Hubris and the Data Scientist
This is a worrying attitude, and I can only hope that those who hold it realise the error of their ways before they make a catastrophic mistake that adversely affects the rest of us.
Data scientists are an increasingly capable bunch, and the tools at their disposal sometimes appear almost magical in their capability to derive insight. Competitions such as those run by Kaggle (more on them in a moment) clearly show that an aptitude for numbers and analysis can deliver some remarkable results, even when that analysis is being undertaken by individuals who lack specific domain expertise.
But to suggest that simply “letting the numbers speak for themselves” is an effective way to make real decisions is, quite simply, bonkers. Data is merely one input to an effective decision making process. Prior knowledge, policy considerations, and an awareness of experimental bias, sampling error, and quaint notions such as ground truth continue to play a fundamental part.
Data scientists undeniably bring a wealth of skills to the table, but so do domain specialists. The domain specialists would be unwise to presume that they can continue to keep pace with exploding data volumes without judicious application of data science. But for data scientists to presume, even for a moment, that they and their algorithms can replace domain expertise is laughable.
Moving forward, we need both domain skills and data skills. Sometimes those skills may be present within a single individual, especially as practitioners within more data-intensive domains equip themselves with the skills required to continue functioning as data volumes blossom. At other times, they will be brought together in the makeup of a team that comprises domain experts and data scientists. It remains unclear whether it would normally be quicker or easier to teach a domain specialist data skills, or vice versa.
One is not ‘better’ than the other, and instead we need to concentrate on finding ways to make it as easy as possible for both groups to work together. How, for example, do we set about ensuring that conflicting use of superficially ‘obvious’ technical terms does not derail the process from the outset? How do we package and convey deep-seated presumptions from one group to the other, and how do we create the common space within which number-cruncher and specialist can work together?
“One of the conclusions reached was that, when a problem is well-structured (or to Drew Conway’s point, when a good question is posed), it is much easier for machine learning to succeed. Kaggle’s strength as a contest platform is that domain experts have already framed the problem: they choose the features of the data to use (feature engineering or “feature creation”, as Monica Rogati calls it) as well as the criteria for success. This is the first, hardest step in any data science project. After this, machine learners can step in and develop the best algorithms for classifying and predicting new data (or, less usefully, explaining old data).”
In responding to Brockmeier’s post, Strata co-chair Alistair Croll also makes an important point:
“Of course, understanding which data to apply to a problem, and when to listen to the numbers, is a nuanced thing.
One thing about data is that it often has non-obvious, and disruptive, nuggets within it that threaten the status quo. And many ‘domain experts’ thrive on their political skills rather than their actual results. So part of the debate is really about housecleaning to replace anecdote with evidence—an uncomfortable cultural shift.”
Data science — and the data scientist — are here to stay, and they bring tremendous value with them. But they’re an adjunct to domain knowledge, not a replacement for it.
(Note: Opinions expressed in this article and its replies are the opinions of their respective authors and not those of DZone, Inc.)