Creating a Data-Driven Work Experience: Myths and Realities

Steelcase’s Tracy Brower presents seven myths about data and how to make better decisions by bringing more reality to the data-driven decision-making process. 

Data has become a critical component for designing work experiences.

Big data has become big business. Thick data too—that is, qualitative data—has also become prominent, and the combination of the two is an important way we can know more about place, people and the implications for designing the work experience.

With all this focus on data—more data, constant data, all-data-all-the-time—we set ourselves up to miss the forest for the trees, and to make mistakes and be disoriented by myths that can reduce overall effectiveness.

Here are seven myths about data and how to make better decisions by bringing a bit more reality to the data-driven decision-making process. 

Myth One: I’ll believe it when I see it.

Despite its popularity, the phrase “I’ll believe it when I see it” couldn’t be more wrong. According to social psychology research, “I’ll see it when I believe it” is the accurate way to describe our relationship with data, evidence and the way we rationalize our decisions. We tend to believe a perspective and then seek information that supports it. We discount or literally fail to notice data that doesn’t match our own belief system. Believing that open offices negatively influence productivity sets up employees to find all the articles that support this view and avoid articles that present evidence to the contrary. 

In reality, we need to be curious, stay open, and try not to draw conclusions too early in the decision-making process.

Myth Two: Quantitative data is better than qualitative data.

We tend to overvalue quantitative data and undervalue qualitative data. But, both are necessary. Quantitative data can tell us “what” and “how much.” Qualitative data tells us “why.” Mixed methodologies are best in which a mix of quantitative methods (sensor data, survey data, data-base calculations, etc.) and qualitative methods (interviews, focus groups, observation, etc.) are used. From the quantitative data, you’ll know that conference room 2A has the lowest levels of usage or efficiency. But, it’s through interviews with users that you’ll learn it’s because it’s right next to the especially enthusiastic loud-talking team that tends to distract meetings nearby.

In reality, both quantitative and qualitative data are critical to the process of decision-making and the design of a great work experience. 

Myth Three: More data is better.

The more data we can access and generate, the more we tend to put our faith in data—lots of data. But, too much data can overwhelm the process and actually muddy the waters. Providing the just-right amount of data is the better way to inform, tell a story and communicate a decision. Michelangelo famously said his sculptures were already complete within blocks of marble. His role was simply to chisel away the extra material. Data is like this too. When it comes to data, more isn’t better. 

In reality, providing the right data and the right amount of data at the right time is most effective. 

Sharing the right data at the right time is most impactful.

Myth Four: Logical, non-emotional decisions are best.

Despite our preference for logic and a deliberate decision-making process, a new study demonstrates that decisions which factor in feelings—rather than exclude them—tend to be decisions about which people have more certainty. These are the decisions for which people are willing to advocate. In addition, a range of emotions—dubbed emodiveristy—can be useful in developing a more nuanced view of situations. In managing change, it can be especially helpful to provide users with the opportunity to hear each other’s perspectives. Because it’s impossible to create a work experience that meets every single user desire, when users hear multiple points of view, it can give them a new sense of perspective about the range of wants and needs that must be factored into the solution.

In reality, it is most effective to combine both logic and emotion for the best decisions, and to provide opportunities for a range of perspectives to generate the greatest amounts of commitment. 

Myth Five: Accumulated knowledge helps us decide.

We generally trust ourselves and others to consider cumulative facts and knowledge in order to make the best data-driven decisions. But, recent research demonstrates participants regularly committed the error of recency in drawing conclusions. In other words, the most recent data about their conclusions was more important in making decisions than accumulated knowledge and experience. The implication is that regular infusions of data will be helpful to the decision-making process. It is important to continually study the work experience and make ongoing investments to ensure it is keeping up with user needs.

In reality, it won’t be enough to provide data, draw conclusions and assume they will be stable over time. It will be necessary to provide ongoing data to ensure continued commitment to a direction. 

Data can help inform the work experiences we create for people.

Myth Six: Data has its own credibility.

While we like to think of data as objective, we tend to use data to our own ends. In fact, when data meets our current beliefs, we tend to give it more credence. This is called confirmation bias and it gets in the way of our ability to consider dissenting data or that about which we might disagree. Experience is key here. While more data tends to build up to proof, it is experience that will aggregate to belief. It is generally not more data that will convince someone, it is new experiences. If a client is committed to a particular solution—say, enclosed offices as the only way for focused work to be supported—it will be important to provide plenty of alternative experiences in order to shift perspectives. A white paper or a couple examples may not suffice. A range of experiences like visits to other installations with full palettes of place may be the antidote to a rather entrenched opinion.

In reality, we need to actively seek and provide data which doesn’t meet pre-set expectations and provide holistic experiences if we want to shift belief systems. 

Myth Seven: It’s best to be right.

While we strive to be right and feel most comfortable when we are, being wrong is actually a very productive place to be. Learning only happens when we can fail forward and acknowledge when we’re wrong. In addition, being uncertain contributes to creativity and innovation because it allows us to bring in new perspectives and try new things.

In reality, we need to embrace failure as a way to learn and grow. By staying open to all kinds of data, we can watch for moments when we need to adjust our incorrect conclusions and learn something new.

Big data has garnered a lot of attention, and gathering, sifting, assessing, analyzing, synthesizing and presenting so much data is no walk in the woods—at least if we’re doing it right. But, let’s not miss the forest for the trees. Overall, data isn’t useful for its own sake, it must be part of a holistic model for sound decision-making and part of a conscious and intentional process toward good thinking. In turn, we can turn this sound thinking toward designing the best possible experiences for people at work.

Images courtesy of Steelcase.
More from Tracy Brower, PhD, MM, MCRw

Inspiring Spaces to Boost Creative Confidence

As we enter a brand new era – a ‘Fourth Industrial Revolution’...
Read More

1 Comment

  • Excellent review and advice, Tracy. The concept of a data-driven workplace has concerned me for a long time. Most of the data collected about the workplace is about the thing, the space, and not about the activities and organization of work and the fundamental changes to most companies about to take place. That is, most of the data is about utilization of settings that exist, not about the newly-developing activities and the not-yet-invented resources they’ll need.

    I’ve liked Roger Martin’s observation: “Over-reliance on scientific analysis tends to narrow strategic options and shut down innovation. That’s because it’s designed to understand natural phenomena that cannot be changed. It’s not an effective way to evaluate possibilities—things that do not yet exist.”

Leave a Reply

Your email address will not be published. Required fields are marked *