Adding Human Insights to Data Analysis

We are living in the age of big data. From making business decisions such as whether we should place 50,000 or 100,000 orders of floral skirts for spring, to figuring out which restaurant has the best pasta in an unfamiliar city, we are instantly able to access data to help us predict outcomes, gain clarity, or inform decision-making. In fact, we are sometimes unable to make decisions without consulting data.

While data is a powerful tool, data can also be deceiving when it is used without context.

Have you ever been in a situation where data is directing you down one avenue, but your gut is telling to take a different direction?

Learning from Nokia’s failure
Tricia Wang, a Technology Ethnographer, shared in her fascinating TED Talk why so many companies make bad decisions, even with access to unprecedented amounts of data.

What I found interesting was when she mentioned how big data is a $122 billion-dollar industry, yet 73% of big data projects are not profitable. “I have executives coming up to me saying – ‘… we invested in some big-data system and our employees aren’t making better decisions, and are certainly not coming up with more break-through ideas.’

So why is that? In the context of Nokia’s notoriously bad decision not to pursue smartphones, Wang shares the insights she gained through her work and research on the informal economy in China.  Personal connections and personal observations led Wang to believe unequivocally that smartphones were the future.  Nokia, however, was not interested in ‘observations’ outside the traditional big-data realm, and passed on her insights. We all know what happened to Nokia as smartphone sales skyrocketed beginning in 2010…

Big data vs. thick data
Data that comes from our own observations, and the personal experiences of those around us, is what Wang calls “thick data.”

I think there are valuable lessons for those of us in sustainability when we think about big data vs. thick data. While our decisions may not (yet) lead to catastrophic failure on the scale of this Nokia example, where the company completely missed out on a key market, I’m sure you can relate to the times where you’ve seen organizations oversimplify certain goals or chase after the wrong targets because they relied only on big-picture aggregate data.  Missing out on the insights that come from less quantifiable sources, such as the experiences and learnings of our peers and colleagues, can still hold us back.  

How can we help
My colleagues and I really connected with Wang’s example of the Oracle in ancient Greece, a person who provided information on life’s big questions—in theory through direct contact with a valuable data source (the Gods). While people came to get answers from the Oracle, the complete interpretation didn’t come from her alone. The Oracle was supported by temple guides who would observe, ask the inquisitors follow-up questions, and help decipher and provide context to the Oracle’s babblings. Wang emphasized how the Oracle didn’t stand alone, and neither should our big data systems.

At GoBlu, we are here to help weigh in, dissect and provide context to the data you might be working with. Take water as an example: It might seem easy to stamp a percentage reduction goal on water usage in your supply chain, but then comes context.  We think our role is similar that of the guides above, and our aim is to help apply the thick data that is relevant to your individual situation.  We would dive into the particulars, such as: What is the broader environmental situation in your production region? Is your supply chain primarily in a water-stressed region, and do the operations contribute to this? How efficient are the current operations? Would reduction goals inadvertently increase pollution loads? What is the water source, and how is it extracted, used, and re-released? Might recycling rate be more relevant than consumption rate?

We are here to bring clarity to the context and enable more informed decisions. Reach out to me or one of our team members and get expert insights or advice on water, energy, chemicals, or other sustainability matters.

Watch Tricia Wang’s Ted talk “The human insights missing from big datahere

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About the author: Claire project managed supply chain sustainability projects at both H&M and The North Face, focusing on water, chemicals, eco materials and animal welfare. Claire brings a deep understanding of textiles design, production and processing, particularly in China, Bangladesh and India. A designer at heart, Claire enjoys streamlining business processes and designing smart approaches that maximize resource efficiency, from lean manufacturing to green chemistry. Claire holds a BA in Fashion Design, a Masters in Global Fashion Supply Chain management and speaks fluent Cantonese Chinese as well as English.