Many of the complaints about Google+ Circles can be paraphrased as Nice idea, but too much of a hassle.
Peter Pachal described the problem in an article at PCMag.com.:
The main problem with Google Circles is that it's tedious. While I agree that most people separate their contacts into various groups in real life, doing so in a social network is a chore. It's one of the reasons we have different social networks (LinkedIn for work, Facebook for friends, etc.). Asking people to do this kind of organizing proactively, on a single network, vastly overestimates the patience of Web users. Sure, some people are very organized and left-brained (like the engineers who created Google+), with spotless inboxes and well-maintained lists of contacts, but my feeling is that the vast majority aren't. And of all the things that have turned people off of Facebook over the years, the lack of focus on friend-organizing tools isn't one of them.
And here’s Andrew Gent (author of the misnamed Incredibly Dull blog) on the same topic:
My second issue is around circles. I understand they sound like a good idea. My personal (and professional) relationships are more complex than Facebook's simplistic friends / non-friends model.So being able to define your relationships in more detail sounds like a positive step.
The problem is, it's far more difficult than it sounds. I have friend friends and I have professional friends. I have professional friends and professional acquaintances. Some work for my old employer; some used to; some never did. Some know I am interested in poetry and video games (among other things); some don't. A few have met my wife; some may not even know I am married.
When I start to break it down, it is not only not binary, it is more complex than even I can describe. Which is what makes Google+'s circles so frustrating. They require too much thinking. This is not a technical issue, per se, but a failure to be able to turn an implicit organic process into an explicit concrete categorization.
The lesson here for data modelers and requirements analysts: Modeling a phenomenon can be the (comparatively) easy part. What’s hard is collecting, cleansing, maintaining, and archiving the data that populates the model.
This lesson cannot be taught to modeling novices; it is one of the lessons that modelers-in-training can never get until they have learned the basics—until they have become intermediate modelers. Upon becoming competent, a freshly minted modeler can be swept away by the power of the technique. Armed with a new facility with content-neutral data-model shapes, the modeler thinks “Wow, this is powerful—I can model anything! No matter what the users ask for I can express it on a model!”
Such enthusiasm about newly acquired knowledge or skill is always dangerous.
In this case, the well-meaning modeler overlooks an ugly truth: every data model will, when implemented, impose a burden on the user community to populate that model with data. If the model oversimplifies the phenomenon, the users will experience a burden much like what the users of Google+ Circles are reporting. Being able to express something on a data model is a good thing. But it is only the beginning. And often, there is little correlation between how easy it is to develop the model and how easy it will be to populate it with instances.
