Beyond Numbers: The Importance Of Human Understanding In A Data-Driven World

• While data and automation are valuable tools, they may not always provide the nuanced understanding necessary for effective business decisions. 
• There’s a need to balance the reliance on data and technology with human understanding in business decisions. Technological tools can provide useful information about usage patterns and preferences, but they fall short in explaining why people behave the way they do. 
• Understanding these motivations requires direct interaction and conversation, which can lead to more comprehensive insights about the workplace and customer experience.

Is data the answer to all our problems? What happens if/when we call a timeout and raise the basic question: Are we putting our hopes in the wrong direction?

First, I don’t dismiss the importance of data. Nor of Big Data. Of course, having lots of information to inform our decisions is important. It would be naive to suggest otherwise.

However, as I discuss here, the rush to turn everything into “an app” and to automate as many aspects of life (inside and outside business) as possible, we run the risk of losing sight of understanding.

With respect to purely objective data, such as how the sensors on wind turbines measure wind speed, humidity, and temperature to regulate the rotor blades’ movements, such data is pure gold. There are no human assumptions, judgments, or interpretations to corrupt the data. In the world of business, which is populated with humans rather than rotor blades, the story gets a bit more complicated.

Understanding Customers (and Innovation)

In a recent interview with the Danish innovation agency ReD Associates, management guru Roger Martin (one of the world’s leading voices on corporate innovation) offers an interesting entry point for this conversation. In the interview he talks about how companies develop strategies for innovation, and how they go about understanding not only what customers in the future may want, but why. The why of human behavior is where Big Data and machine learning can come up short.

In search of technocratic solutions, Martin suggests, business leaders often operate in an illusion of algorithmic predictability. In their desire to quantify exactly what customers feel, they send “out a statistically significant survey with quantitative measurements so they can add everything up.” If you start with a “stupid question,” Martin continues, “you get a statistically significant stupid answer.”

As one of Harvard Business Review’s most prolific authors, Martin is worth quoting at length.

“If you gave me a choice of having a CEO go out and do 10 one-hour interviews of customers versus a survey that goes out to 10,000 customers, I would take the former everyday. If you outsource it to a market research firm you just get garbage, statistically significant, unbiased garbage.”

Understanding Employees (and Culture)

The same dynamics apply to efforts to understand employees as well. Culture surveys of various sorts make the same algorithmic claims to validity and reliability, as if they fully understand employee experience. Adding up the answers to a widely distributed survey, the answers to which are somehow magically converted into “scientific data,”  may provide leaders with a sense of control. But that too is an illusion.

Despite all the corporate efforts at measuring and changing company culture, businesses are horrible at culture. I have discussed this in a previous Allwork.Space article and podcast. 

In order to understand what employees are experiencing and what they need and want, we should heed Roger Martin’s advice and talk with them. The anthropological research method of ethnography does just this. Ethnographers go out into the field (the company), and spend time with and talk with employees as they go through their day-to-day work.

A Time and Place

There is, as the expression goes, a time and place for everything. Purely objective data regarding inanimate objects and elements of the natural world can be relatively easily understood by, as Martin puts it, adding up the numbers. But humans, not so much.

In our rush to quantify and monetize everything, aided by the relentless energy of investors looking for ever-larger returns and dividends, something essentially human in business can be easily lost. And that is understanding.

This plays out in important ways in discussions and consulting projects in the workplace. Prop tech apps, beacons, badges, and sensors can tell us a lot about utilization and what spaces people are using in our buildings. Again, this is very useful information. But why people do the things they do is not predictable or necessarily amenable to algorithmic answers and solutions.

Following Martin, perhaps we should go out there and ask them why? If we do that, we may understand more about employee experience and the workplace than all the data in the world.