July 22, 2008
Data Into Meaning
While its not necessarily my ideal self-image, (still working on image of myself as cheese-making blues guitar playing sommelier) I wouldn't disagree if a sociologist classified me as a knowledge worker living in the tail end of the Information Age (I'll save the rationale behind the tail end comment for another day). Just like I couldn't argue with my college sociology professor who classified me as an only-child (wonder how my sister would feel about that?).
My seventeen year old daughter thinks my job consists of talking, surfing the web, sitting (in meetings and on planes), and answering email. I guess that is mostly true other than her characterization is devoid of my value add to Adobe: thinking, making decisions, and hopefully contributing in some small way to Adobe being a fun place to work.
Being a successful product manager (I'll take liberty and assume my lengthy tenure at Adobe is in part due to being good at what I do) requires an assortment of talents and skills. Decision making as a skill is looking at data and making a decision based on the data. Decision making as a talent is about the meaning you make from the data. Really hard decisions fall into two categories: 1) making decisions when you have little available data, and 2) making decisions when you have too much data. The ability to turn data into something more than a percentage or some type of numerical output is where the real value lies in making decisions and understanding what lies beneath the data.
At the moment I'm spending quite a bit of time talking to individuals and companies about some product concepts we are exploring. These are in-depth conversations about workflows, product needs and challenges, and a range of other topics. We diligently capture these conversations and then try to make sense of the data and turn it into meaning that can help guide the decisions we need to make. As we talk to people, patterns emerge; some obvious but most more subtle. The subtle ones are sometimes the most interesting ones and would be lost if all we did was send these user a link to complete an online survey (because surveys tend to be quantitative and thus only provide data). Its the conversation and collection of data from that conversation that facilitates the transformation of the data into meaning.
Another example comes to mind. While enjoying Sunday brunch at our favorite local breakfast cafe, my nine year old daughter Sara told me about a National Geographic Kids photography contest she wanted to enter. She took lots of photos on our recent trip to Kauai and wanted to send one in. I asked her how she would decide which one to submit. She didn't have a specific set of criteria other than choosing one that she liked a lot. We talked about choosing one that was technically appropriate (focus, exposure, framing) but then started talking about qualitative criteria. I suggested she look at choosing a picture not strictly based on the technical merits of the image (data), but select a picture that evokes emotion and/or tells a story (meaning). I think she got it.
In the digital world we live in today where the explosion of data bombards and overloads us (whether it is images or information) making meaning not only helps us manage and make sense of the data, but it personalizes it and makes for a more interesting story whether we tell that story with pictures, words, or heaven forbid PowerPoint.