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This question already has an answer here:

I have always been curious how other folks are storing their spatial data.

Outside of an enterprise environment where much data is stored in enterprise geodatabases, is it better to store data per project in project folders, (The data may be important for other projects too, or maybe for a stand alone project) OR is it better to file everything in specific geodatabases or main folders - with shapefiles (accessible for many projects)?

How do you manage data accessability?

What has worked well for you?

marked as duplicate by R.K., whuber Feb 17 '13 at 22:04

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The thing about non-enterprise data storage is the users of this data frequently have changing/varying needs. In an enterprise setting it's not always most efficient to store data in central geodatabases, but it goes a long way in sticking people to standards, which can be taught and monitored.

At the most basic level, a small company should house any "universal data" in a globally accessible (in terms of the organization) place. For example, if the company works mainly within one state/province they would store all state-wide base layers on a server. Any common edits (clipped regions) would be stored in sub-folders/geodatabases.

The next level of data storage (personal) can go a few ways. If many people work on a single project they may need to store their data on the same globally accessible server. If only one person is working on the project a local storage solution (backed up!) is fine. How this is done would be up to the individual user, but I think it's safe to say a common solution would be to use individual project folders, possibly containing geodatabases, depending on the project specifics.

Edit: If we're talking about a single user, the same general idea applies. Instead of housing global data on a server, they would use a separate drive/partition/sub-folder.

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Though I am not too experienced with spatial data management, I found the following Software-Carpentry.org video tutorial (discussing general data management practices, widely applicable to various scenarios) pretty helpful:

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