Hackystat UI: Wesabe and Social Software Metrics

Robert recently pointed me to Wesabe, which is a social networking site focusing on personal finances. This is an interesting site to compare/contrast with Hackystat, since it:

• Deals with numbers and "metrics".
• Requires members to share aspects of very personal information (finances) in order to exploit the potential of social networks.
  

Their help guide is in the form of YouTube videos, which is a little weird (or maybe the wave of the future). I will show some blurry screen shots to illustrate some of the interesting aspects of this tool. This first one shows the top-level organization of your Wesabe account, which has three tags: Accounts, Tips, and Goals.

Accounts basically corresponds to your "raw sensor data" in Hackystat. In Wesabe, you are expected to upload your bank and credit card information.

Tips correspond to information supplied by other users based upon analysis of your account data. The idea is that the raw financial data is parsed to find out what you spend money on.

For example, if you have gas charges, then you will be hooked up with Tips on how to save money on gas. They use a keyword-based mechanism to hook together account data with tip data.

The tips could be generic (don't buy premium gas) or more specific (Don't buy gas from the gas station you're going to; they are a rip-off).

Here's a screen shot of a drill-down into an account, along with the tips and keywords associated with it. Often, you will need to manually annotate your raw financial data in order for Wesabe to start to work its magic on it. You can also see from this screen shot that individual financial items can be rated, and you can also see whether other Wesabeans have recorded a similar kind of purchase.

While tips are a kind of "bottom up" mechanism for producing "actionable" information from your raw account data, goals are more of a top-down approach, in which you first specify your high level goal, and then you get hooked up with other users interested in the same approach.

In Wesabe, it seems that the main focus is to direct you into existing discussion forums rather than explicitly connect you to your financial data. For example, a goal would be something like "Start a College Savings fund for my kids.

So, how does this all relate to Hackystat? I think there are some intriguing possibilities. First, Hackystat currently allows data to be "shared" only within the context of a Project---if there are multiple members of a Project, then they can potentially see each other's data. Wesabe illustrates how you might think about "sharing" on a more global level. The idea is that you don't share the actual financial information: no one knows where you shopped or how much spent, but via the social bookmarking mechanism, the system can hook you up with "tips" (specific actionable items) or "goals" (a community of people with the same intentions).

To explore how this might work, let's imagine some possible "Tips" from the realm of Java software development:

• How to convert from Java 1.4 to Generics in Java 5 (See my previous blog posting on this.)
• Proper use of concurrency mechanisms.
• Diagnosing a null pointer problem.
  

Hmm. These tips all seem to require more context than is typically provided by Hackystat data. One could image a sensor data type that provides data on the import statements associated with a file you are editing. That would give some insight into the kinds of libraries you are using, which might enable you to be hooked up with helpful tips. Another sensor data type might provide the stack trace and error message associated with a thrown exception.

Now let's think of possible "Goals"

• Reduce the number of daily build failures.
• Reduce the time required for running all unit tests.
  
• Improve the quality of code.
  
• Improve the scalability of the database.

Some of these might be inferable from the kinds of telemetry charts you are monitoring, for example.

In any case, Wesabe indicates an interesting research direction for Hackystat: create the capability for users to add keywords to their data, and then process these keywords as a way to hook users with common interests and mutually useful skills with each other.