Human Factors Engineering: Big Data & Social Analytics to #MakeTechHuman
“Netflix’s analytical orientation has already led to a high level of success and growth. But the company is also counting on analytics to drive it through a major technological shift […] by analytics we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions an actions”. Competing on Analytics by Thomas H. Davenport and Jeanne G. Harris.
“Big data changes the nature of business, markets, and society […] the effects on individuals may be the biggest shock of all […] this will force an adjustment to traditional ideas of management, decision making, human resources and education”. Big Data by Viktor Mayer-Schonberger and Kenneth Cukier.
“Social physics functions by analyzing patters of human experience and idea exchange within the digital breadcrumbs we all leave behind as we move through the world […] the process of analyzing the patterns is called reality mining […] one of the ten technologies that will change the world [according to MIT Technology Review]”. Social Physics by Alex Pentland.
It’s Saturday night and I am happy to share that I just submitted my last two Jupyter notebooks and, therefore, completed MIT’s first certificate course on Big Data and Social Analytics.
This was one intensive summer with very little time left for anything else beyond work, day-to-day family life and spending most evenings and weekends studying. MIT BD&SA course developers estimated a weekly workload of 8 to 12 hours through 9 weeks. Though, many of us have spent north of 15 hours a week to cover: videos and readings, Python programming and written assignments, quizzes, and forum discussions. By the way, all definitely worthwhile.
While taking this course, I couldn’t help recalling the kind of scarce data we used to work with when I got my postgrad on Human Factors Engineering at BarcelonaTech in the early 90s, also graduating with the first class.
By means of an example, one of the industrial ergonomics projects got kicked off with statistical data provided by the military. Stats on Marines fit for service being the only readily available physiological data for us to design a local civilian application. We knew that wasn’t a representative model of the target user base for the industrial workstation under design. Back then, undertaking a proper data collection study was costly and beyond project means.
Our group worked with small data by testing things on ourselves and leveraging in-house dogfooding to some extent. Though, unfortunately, this kind of findings might not adequately reflect the reality of human variability. If overlooked, that can result on implementing designs that optimize for a set of “proficient some” while undermining ease of use for many others and missing the mark in the process. Let’s keep in mind that, as clearly outlined in Crossing the Chasm, early success among devoted early adopters might not translate in mainstream praise and popularity, then failing to grow the user base and failing in the market.
To be clear, working with secondary research (e.g. reference data sets from third parties) and conducting primary research by testing things on ourselves coupled with in-house dogfooding are all valuable practices. Though not necessarily enough to make a compelling difference in today’s “big data” day and age.
MIT BD&SA discusses the benefits of working with living labs driven by UCD, User Centered Design. We now have commercial off-the-shelf technologies (smartphones, Internet of Things, sensing networks, machine learning) at our disposal, which allow us to capture user actions and behavior on location and, most importantly, with greater data resolution.
Couple that with ethnographic research focusing on understanding human factors by observing users in their own environment and usage context and, most importantly, capturing their PoV, Point of View at each step.
So, those of us working on Human Factors Engineering and driven by User Centered Design to deliver processes, tools, products and services, can create new experiences that take the human possibilities of technologies to new unprecedented levels, analytics becoming of the essence to #MakeTechHuman.
Big Data Revolution. TED Radio Hour. NPR.
The Human Face of Big Data. PBS.
Source: Business Innovation Demands Accelerated Insights. Intel.
See you at RecSys 2016 next week : )