The Quantified Self, Part I: Will it Lead to Better Data Management?
Over the next few weeks, I’ll be writing about self-quantification. In today’s post, I’ll introduce the topic and address a high-level question.
As a kid, I used to play videos games and track my high scores. I remember playing games like Techmo Football and NBA Jam. During quarter and period breaks, I would look at game statistics with my friends. At an early age, data just made sense to me.
While I still play video games from time to time, most of my involvement with data these days takes place in a professional context–i.e., at work. I suspect that I’m not alone here, but that may be changing. More and more, we’re hearing about the quantified self. Forget just measuring how many calories you burned at the gym or how many miles you ran with an app. These activities have been quantifiable for some time–and wearable technology is only intensifying this trend. Today, people are increasingly monitoring their health, sleep patterns, food, and other aspects of everyday life.
The movement arguably dates back to 2007, when journalist and author Gary Wolf co-founded the Quantified Self blog. Just six years later, there are worldwide QS conferences.
IM Ramifications of the Quantified Self
No doubt that some wonder whether the self-quantification movement is a good thing. Are we merging with machines? Is Ray Kurzweil right about singularity? What happens if someone hacks devices responsible for generating data on sensitive health matters?
These are lofty issues that I’m not going to address here, but there’s one indisputable benefit of the trend towards auto-analysis: It should make us better at information management (IM). For instance, let’s say that we’re using an app to monitor our sleep. We suspect the quality of the data generated by the app, not to mention its recommendations. We start searching for a better app or means to evaluate the quality of our sleep. We ignore data that doesn’t reflect our normal state of mind. Perhaps we had to pull all-nighter in college or we worked longer to meet an urgent work deadline.
In short, all of these things mean that we become increasingly comfortable with data. Data becomes a greater part of our personal lives, not just something we have to deal with at work. We see the importance of data quality first-hand, not just because someone in IT scolded us or an obscure interface failed.
Not everyone needs a reminder about the importance of data quality. Many of us understand GIGO–and have for years. There are plenty of us, however, who could benefit from a not so gentle tap on the shoulder here. To the extent that we learn more from our own mistakes than from external sources telling us what to do, I for one believe that the quantified self will make us better IM professionals.
What say you?
Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management. The MIKE2.0 Methodology has been built to support our belief that information really is one of the most crucial assets of a business. We believe meaningful, cost-effective Business and Technology processes can only be achieved with a successful approach for ...