Innovative Workplace Design Starts By Asking The Right Questions

August 14, 2019 by Catherine Porth

Haven’t taken our survey yet? You can still take it here! Read on for part two of a deeper dive into the results and make sure to check out the overview here and part one here.


In the first two articles, we discussed that the dominance of digital screens in our work and private lives is already “here and now” as opposed to being for the “future workplace. We also explained that most screen-enhanced workspaces can be considered unique because the function of those spaces is truly in the eyes of the beholder – or in this case – worker.

In this article, we want to share the strategy we take when designing workplace design surveys. Specifically, why we ask the questions that we do and the insights we’re able to capture by taking an indirect questioning approach.

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Connect the dots to see the real picture hiding in feedback

April 25, 2017 by Dr. Jian Huang

So, there is a lot of data at our disposal these days. Lots of it. And along with all this data often comes the feelings of “I am not getting what I need from the data” or “it’s just a lot of wasted time and effort”.

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Using data + psychology to unlock people’s true opinions

April 13, 2017 by Dr. Jian Huang

Good feedback, a willingness to understand, a desire to improve … These altogether lead to meaningful actions and positive changes.

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3 costly misconceptions about hiring data talent

March 1, 2017 by Dr. Jian Huang

Data jobs are “the sexiest jobs this century”. Data jobs are also among the hardest to positions to fill. The qualifications are so high that candidates have the leverage to “name their own price.”

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Why is a data-enabled business better, and how do you build one?

January 17, 2017 by Dr. Jian Huang

Deep learning was prominent in the venture capital world of 2016 and rightfully so. This wave of excitement about AI and computing grew strong, because of a new-found comfort on letting unprecedented rich data guide progress. Interestingly, the term “deep learning” draws another contrast, that is, previous generations of machine learning lacked support of real data — in other words they were “shallow”.

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