The Data-Driven Workplace: Developers and IT Workers Embrace Data

data-driven workplace: data reflected in sunglassesIn an increasingly data-driven business world, is there such a thing as too much information? Maybe, but it might be more accurate to say there are some misguided uses of data in the workplace. For example, according to The Independent, one employer allegedly used wearable devices—originally intended to improve operational efficiency in its warehouse—to keep tabs on the bathroom habits of its workers. That’s probably not what anyone had in mind when talking about the “Quantified Self.”

But while the specter of “Big Brother” tracking has loomed in the workplace since long before the current wave of interest in data analytics, it turns out that the vast majority of workers actually welcome data into their jobs.

A recent New Relic survey asked the question:

If you were considering a new job and a company and the department you were going to join had a culture of using data to prioritize work, build and adjust feedback loops in a logical way, and holding team members and other groups accountable, would the prospect of working there be…

data-driven workplace chart

At total of 87% of respondents said they were excited by or even expect to work in that kind of environment. Only 11% said they were unsure, while just 2% said they’d find such an environment “distasteful.” That’s a pretty ringing endorsement of using more data in the workplace, but what’s behind the disconnect between the conventional wisdom and what our survey respondents said?

Using data is not the same as spying

People have long debated the merits, or lack thereof, of companies monitoring their employees’ email and Web use, for instance—a discussion that continues today on sites like InformationWeek. In a somewhat different twist on email monitoring, messages between employees at Bloomberg now include the time the sender arrived in the office, according to a recent New York Times profile. The timestamp is likely intended to motivate early arrivals, but may rub people the wrong way without the context of other outcome-related data tracked within the company.

Such tactics and the discussions around them are inevitable in an ever-connected world of ubiquitous mobile devices, social media, geo-location tracking, and growing interest in wearable technologies and the Internet of Things. But done right, a culture of “using data to prioritize work, build and adjust feedback loops in a logical way, and holding team members and other groups accountable” can and should engender smart processes and decisions, productivity, and efficiency—not erode trust and morale.

Fortunately, that seems to be the viewpoint of many technology professionals. Almost two-thirds (63%) of the New Relic survey respondents worked in a Web-, mobile-, or software-native business. So how do you make sure you get positive results and don’t veer into the dark side of “people analytics?”

Expert advice

For starters, don’t hide the fact that you’re employing data to make decisions; instead you should celebrate it. If it’s not something management is comfortable discussing—timing how long it takes someone to use the bathroom, for example—it probably won’t have productive outcomes. Here are three other key ideas for making productive, thoughtful use of data in the workplace:

  1. Use data to match strengths with business needs. Matching the right people to the right jobs and projects is a principle with new currency in the data-driven workplace. Ryan Fuller, CEO of people analytics company VoloMetrix, says data can help executives better understand their team members’ real passions, according to Entrepreneur. Using that data to direct people to the projects and tasks where they’re most likely to be most productive—and least distracted—is good for everyone.
  1. Do not trust data without context. Humans tend to over-ascribe behavior to the person while discounting their situation. In the Harvard Business Review, organizational development consultant Ben Dattner calls this social psychology principle of “fundamental attribution error” the “fundamental analytic error” of people analytics. Instead of settling on the easiest explanation—typically derived from management perspectives—Dattner advises looking for an objective viewpoint. “The most valuable human capital or people analytic initiatives get deployed in a scientific manner,” he writes.
  1. Foster productivity, not policing. Enkata CMO Dan Enthoven concurs in Inc. that people crave self-measurement—just not creepy self-measurement. As you consider the data your business measures about its people, applications, processes, and so forth, ensure you’re making sensible distinctions between intelligent quantification and unnecessary intrusion. “Even hardworking people check Facebook once in a while,” Enthoven writes.
  1. Use data to enable self-service feedback. As noted above, people want feedback on the quality and impact of their work. In vocations like software development, giving engineers easy access to metrics that illuminate the performance, adoption, and business results of the software they create can help improve software quality. For example,, a major source of U.S. real estate listings, used data to help drive down its production error rates in software deployments by 82% in six months.
  1. Use data to cut across functional silos. Functional groups like sales and marketing, development, IT operations, and many others must work together to be successful. When these groups interact with each other, using data can remove the emotion from the decision-making process and help each group hold the others accountable. When done well, data-driven meetings can turn from finger-pointing sessions to discussions over live dashboards.

Most people really do want to work in data-driven environments. They just want the right data used for the right reasons.

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Abner Germanow was formerly Senior Director of Partner Marketing & Evangelism at New Relic. View posts by .

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