The Experts below are selected from a list of 78 Experts worldwide ranked by ideXlab platform
John Ladley - One of the best experts on this subject based on the ideXlab platform.
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Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program
Morgan Kaufmann, 2020Co-Authors: John LadleyAbstract:This book is for any manager or team leader that has the green light to implement a Data Governance Program. The problem of managing Data continues to grow with issues surrounding cost of storage, exponential growth, as well as administrative, management and security concerns – the solution to being able to scale all of these issues up is Data Governance which provides better services to users and saves money. What you will find in this book is an overview of why Data Governance is needed, how to design, initiate, and execute a Program and how to keep the Program sustainable. With the provided framework and case studies you will be enabled and educated in launching your very own successful and money saving Data Governance Program
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Overview of Data Governance development and deployment
Data Governance, 2020Co-Authors: John LadleyAbstract:Abstract This chapter presents an overview of the process to deploy the Data Governance Program.
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Operation and change
Data Governance, 2020Co-Authors: John LadleyAbstract:Abstract This chapter covers operating the Data Governance Program. It covers operating technology, training, measuring, and sustaining Data Governance.
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Overview: A day in the life of a Data Governance Program and its capabilities
Data Governance, 2020Co-Authors: John LadleyAbstract:Abstract This chapter addresses the most common question—“What does it look like?” It continues with a detailed examination of who should do the governing, what activities they need to perform, what is actually governed, and what Data Governance looks like when it occurs.
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Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program
2012Co-Authors: John LadleyAbstract:This book is for any manager or team leader that has the green light to implement a Data Governance Program. The problem of managing Data continues to grow with issues surrounding cost of storage, exponential growth, as well as administrative, management and security concerns - the solution to being able to scale all of these issues up is Data Governance which provides better services to users and saves money. What you will find in this book is an overview of why Data Governance is needed, how to design, initiate, and execute a Program and how to keep the Program sustainable. With the provided framework and case studies you will be enabled and educated in launching your very own successful and money saving Data Governance Program.Provides a complete overview of the Data Governance lifecycle, that can help you discern technology and staff needs Specifically aimed at managers who need to implement a Data Governance Program at their companyIncludes case studies to detail 'do's' and 'don'ts' in real-world situations
David Plotkin - One of the best experts on this subject based on the ideXlab platform.
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Data Stewardship and Data Governance: How They Fit Together
Data Stewardship, 2014Co-Authors: David PlotkinAbstract:This chapter discusses the definition and deliverables (including policies, procedures, and processes) for a Data Governance Program. The structure of the organization is discussed, as well as the roles and responsibilities of participants in the Program, including a brief list of stewardship tasks. The key role of Data Stewardship in a Data Governance Program is discussed, as well as how Data Stewardship fits into the overall Program.
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Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance
2013Co-Authors: David PlotkinAbstract:Data stewards in business and IT are the backbone of a successful Data Governance implementation because they do the work to make a company's Data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of Data Governance, including how to organize, train, and work with Data stewards, get high-quality business definitions and other metaData, and perform the day-to-day tasks using a minimum of the steward's time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort. Provides clear and concise practical advice on implementing and running Data stewardship, including guidelines on how to organize based on company structure, business functions, and Data ownership Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the Data stewardship effort and report back to management Includes detailed lists of responsibilities for each type of Data steward and strategies to help the Data Governance Program Office work effectively with the Data stewards Table of Contents Introduction Chapter 1 Data Stewardship and Data Governance: How They Fit Together Chapter 2 Understanding the Types of Data Stewardship Chapter 3 Stewardship Roles and Responsibilities Chapter 4 Implementing Data Stewardship Chapter 5 Training the Business Data Stewards Chapter 6 Practical Data Stewardship Chapter 7 The Important Roles of Data Stewards Chapter 8 Measuring Data Stewardship Progress: The Metrics Chapter 9 Rating Your Data Stewardship Maturity Chapter 10 Summing it all up Appendix A Example Definition and Derivation Appendix B Sample Training Plan Outline
Catherine E. Connelly - One of the best experts on this subject based on the ideXlab platform.
