How do you quantify your life?
Philosophers and poets have long suggested the benefits of quantification.
In heeding their advice today you can do a lot more than simply measure out your life with coffee spoons, to borrow a line from T.S. Eliot. For instance, millions of consumers now use NikePlus devices to carefully track the miles, speeds, and calories burned during their evening jogs. Nike believes so strongly in the growth and profitability of the "auto-analytic" market that it it's about to launch a "FuelBand" that measures oxygen consumption, motion, steps taken per day, and more.
The recent boom in auto-analytics, or "self-tracking," or "self quantification" has been playing out mostly as a consumer trend — as something we do during our leisure time to become more deliberate about personal fitness, finances, or diet. (See here for an excellent list of 500 auto-analytic tools.) However, there has been little explicit discussion of how auto-analytics might be translated into a business context.
Below are five pointers to frame and guide the conversation for technology geeks and practitioners to champion the use of auto-analytics in their businesses:
Auto-analytics can be understood within the tradition of scientific management. Management science has its roots in experimentation and productivity improvement. Frederick Taylor's early pig-iron experiments measured and improved factory worker performance. Yet embedded in this tradition is the assumption that improving worker productivity is something done by an outsider observer (manager or consultant like Taylor). While this logic is reasonable on the shop floor, it doesn't apply as smoothly to knowledge work, where thought processes are intractably difficult for outsiders to quantify.
Auto-analytics offers a way for knowledge workers themselves to test approaches to productivity improvement without managerial intervention. Here are a few examples:
- Experiments to minimize "wasted time." By using tracking tools like RescueTime, practitioners are able to collect data on how they spend time on various websites and apps. As a result, they can take a more fact-based view toward decisions on where and how to change work routines. For instance, users can choose to compare one productivity-boosting intervention, like blocking access to their favorite online gossip site, with another, like spending 30% more time on a work-related app. (Click here to see what one entrepreneur learned.)
- "Knowledge Workload" forecasting. While business processes use advanced analytics to predict customer demand and production, knowledge workers must guesstimate whether or when they'll have bandwidth for a new project. However, using personal data (emails, calendars, etc) and spreadsheets, some practitioners are developing personal yield management tools. For instance, this worker tried out several experimental models until he found one that effectively predicted his actual workload, as well as the underlying work required on each of his many projects.
- "Physiological systems" experiments. How do changes in nutrition, fitness, or sleep affect your job performance and productivity? Given the sheer number of tools being developed, this might be the rich area for business experimentation. Using a device like Jawbone, workers can develop a more analytical view on sleep quantity and quality. Using this data they can then examine correlations between sleep patterns and personal output (deals closed or lines of code written), and test new alternatives (afternoon naps or waking earlier).
Auto-analytics could increase self-awareness on the front lines of an organization. Thanks to work by Daniel Goleman and others, leaders understand and appreciate the crucial links between leadership performance and self-awareness. Yet, since increasing self-awareness often requires expensive coaching and frequent assessment, it's been a C-suite luxury to develop these links. Auto-analytics can democratize interventions that increase self-awareness. Today, there are scores of technologies that help practitioners automatically (or "passively") collect data to determine how well they are progressing toward their objectives. Many of these technologies build awareness of underlying factors that influence job performance (time use, social behaviors, health, nutrition), while others are designed explicitly for business use. For instance, Rypple is a self-tracking app designed to help employees set goals and monitor progress through quick feedback like peer recognition and a "continuous coaching" function.
Auto-analytics builds on the momentum and current directionality of business IT. Wired's Gary Wolf has noted that 4 trends — mobile phones, social media, cheap electronic sensors, and cloud computing — are making consumer adoption of auto-analytics considerably easier. These four are also currently the focus of managerial attention and significant enterprise IT investment, as Andrew McAfee and others show. Auto-analytics can create new and unexpected ways for companies to capitalize on these trends. Consider KEAS, a tool being tested at companies like Pfizer, Solix, and Novartis. It uses monitoring devices (to collect employee-generated data) and social networking (for hosting employee competitions) to promote employee wellness and productivity.
I would add business analytics as another source of momentum for auto-analytics. Workers are learning to become more analytical in making decisions that affect enterprise-wide performance. So why not extend analytical techniques and technologies to your own job performance? (On this point: particular thanks to my colleague Tom Davenport, with whom I've been discussing this topic for almost a year.)
Auto-analytics can help foster personal CSR and sustainability measurement. Many employees are motivated by the fact that their organization aspires not only to create economic value, but also to cause social or environmental impact. However, individual employees are often left scratching their heads when they try to determine their personal impact. Auto-analytics can be used to "scale down" CSR or sustainability metrics to the individual level. For instance, an employee who embraces her company's commitment to reduce energy consumption by 25% can use tools like Wattzon to track, analyze, and reduce her own energy use while working at home or commuting.
Auto-analytics can be a platform for growth and innovation. Auto-analytics is itself a potential source of revenue growth and entrepreneurial innovation. Many of the auto-analytic tools being developed for large consumer markets, like the $4.5 trillion healthcare market or the $10.5 billion self-help market, started out as prototypes that workers developed for personal use. "Anyone who can code software can write a self-quantification app," notes an R&D executive, "the question is...who can change the business with it?" Thus even managers who aren't auto-analysts can find inspiration by studying colleagues who are using or creating tools-an innovation method that's already being followed at companies like Intel, Philips, and Microsoft.
Which of these tools might help you think and act in more productive ways? Where should your organization start?
This blog first appeared on Harvard Business Review on 1/26/2012.