A Capability Maturity Model for Research Data Management
CMM for RDM » 1. Data Management in General » 1.1 Commitment to Perform

Changes for document 1.1 Commitment to Perform

Last modified by Arden Kirkland on 2014/05/18 11:53
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edited by Jian Qin
on 2013/09/21 22:46
To version 6.1
edited by Jian Qin
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1 -== 1.1 Commitment to Perform ==
1 += 1.1 Commitment to Perform =
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3 3 {{box cssClass="floatinginfobox" title="**Contents**"}}
4 4 {{toc/}}
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7 7 //Perhaps the most important actions are Commitment to Perform, the actions the organization must take to ensure that the process is established and will endure. Commitment to Perform involves establishing organizational policies that support data management in general. //
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9 -=== 1.1.1 Identify stakeholders ===
9 +== 1.1.1 Identify stakeholders ==
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11 11 The goal of identifying stakeholders is to establish a shared understanding of who are the data owners, contributors, managers, and users affected by data management. Stakeholders include not only those who create and manage data but also entities that are data users, funding agencies, home institutions of contributing researchers (DataOne, 2012).
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17 17 As a result, explicit identification of stakeholders is necessary to ensure that the design of the processes meets their different needs and to ensure implementation efficiency and usefulness of data management. As in Mullins (2007) identification of stakeholders may start with discussion with key informants, such as researchers or sponsored program office staff and then use snowball sampling to identify additional stakeholders. The results of these efforts may be confirmed by a follow up survey.
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19 -=== 1.1.2 Develop user requirements ===
19 +== 1.1.2 Develop user requirements ==
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21 21 The goal of developing user requirements is to describe the goals the data management systems and practices achieve for various user groups without going into details about how those goals are to be achieved. For example, researchers may require that data management ensure that data are available for future analysis, while potential reusers of data may require effective data description to enable them to find and make sense of the data.
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25 25 User requirements for scientific data management may be identified through analyzing data flows, workflows, leading data management problems, and researchers’ data practices. These requirements can be represented at a high level in use cases, user scenarios or personas [[(Cornell University Library, 2007>>CMM for RDM.Bibliography||anchor="Cornell"]]; [[Lage, Losoff, & Maness, 2011>>CMM for RDM.Bibliography||anchor="Lage"]]). A key point in this process is that user requirements mean not only clear-cut project objectives but also goals for the data management services to serve a longer term and wider scope of scientific data management.
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27 -=== 1.1.3 Establish quantitative objectives for data management ===
27 +== 1.1.3 Establish quantitative objectives for data management ==
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29 29 The goal of establishing quantitative objectives for data management is to provide a set of measures of the data management process and quantitative targets for those measures. For example, a simple metric is the quantity of data collected and the cost of the collection process. For instance, in doing a survey, a goal might be a certain sample size (number of surveys completed) and a target set based on the research needs and the project’s budget for data collection. An alternative metric is the quality of the data, with a target of a no more than a certain error rate. A goal for data privacy might be that there be no unintentional data releases. For data sharing, a goal might be that new users can gain access to the data within a certain time period.
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35 35 Establishing quantitative objectives can be done following common practices in management (e.g., key performance indicators and balanced scorecard) and in research project assessments (e.g., outcome-based assessment).
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37 -=== 1.1.4 Develop communication policies ===
37 +== 1.1.4 Develop communication policies ==
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39 39 (% style="font-size: 14px;" %)Developing communication policies is developing communication channels and procedures among the constituencies. This makes communication efficient and clear. Communication channels are specific to organizational contexts, and can be facilitated by communication technologies such as websites, ticketing systems, discussion forum, mailings, wikis, social media, etc.
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