Welcome to familysmarts.net. In this third post I move past the stage of simply ‘counting’ things in the household datasets I generate, to a new stage where I seek a different type of data to support probing family data science related questions.
If you are new to this site, here is a brief overview. Since 2013, I have been experimenting with family data science – or the process of drawing deeper understanding and insights to help my wife and daughters grow, stay healthy and be happy. I am not (yet) one of those obsessed quantified-self data geeks nor do I buy into the fitness tracker fads either. I also have no intentions of replacing intuition and the good ol’ role of parenting with smart bots to guide your loved ones towards doing the right thing. Rather, I am just a curious dad who is convinced that a little 21st century IT along with a stream of the right data and analytics can help reinstate a healthy dose of household conscientiousness in between the joy, pressures and chaos of everyday family life.
I generate and maintain datasets that record key aspects of my family’s day-to-day lives. I use these datasets to drive most of my family data science experiments and they can be grouped in one of two ways.
The first group is mostly transactional in nature. Credit card purchases, blood test results or dance practice attendance are just a few examples of datasets in this group.
The second group is mostly analytical in nature. These datasets aggregate, or count the different aspects recorded in the day-to-day transactional events.
During the first few years of running my family data science experiments, I happily collected and aggregated this information. For example, I could tell you the number of hours my daughters spent at dance practice,how many times my wife and oldest daughter suffered from bronchitis during winter months, or the months during which I sent the most work-related emails.
These analytics can be amusing for a little while, but as dad and husband, I wanted answers to more probing questions. I wanted to know what happens to my daughter’s respiratory issues when we cut lactose for a period of six months? How do my wife’s sleep patterns improve when she goes swimming on a regular basis? Does my standing heart rate decrease during weeks of healthy eating and intense exercise?
To answer these types of questions I needed a new type of dataset. One that would do less ‘counting’ of day-to-day events and instead more ‘explaining’ of how these events come together in unique ways that help my family grow, stay healthy and be happy.