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e-HRM Systems in Support of “Smart” Workforce Management: An Exploratory Case Study of System Success
Electronic HRM in the Smart Era, 2017Co-Authors: Kathleen Mcdonald, Sandra Fisher, Catherine E. ConnellyAbstract:Abstract Purpose As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are increasing. This chapter uses the well-established DeLone and McLean (D&M) model from the information systems literature to analyze how a smart workforce management system can create value for an organization. Methodology/approach The chapter is based on an exploratory case study conducted with a North American industrial products firm. We review three systems-level predictors of success from the D&M model (system quality, information quality, and service quality) and evaluate the company’s systems on these attributes. Findings The company’s e-HRM systems fall short on the information quality dimension, which limits potential for overall system success related to smart workforce management. Research limitations/implications The e-HRM literature focuses on individual-level factors of system success, while the D&M model uses more macro factors. Blending these may help researchers and practitioners develop a more complete view of e-HRM systems. Conclusions from this chapter are limited due to the use of a single, exploratory case study. Practical implications Companies must pay attention to all three predictors of system quality when developing smart workforce management systems. In particular, implementation of a Data Governance Program could help companies improve information quality of their systems. Originality/value This chapter adds to the literature on smart workforce management by using a model from the information systems literature and a practical example to explore how such a system could add value.
Jack R Stickel - One of the best experts on this subject based on the ideXlab platform.
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Transportation Agency Data Governance Programs Getting Started Toward Sustainability
2015Co-Authors: Jill Sullivan, Jack R StickelAbstract:Transportation agencies deal with a multitude of issues covering increased streams of Data, new Program requirements, information technology changes, refocusing agency strategic goals, and constrained budgets. MAP-21 (Moving Ahead for Progress in the 21st Century Act) establishes a performance, outcome-based approach for transportation Programs. A Data Governance Program can improve an agency’s capability to manage their transportation Data Programs effectively and to address these Data challenges. Starting and sustaining a Data Governance Program is a daunting task, in part because the concepts are hard to explain. There is typically a strategic event or series of events that trigger an agency to start a Data Governance Program, e.g. developing a transportation asset management Program, incorporating an enterprise geographic information system, or evolving information service needs. Starting Data Governance Programs requires a common vision, documented Data business work flows, defined roles and responsibilities, and transition progress tracking. This paper highlights effective fundamental steps that can aide in establishing and sustaining a strong Data Governance framework. This paper provides the context for Data Governance within an overall Data management Program and includes a business need for Data Governance, target areas and initial steps to consider, current initiatives in transportation, and finally, an overview of how the Alaska Department of Transportation and Public Facilities initiated a Data Governance Program in response to a transportation asset management information system, the transition from a legacy mainframe transportation Database to a geospatial linear referenced based system, and the new MAP-21 Data requirements.
Kathleen Mcdonald - One of the best experts on this subject based on the ideXlab platform.
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e-HRM Systems in Support of “Smart” Workforce Management: An Exploratory Case Study of System Success
Electronic HRM in the Smart Era, 2017Co-Authors: Kathleen Mcdonald, Sandra Fisher, Catherine E. ConnellyAbstract:Abstract Purpose As e-HRM systems move into the ‘smart’ technology realm, expectations and capabilities for both the automational and informational features of e-HRM systems are increasing. This chapter uses the well-established DeLone and McLean (D&M) model from the information systems literature to analyze how a smart workforce management system can create value for an organization. Methodology/approach The chapter is based on an exploratory case study conducted with a North American industrial products firm. We review three systems-level predictors of success from the D&M model (system quality, information quality, and service quality) and evaluate the company’s systems on these attributes. Findings The company’s e-HRM systems fall short on the information quality dimension, which limits potential for overall system success related to smart workforce management. Research limitations/implications The e-HRM literature focuses on individual-level factors of system success, while the D&M model uses more macro factors. Blending these may help researchers and practitioners develop a more complete view of e-HRM systems. Conclusions from this chapter are limited due to the use of a single, exploratory case study. Practical implications Companies must pay attention to all three predictors of system quality when developing smart workforce management systems. In particular, implementation of a Data Governance Program could help companies improve information quality of their systems. Originality/value This chapter adds to the literature on smart workforce management by using a model from the information systems literature and a practical example to explore how such a system could add value.
