Lessons on data analytics from a tween
I recently received a note from my 11 year old son’s maths tutor, which read:
“Jack will be covering Data, specifically: collecting categorical and numerical data thru observations and surveys, Constructing data displays including dot plots, column graphs and line graphs, naming and labelling vertical and horizontal axes, Using scale to determine placement of each point when drawing a line graph.”
“Aha! Welcome to the world of Business Intelligence!” I thought with a smile. Excitedly, I introduced Jack to my veritable library of books on the subject of charting and data visualization, including of course, all my favourites such as “Signal: Understanding What Matters in a World of Noise” by Stephen Few, “The Wall Street Guide to Information Graphics (the do’s and don’ts of presenting data, facts and figures)”, “Data Points: Visualization That Means Something” and many others. With no words and a tweenage look of disdain, he resumed his game of Roblox (Minecraft was no longer “cool” since it’s acquisition by Microsoft, I was off-handedly informed…)
Then, almost in passing, Jack mused: “I wonder how the number of Roblox users by year would look in a line chart…?”
Challenge accepted. A quick Google search, drop the data into a self-service data visualization tool, and we had our answer.
What was quite interesting to me was that he was thinking in terms of the data, wanting to answer a question empirically, and wanting to see the data visually to interpret the results. Now, as I listen to my son in the background, collaborating in real-time with one of his school friends in multi-player Roblox while they chat and interact simultaneously through Facetime, it’s occurs to me that future generations of ‘knowledge worker’ are likely to be much more collaborative and team-oriented, much more visual and data savvy, much less inclined to accept decision-making which cannot be supported by data. Which, of course, reminds me of a classic Dilbert moment:
Dilbert ©2018, Universal Uclick
Couple the inquisitive, knowledge-hungry mindset of our younger generations with the relentless pace of change and innovation, and we can anticipate that our future business users will not be tolerant of poor decision-making or slow time to action caused by the outdated insights provided by traditional analytics infrastructures. Today more than ever, analytics is a key to business success, but it needs simplicity, real-time speed and security: how fast can a user access and blend all the disparate data they need, analyze and share it, then take action, all in a secure and controlled environment? How can we provide solutions which enrich data with context, to build consensus? How can we empowering teams with the right data, providing machine learning-driven insights and personalization? Cognitive, collaborative analytics helps teams take action in real-time, to work on the right things at the right time. Our future decision-makers will expect nothing less.
Footnote – 10 of my favourite books on Data Visualization and Presentation:
- Signal: Understanding What Matters in a World of Noise – Stephen Few
- Now You See It: Simple Visualization Techniques for Quantitative Analysis – Stephen Few
- Data Points: Visualization That Means Something – Nathan Yau
- Presentation Zen: Simple Ideas on Presentation Design and Delivery – Garr Reynolds
- The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures – Dona M. Wong
- Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations – Isabel Meirelles
- Visualize This: The FlowingData Guide to Design, Visualization, and Statistics – Nathan Yau
- The Visual Display of Quantitative Information – Edward R. Tufte
- Show Me the Numbers: Designing Tables and Graphs to Enlighten – Stephen Few
- Storytelling with Data: A Data Visualization Guide for Business Professionals – Cole Nussbaumer Knaflic