Appendix A DCMP Guide.docx

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APPENDIX A: Data Collection Management Plan (DCMP)

1. Purpose.

1.1 A data collection and management plan (DCMP) provides a logical framework to focus data collection, and ensure collected data links to the event objectives, learning demands, Essential Elements of Analysis (EEAs) with learning demands derived from Challenges, Opportunities, Issues, and required Decisions (COIDs) and their corresponding problem statements identified in Forge database. The DCMP consists primarily of tables that connect experimental objectives and learning demands to Measures of Merit (MOM) indicators such as Measures of Effectiveness (MOEs) and Measures of Performance (MOPs) via events in the experiment. The DCMP then shows what data must be collected in order to calculate the indicators, MOEs, or MOPs, and who is responsible for doing so and getting the data to the other analysts within the team.

1.2 The DCMP provides the Futures Integration Team (FIT) with a clear delineation of the scope, objectives, tasks, and team member responsibilities prior to execution of the experiment in order to create efficiencies and enable a greater expectation of successfully addressing COIDs and their associated learning demands.

1.2.1 Before the Event.

· To prevent problems with data collection

· To ensure data collection answers the why, how, and who is collecting observations

· To avoid data collection drift—losing focus

· To facilitate post event analysis and rigor

1.2.2 Post Event.

· To provide analytical links to how, when, and where data was collected

· To facilitate post event analysis on the breadth and depth of the data collection

· To provide potential answers to the LD & EEAs

· To assess the need for further analysis/sufficiency of the results

2. Discussion.

2.1 The DCMP is a useful tool all the way through to the end of experiment execution, because the analyst will often be asked to assess the impact that would be incurred if a particular event were to be removed from the schedule. The DCMP allows the analyst to give a quick answer to this question, phrased in terms of which learning demands and/or EEAs would go unmet.

2.2 Some objectives may go unmet even if all events take place. For example, the course of free play may unfold in an un-foreseen manner.

2.3 Analysis should not to be limited by the DCMP: the analysts should be free to perform analysis of unforeseen topics, if events unfold in such a way as to provide insight into them.

3. Format.

3.1 FITs will up-date tables in the Forge Focused Learning Module that connect experimental objectives/EEAs derived from the COIDs and learning demands to specific MOEs and MOPs and the experimentation schedule of events, scenario/plays, and tools used to execute the experiment.

3.1.1. MOEs and MOPs are performance measures used to examine how a force meets mission objectives, operates within an operational environment, and the necessary functions and sub-functions, tasks, structure/interfaces, and physical entities required to achieve the objective. For example, Headquarters MOEs and MOPs could examine monitoring and understanding, planning, and directing and disseminating. For each of these areas there are specifics such as information transmission/exchanges, values, times, effect, comprehension, process quality, etc.

3.2 Example of a DCMP using the Forge Focused Learning Module attached (Appendix A-1) (TBP).

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