A while back Bokardo wrote a post about structured blogging and who it benefits…Joshua included a few links in the post to people who think bloggers have the least to benefit, and that the system can be gamed.
This may be all true and I’m not sure myself how you can get the masses to blog in a structural template, it has to be hardwired in the blogging software, and then there is the lazy factor.
I’ve posted in the past about structured blogging in general, but this post is more on the personal value of structured blogging, and this is very apparent in a service that I’ve mentioned before called datablogging.
Back to Bokardo’s post for a second, the insight on the personal value of structured blogging, may drive the aggregate value, people need to firstly have a personal benefit…and I believe that’s how the del.icio.us lesson came about.
In this post about the del.icio.us lesson Bokardo links to and explains the personal benefits of datablogging. It seems datablogging doesn’t have to be your usual type of blogging, it may be that you would like to input data (figures/numbers) to later see a timeline, graph, etc…version of your data, in order to make inferences, etc…This is very much so a type of knowledge blogging, gaining some insight and being able to take action from all the data input. In many cases this is more logging than blogging, in fact this format of publishing in this instance are called logs.
The classic example is what Joe (the developer of datablogging) refers to as the slut factor, this is an example of datablogging, here is the excerpt:
“About three years ago a young woman decided to track her sex life with Reger.com’s datablogging service. She was using a Sex Log that tracked sex partner, intimacy rating, orgasm rating, who initiated, etc. And she decided to make it public. Of course, I immediately subscribed to it via RSS like any good horny geek would
Over the course of a few months she blogged about having sex with quite a few people. Using our graphing component she created a pie chart of her partners and the number of times she had sex with them. It started out as a solid circle. Then it was cut in half when she found another sex partner. Then in thirds.
Before I knew it her sex partner graph looked like a freakin’ pizza pie… small slivers… she must have had fifteen partners inside of two months. “What a slut,” I thought… and went on with my life. (After telling my RSS reader to check her feed every hour, instead of every day.)
Then one day something special happened. She was writing about her sexual encounters over the last few months in a blog entry. Reviewing her graphs. Kind of a nostalgic piece. All of a sudden she says something to the effect of “oh my gosh, I just looked at my graph… I’m slutty!”
After I pulled myself off the floor from laughing so hard, I realized that something special had happened. Of course she was slutty… I had noted as much a month before. Anybody who knew her probably knew she was slutty. But she didn’t!
She didn’t know she was slutty!
Until she tracked the data, graphed it and analyzed it. Datablogging had given this young woman insight into her life. For me this was a watershed moment in datablogging. It was proof-of-principle that datablogging can help us learn about ourselves in ways that other tools can’t. Sure, she probably had an inkling as she dropped trow for that fifteenth guy that maybe she was slutty, but datablogging brought it into conscious focus for her… a graph representing her sluttyness… her slut factor.”
I can’t see why datablogging hasn’t taken off in many industries, it’s different than straight up blogging, from this example it enables you to over a period of time discover, trends and behaviour…how perfect would this be in the sales industry, etc…
At the moment it is more prominent in the personal arena, such as fitness logs, pregnancy logs, etc…Joe sometimes refers to it as a personal nostalgia repository (personal value) and how data could be even easy to capture in the future (but to store it all).
Actually the personal value post is interesting as it mentions that value in datablogging grows over time, just like some old photo’s. This is contrary to most information that is of no value after a certain time, eg. stockmarket, etc…
Maybe they could also be called attention logs, as it records very immediate and personal stuff.
Joe has a great take on Continuous data and Schema data, mentioning that schema data, much like the structured blogging concept, requires a certain scale to show any group context value.
Let’s check out some of the unique features of datablogging, and the different logs that are available, note that you can even modify the log templates to suit your needs.
Location - each entry can be attributed to a location (users can see entries for a particular location), also has GPS coordinates for a location.
Activity-Specific Log Types - this is the range of logging platforms
Prebuilt and custom charts and graphs - overtime see a visual representation of your logging…here are some screenshots. Even build graphs from search queries, see more. Another thing is you can link to a graph from each entry.
Multiple logs - as many as you like…see the admin page.
Private logs - choose public or private
Social network - see your friends most recent posts, you can even message them and you also have an inbox
Mobile - post from your phone, even pictures, also post from email
Time periods - set time periods in your life, this also crosses over to aggregate posts from your various logs.
Do this by setting up a start/end time or you can make it open ended. Then when you post a log entry it will automatically fall into the time periods that you have set.
This way you can see all the different time periods a post was happening in, and from each post you can view all posts in a time period.
Eg. if you post about “failing school”, in the future you can look back and see that this post happened in the time periods (moving house, breaking up with girlfriend, don’t like my job). In hindsight you can see in context probably why you failed school, by looking at all the emotional stuff that was happening at the same time. Then from this post you can click on a time period, eg. moving house, and see all the posts in this time period…here’s a screenshot, and another.
Episode - these connect posts together, it’s kind of like a category, you are just choosing to mark a particular post as part of an episode. Within each post you can see all the other posts within this episode.
So unlike Time Periods, this is something you have to mark for each post, like adding a category for each post. Here is a screenshot.
This is something I want to see on Twitter.
There is loads more features, I just listed the most unique features…as you can see this is a new tyoe of blogging, it has a more intimate purpose.
If you want to learn more, see here.
Here are some examples of business logs.
Here are some example of personal logs, the classic example is Ronny the Runner, he logs to keep track of his runs, to see how far he went. In hindsight he can see his performance by generating graphs and charts from this log and make decisions from this information…this is personal knowledge management, a classic example of data (logs), to information (charts), to making decisions and taking action (knowledge).
datablogging has been round for a while, I thought there would be more copycats, maybe it’s ahead of its time. I do like the idea of recording and documenting your personal life, we all like looking at old photo’s and reading old journals, well this is that sort of thing to the extreme.