Project Data Management Planning

2017 ◽  
pp. 13-26 ◽  
Author(s):  
William K. Michener
Keyword(s):  
Data Management ◽  
Management Planning ◽  
Project Data

Integrating GIS and Data Management Within the Project Environment

2008 ◽  
Author(s):  
Hugo Guerrero
Keyword(s):  
Data Management ◽  
Project Teams ◽  
The Other ◽  
Geospatial Technology ◽  
Right Of Way ◽  
Project Environment ◽  
Other Hand ◽  
Project Data ◽  
Minimum Work ◽  
Project Level

Today, much of the focus on integrating Geospatial technology and data has been on the operations side of the business. Not much attention has been paid to the workflow within the project environment even though most of the data that is used to populate enterprise datasets is created or prepared as a requirement of a project; that said; it is early on at the project level when geospatial integration needs to be implemented and incorporated into the project workflow. On the other hand, project teams have historically focused on strictly satisfying the needs of the project. This is typically limited to the minimum work required to design, permit & build a given work scope. This approach has left many companies with the task of paying high costs for the project data to be translated, captured or in some cases recreated after the fact. Too many times, Gas Company X hires multiple consultants with different disciplines responsible for different project scope items (i.e. Environmental, Right-of-way, Engineering, etc...). Each company has established methods for preparing and organizing their respective data without ever thinking how Gas Company X intends on using the data for other enterprise needs during the project and after the project has been completed. This presentation outlines methods by which companies can require that their project consultants produce project data with geospatial integration in mind. This includes identification of required resources & workflows to specify and manage the data that is prepared and/or collected in a structured environment that is geospatially & data aware.

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

2021 ◽  
pp. 193-225
Author(s):  
Carolin Helbig ◽  
Uwe-Jens Görke ◽  
Mathias Nest ◽  
Daniel Pötschke ◽  
Amir Shoarian Sattari ◽  
...  
Keyword(s):  
Data Management ◽  
Research Project ◽  
Entire Data ◽  
Data Lifecycle ◽  
Project Data ◽  
Institutional Unit

AbstractData management includes the development and use of architectures, guidelines, practices and procedures for accurate managing of data during the entire data lifecycle of an institutional unit or a research project. Data are defined as different information units such as numbers, alphabetic characters, and symbols that are particularly formatted and can be processed by computer. The data in the project is provided by various actors which can be GeomInt partners, their legal representatives, employees, and external partners.

scholarly journals Data Management Planning: How Requirements and Solutions are Beginning to Converge

2020 ◽  
Vol 2 (1-2) ◽  
pp. 208-219 ◽  
Author(s):  
Sarah Jones ◽  
Robert Pergl ◽  
Rob Hooft ◽  
Tomasz Miksa ◽  
Robert Samors ◽  
...  
Keyword(s):  
Data Management ◽  
Driving Forces ◽  
Management Planning ◽  
Current State ◽  
Adoption And Implementation ◽  
Key Characteristics ◽  
Data Stewardship ◽  
Fair Principles ◽  
Research Workforce ◽  
Research Initiatives

Effective stewardship of data is a critical precursor to making data FAIR. The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions (DMP). We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles, we describe the background, context and historical development, as well as major driving forces, being research initiatives and funders. Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics. Next, we elaborate on emerging common standards for DMPs, especially the topic of machine-actionable DMPs. As sound DMP is not only a precursor of FAIR data stewardship, but also an integral part of it, we discuss its positioning in the emerging FAIR tools ecosystem. Capacity building and training activities are an important ingredient in the whole effort. Although not being the primary goal of this paper, we touch also the topic of research workforce support, as tools can be just as much effective as their users are competent to use them properly. We conclude by discussing the relations of DMP to FAIR principles, as there are other important connections than just being a precursor.

Data management planning and repository demands for qualitative research

KWALON ◽  
2016 ◽  
Vol 21 (1) ◽  
Author(s):  
René van Horik
Keyword(s):  
Data Management ◽  
Research Data ◽  
Digital Data ◽  
Data Repository ◽  
Data Sets ◽  
Data Repositories ◽  
Management Planning ◽  
Research Activities ◽  
Trusted Data ◽  
New Research

Summary Nowadays, research without a role for digital data and data analysis tools is barely possible. As a result, we see an increasing interest in research data management, as this enables the replication of research outcomes and the reuse of research data for new research activities. Data management planning outlines how to handle data, both during research and after the research is completed. Trusted data repositories are places were research data are archived and made available for the long term. This article covers the state of the art concerning data management and data repository demands with a focus on qualitative data sets.

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