insights from twelve papers published in the Special Issue of Technological Forecasting & Social Change entitled "Tension in the Data Environment: Can Organisations Meet the Challenge?" in order to build a 'Multi-Layer Tensions Model' that highlights key pressures and challenges in the BD environment. We find evidence of tensions of three types, which we summarise as "Organisational Learning", "Organisational Leadership" and "Societal" tensions. We contribute, first, by identifying and developing a nuanced understanding of the tensions faced in the Big Data environment; and second, by elaborating on the capabilities that can be developed and the actions taken to maximise the benefits of Big Data. We end with a "Learning, Leading, Linking" framework, which points to implications for practice and a future research agenda.

2023

Data management in public administration and large organizations requires policies and procedures that enable the proper use and preservation of digital archival documents. New ways of information affect the governance of citizens in modern democracies, and therefore the effective functioning of institutions and organizations in the digital environment is essential. Organizing, describing, clearing, and maintaining records is vital to effective data management. By examining recent developments in data management practices, this article offers insights and recommendations for organizations striving to optimize their data governance strategies. Effective data management in public administration and large organizations contributes to enhanced decision-making, transparency, accountability, and citizen engagement. The article underscores the importance of data governance frameworks, data classification, access and security policies, data quality assurance, and retention procedures through a comprehensive review and analysis of existing literature, regulations, and case studies related to data management in public administration and large organizations. The study draws insights from scholarly articles, government reports, and industry publications to understand current trends, challenges, and best practices in data governance.

International Journal of Computer Trends and Technology , 2025

The field of data governance is changing drastically due to the emergence of AI technologies. The way data is being managed, secured, and gained value is being re-imagined as AI has new capabilities and challenges. AI is rewriting data management rules by allowing for real-time monitoring, automatic decision-making, and predictive analytics on a scale and speeds never before possible. This change that we refer to as Data Governance 2.0 is distinguished by automation enhancement, predictive capabilities improvement, and dynamic approaches to data processing and security (These are not just incremental changes but a paradigm change in how organizations handle data governance. Automated data quality management, AI-driven compliance monitoring, and more are the effects of these technologies in every aspect of data management [3]. Organizations must adjust to the new Data Governance 2.0 since it has become crucial for success in the contemporary business world. Firms that integrate AI tools within their existing data governance systems are in a position of superiority in the market. These advantages include enhanced functioning, analytical skills, and risk management methods. Nevertheless, it is difficult for businesses since they experience numerous obstructions in this transition. They have to address ethical issues related to the use of AI, confront some technical issues and even train their employees so that they can learn new skills. The management groups are usually forced to rearrange their company structure to cope with these changes. Studying the current situation in the business landscape, it is evident that the necessity to understand and align with Data Governance 2.0 is important for firms keen to remain competitive in the current data-oriented economy [4].