The case for using data analytics in your personal life
With the growth in data and analytics, it is not uncommon to hear organizations talk about how focused they are on data-driven decision making. The premise behind this focus is that the quality of organizational decisions is improved if they are driven by data.
Those of us who are in the analytics profession, and for others who are in other business functions and believe in the power of data, here is a question to think about – is it possible to pursue data-driven decision making in our personal lives?
My interest in this subject was triggered by a blog by Stephen Wolfram and the book Better by Atul Gawande. Wolfram, who is the brain behind Mathematica and Wolfram Alpha, in his blog back in 2012 wrote about the insights he was able to draw from the data crunching he did on personal data that he had accumulated over 20+ years. Wolfram’s analysis of his emails, keystrokes, phone calls, meetings, events, and walking habits helped him draw very meaningful insights. He called out that storing personal data at the minimum provides the benefit of “memory augmentation” – the ability to recollect events, incidents, and actions from data archives as an extension of one’s own memory.
For the more analytically inclined, he was able to show that more meaningful insights could be drawn from personal data. For example, analyzing his email archive helped him realize that most issues at his workplace resolved themselves by the end of the day without his intervention. His intervention would only have resulted in wastage of his time.
Gawande, a surgeon in the US, recommends in his book that people could count something that is of interest to them. He counted (and recorded the data) on how often things were left inside patients after surgery. Things left inside patients included surgical instruments, sponges, etc. Analyzing the data showed Gawande that these incidents were more likely to occur during emergency situations (unexpected complications) in a surgery. This insight allowed him to be better prepared in such situations to avoid these mishaps.
The blog and the book only amplified my interest in the area of Personal Analytics that I had unwittingly gotten interested in many years ago. I had been collecting as much data as I could about my personal finance (bank statements, daily spends, etc.), my daily habits (eating/walking), and my work activities (emails and meetings) for many years. I have 15-20 years of hourly/daily data in some of these areas. The question is – how well have I used this data to draw meaningful insights? Trends in my financial data have helped me make better personal investment decisions.
My work-related data has helped me become more productive at work by allowing me to plan meetings, events and other commitments suitably. I only wish I had applied insights that I have learnt from my eating and walking behaviours over the years.
Reviewing this data archive recently led me back to Wolfram’s blog, which then led me to realize that Personal Analytics is an emerging technology trend as recognized by Gartner in 2016. Personal Analytics is a field of analytics which focuses on allowing people to collect, measure, analyze, and improve using data about themselves. With the pervasive presence of technology in our lives, it is increasingly becoming easy for people to collect and analyze data about themselves. The movement behind this emerging phenomenon is called Quantified Self, with the belief that collecting and analyzing one’s personal data could help one improve one’s health, wealth, social life, family relationships, work productivity, personal productivity, and more.
Just as Gartner’s Analytics Maturity Model (Descriptive, Diagnostic, Predictive, Prescriptive) applies to business analytics, it is quite possible in the near future that we will be able to apply the same model to Personal Analytics. Imagine a personal dashboard (Descriptive) refreshed every day, week, and year that helps you understand the impact of your behaviour and your actions on your health, wealth, social life, and other aspects of your life. Imagine then a visual application that allows you to deep-dive (Diagnostic) into the dashboard to help you understand why your savings dipped over the past year, or why your weight went up over the last month, and so on.
What if you could have automated personal agents (bots) that review your data and provide you alerts (Predictive) to manage your social relationships (“you will miss your daughter’s school performance if you don’t get off work now”) and better still, provide meaningful insights (Prescriptive) to meet your health, wealth, or other personal goals.
If your interest has been piqued with the possibilities of Personal Analytics, here are starter solutions that can help you get started:
- Wolfram Alpha (the brainchild of Stephen Wolfram) provides a Facebook App to draw insights from your social network. Some of the insights you can get are about where your friends are clustered geographically, what do you talk about often on Facebook, when do you use Facebook, and more
- If you know R, then you can use data from your chat tool (Whatsapp, Snapchat, WeChat, etc.) or your bank statements or work archives to analyze and draw insights about your personal, professional, and financial behaviour. Get started here.
With technology getting increasingly embedded in our lives through wearables and the availability of analytical tools, apps and models to mine data from these wearables, it is quite likely that Personal Analytics will be a technology phenomenon in the next 5-10 years. If you are interested in getting a head start, you can begin by collecting data and using simple analytical tools to analyze and draw insights from the data. Here is to a better you – because of Personal Analytics!
Subramanian M S (Mani) currently heads the analytics function at BigBasket, India’s largest online supermarket.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)