For the past six months, I have been engaged in a massive data analysis project at Lithium to develop an index that measures the health of online communities. I've subsequently refer to this index as the community health index (CHI), which I like to denote with the Greek letter Χ. This project began shortly after I joined Lithium when I received my Ph.D. at UC Berkeley in Biophysics. Although it was a dramatic transition from academic to industry, I thought that analyzing community data shouldn't be that difficult. After all, data are just numbers and the math and statistics required to gain insight from them are just equations and symbols, which are universal across all disciplines. I was in for quite a surprise.
I have been a brain scientist during my academic years, and I focused in an esoteric area called computational visual neuroscience. Basically, that just means that I use a lot of math, statistics, and techniques in physics to model, study and ultimately understand how the brain process visual information. Coming from this background, I see an obvious connection between a community and the brain: they are both complex networked dynamical systems.
- The brain is made up of approximately 100 billion neurons talking to each other through a language of their own (action potentials, which are impulses much like the Morse code).
- Each neuron also network with other neurons and form connections that create local cliques of friends and buddies.
- The interactivity between the neurons is what makes the brain (viewed as a community of neurons) work. Without these interactivities the brain will wither and die of atrophy.
Although there are many more interesting analogies between the brain and a community, now that you see the connection, it is time for the surprise. To my astonishment, Lithium actually has a huge data set spanning the 10 years of its SaaS business operation. This is compounded by the fact that Lithium keeps about 240 different metrics that monitor every moving part of the community, and the metric list is growing as new features are being added. Moreover, there are copious non-metric data. These include moderator log files, notes from customer engagement, and annotations of PR or any event related to the customer. To my surprise, it turned out that these non-metric data accumulated over the years through active community management, moderation and customer engagement are most valuable and informative for the development of the community health index.
In later posts I'll describe my journey through this large and complex data set. But today I'd like to hear from you - what do you most want to know about the Community Health Index? What next steps would you like to see?
Photo by jepoirrier
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