THE CLOUD -- HOW DO USERS UNDERSTAND THEIR COMPUTING ENVIRONMENT? .. AND DOES IT MATTER?
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Google and many others are looking to a world where the Internet is your computer. Your data storage, word processing, presentations, everything can run remotely if you’re willing to have it live somewhere besides your own hardware. (Security and speed are two commonly mentioned downsides.)
Whether Cloud (more technically, Grid: Ian Foster’s grid computing blog) computing is the way of the future is not my area of expertise. I’m more interested in how people come to understand their computing environment – and whether this understanding matters. One side argues that you just want your appliances to work – you pick up the phone and make a call, you don’t need to know how the electrons translate into audio. The other side is that you can’t take full advantage of your systems if you don’t understand their features.
Clearly there is a range. I don’t actually know how my cell phone works, but I do know enough that if I’m getting a bad signal going to a window may help the antenna do its job. I don’t know how the underlying PHP code works that runs my wiki, but I do know how I can work around some of its limitations to get a particular job done. Similarly, most of us don’t know how email systems talk to one another, but we can make adequate decisions about when to use email versus when to pick up the phone and call someone.
Our problem solving ability and decisions are based on our “mental models” of how the system works. As long as these mental models are moderately accurate we can make decent decisions about how to use our technology.
Two points: (1) mental models do make a difference and (2) mental models are the result of the technology itself and the technology’s implementation.
Accurate models of “cloud computing” will include a knowledge of where the data is stored (e.g., on your local PC, inside the corporate firewall, on a commercial secure server); how often the data is backed-up; whether syncing between the local PC and the “cloud” version is automatic; where the application is running (e.g., will you be able to use the application when on a plane, or at a vendor’s site where you either will or won’t have Internet access); do you need to worry about updates to the software (not if the application is on the “cloud”); etc.
The difficulty with abstract concepts such as cloud computing is that they don’t give us much on which to base our sensemaking. Ancient technologies had a concreteness that hinted as to their use and functioning – the “affordances” were clearer.
The more abstract the technology, the more implementation needs to focus on helping users develop their mental models. This may mean increased implementation time and budget. Not paying this price may mean users make uninformed decisions about how they manage their computing environment – an expensive outcome if that data is lost or compromised.
In a future post I’ll consider how different mental models of collaboration tools might help teams work better (e.g., who "owns" the team wiki?).
Whether Cloud (more technically, Grid: Ian Foster’s grid computing blog) computing is the way of the future is not my area of expertise. I’m more interested in how people come to understand their computing environment – and whether this understanding matters. One side argues that you just want your appliances to work – you pick up the phone and make a call, you don’t need to know how the electrons translate into audio. The other side is that you can’t take full advantage of your systems if you don’t understand their features.
Clearly there is a range. I don’t actually know how my cell phone works, but I do know enough that if I’m getting a bad signal going to a window may help the antenna do its job. I don’t know how the underlying PHP code works that runs my wiki, but I do know how I can work around some of its limitations to get a particular job done. Similarly, most of us don’t know how email systems talk to one another, but we can make adequate decisions about when to use email versus when to pick up the phone and call someone.
Our problem solving ability and decisions are based on our “mental models” of how the system works. As long as these mental models are moderately accurate we can make decent decisions about how to use our technology.
Two points: (1) mental models do make a difference and (2) mental models are the result of the technology itself and the technology’s implementation.
Accurate models of “cloud computing” will include a knowledge of where the data is stored (e.g., on your local PC, inside the corporate firewall, on a commercial secure server); how often the data is backed-up; whether syncing between the local PC and the “cloud” version is automatic; where the application is running (e.g., will you be able to use the application when on a plane, or at a vendor’s site where you either will or won’t have Internet access); do you need to worry about updates to the software (not if the application is on the “cloud”); etc.
The difficulty with abstract concepts such as cloud computing is that they don’t give us much on which to base our sensemaking. Ancient technologies had a concreteness that hinted as to their use and functioning – the “affordances” were clearer.
The more abstract the technology, the more implementation needs to focus on helping users develop their mental models. This may mean increased implementation time and budget. Not paying this price may mean users make uninformed decisions about how they manage their computing environment – an expensive outcome if that data is lost or compromised.In a future post I’ll consider how different mental models of collaboration tools might help teams work better (e.g., who "owns" the team wiki?).
