Data Scientists Need ‘Cynicism’ to Address ‘Why’ and ‘Who’ Issues

Why do data scientists need cynicism? Cynicism increases suspicion which is a must!

Good data science comes from a state of precarious balance between two diametrically opposed traits, skepticism, and creativity. Fall too far toward one pole and your work stagnates, paralyzed by uncertainty. Too far in the other direction and you waste resources chasing rainbows. As we all know Cynicism increases suspicion, which prevents cooperation. If that is true, cynicism should not be harmful in places where a high degree of skepticism is justified. On that note, it is pretty obvious that data scientists need cynicism.

As our society becomes more reliant on data-driven insights, people are shifting careers and going back to school to pursue positions in data science. This influx of new candidates, and the increase in available positions, creates a situation where hiring managers aren't certain of what to look for and new data scientists aren't certain where they should focus their training. While knowledge base and proficiencies are important metrics, good candidates can learn on the job to fill in knowledge deficiencies, and, more importantly, there is a large gap between knowing facts and the ability to apply these effectively. It is being found that a better characterization of a data scientist's innate potential is the way they balance creativity and skepticism.

Data scientists should rarely take an important analysis at face value. They should almost always dig into the data and develop a deeper understanding of the hidden insights that lie within. Sometimes there are real gems awaiting discovery. Other times the data contain some truly snarky beasts and failing to spot them soon enough presages real danger.

It is too easy to be seduced by good news. If something looks too good to be true, it probably is. So being skeptical — very skeptical is an important trait. It is required to ensure that important results hold up to a deeper look and, if they do not, get the full explanation.

Data Scientists Need to be Creative

If creativity is the solution to problem-solving, cynicism is the motivating factor. You can't solve a problem unless you realize the problem first exists; the only way to innovate your field is to identify the current deficiencies. Being skeptical of others' work is a good place to start, but great data scientists should be the most critical and skeptical of their own hypotheses, models, and findings. Perhaps the biggest risk of any data science project is trusting results without thorough and robust interrogation; there are countless ways to get acceptable model accuracy from a completely worthless model. The same factors leading to creativity should naturally lead to skepticism, seeing all the confounders, conflicting hypotheses, and potential sources of error within the pipeline and code base. Trust is the biggest trap of modern data science; data scientists should constantly ask, "How am I being fooled by this result and how can I test that?".

A Mixture of Creativity and Cynicism

The truly great data scientist exhibits both of these traits in balance. Real-world data science applications are constrained by time, money, and limited data availability, often with very messy data. Innovation, therefore, requires a creative solution to overcome these challenges given the unique constraints of each project and dataset. Solutions need to be innovative but also practical, explainable, and succinct. Focusing creativity with skepticism ensures a practical solution that solves not only the data science challenge but also the problem of making it work in the real world.