There have been many articles written about the danger of putting data science tools in the hands of analysts. Warnings have included concerns that reckless use of the “black box” of machine learning could lead to financial ruin or some other terrible consequence for the business.
So is the fear warranted? Have we really seen some incredible damage done by analysts using these modern techniques?
First, a Bit of History
Technological advancements that apply mathematics and provide automation to analyses are certainly not new. I can only imagine the conversation as calculators became available for people to use in business. Were there people suggesting that providing a calculator to someone who used an abacus, or who performed math with paper and pencil, would lead to devastating results? If someone used a calculator but didn’t understand the complexities of all the algorithms, would they perform better or worse than the trained expert with the abacus?
My experience in data science and watching technology applied to different domains would suggest that the use of these tools has reduced errors and improved the accuracy of the results, hence why adoption has been so widespread. Interestingly, the safety net to prevent errors has remained the same. The failsafe is not the math and science expert, but the domain expert who can look at the results and understand if they make sense in the context of the subject matter.
But I’ve Heard Stories of Huge Issues with Machine Learning and Data Science!
Certainly, mistakes have been made with bad models, incorrect conclusions and even typos causing impacts to businesses. The reality is that these types of mistakes have been occurring for decades. I have personally seen numerous examples where a complex spreadsheet was rebuilt using modern technology with an Alteryx workflow and the answers didn’t agree. Every time I have seen this, the newer technology provided the correct answer.
While I’m sure someone can find an example that went the other way, I’m certain that you are less likely to make a mistake using the more modern approach to solving complex analytic processes than by using technology from two decades ago.
Will mistakes be made? Certainly, the art and science of analytics is still human powered, and humans make mistakes. However, I’d want to equip my analysts with the best possible tools for the journey.
So What is Happening?
New technologies that allow analysts to learn and leverage Machine Learning (ML) and advanced modeling techniques have become easy to use and readily available. The challenge is quickly becoming cultural versus technological, and this obstacle is widely recognized.