Jake Porway who was a data scientist at the New York Times R&D labs has a great perspective on why multi-disciplinary teams are important to avoid bias and bring in different perspective in data analysis. He discusses a story where data gathered by Über in Oakland suggested that prostitution arrests increased in Oakland on Wednesdays but increased arrests necessarily didn’t imply increased crime. He also outlines the data analysis done by Grameen Foundation where the analysis of Ugandan farm workers could result into the farmers being “good” or “bad” depending on which perspective you would consider. This story validates one more attribute of my point of view regarding data scientists – data scientists should be design thinkers. Working in a multi-disciplinary team to let people champion their perspective is one of the core tenants of design thinking.
One of the viewpoints of Jake that I don’t agree with:
“Any data scientist worth their salary will tell you that you should start with a question, NOT the data.”
In many cases you don’t even know what question to ask. Sometimes an anomaly or a pattern in data tells a story. This story informs us what questions we might ask. I do see that many data scientists start with knowing a question ahead of time and then pull in necessary data they need but I advocate the other side where you bring in the sources and let the data tell you a story. Referring to design, Henry Ford once said, “”Every object tells a story if you know how to read it.” Listen to the data—a story—without any pre-conceived bias and see where it leads you.
You can only ask what you know to ask. It limits your ability to unearth groundbreaking insights. Chasing a perfect answer to a perfect question is a trap that many data scientists fall into. In reality what business wants is to get to a good enough answer to a question or insight that is actionable. In most cases getting to an answer that is 95% accurate requires little effort but getting that rest 5% requires exponentially disproportionate time with disproportionately low return.
Thrive for precision, not accuracy. The first answer could really be of low precision. It’s perfectly acceptable as long as you know what the precision is and you can continuously refine it to make it good enough. Being able to rapidly iterate and reframe the question is far more important than knowing upfront what question to ask; data analysis is a journey and not a step in the process.
Photo credit: Mario Klingemann