These algorithms can get complicated and do require strong technical skills. But it is important to not get too bogged down in the technology. After all, the focus is to find ways to use machine learning to accomplish clear objectives.
Again, Stich Fix is a good place to get guidance on this. In the November issue of the Harvard Business Review, the company’s chief algorithms officer, Eric Colson, published an article, “Curiosity-Driven Data Science.”17 In it, he provided his experiences in creating a data-driven organization.
At the heart of this is allowing data scientists to explore new ideas, concepts, and approaches. This has resulted in AI being implemented across core functions of the business like inventory management, relationship management, logistics, and merchandise buying. It has been transformative, making the organization more agile and streamlined. Colson also believes it has provided “a protective barrier against competition.”
His article also provides other helpful advice for data analysis:
- Data Scientists: They should not be part of another department. Rather, they should have their own, which reports directly to the CEO. This helps with focusing on key priorities as well as having a holistic view of the needs of the organization.
- Experiments: When a data scientist has a new idea, it should be tested on a small sample of customers. If there is traction, then it can be rolled out to the rest of the base.
- Resources: Data scientists need full access to data and tools. There should also be ongoing training.
- Generalists: Hire data scientists who span different domains like modelling, machine learning, and analytics (Colson refers to these people as “full-stack data scientists”). This leads to small teams—which are often more efficient and productive.
- Culture: Colson looks for values like “learning by doing, being comfortable with ambiguity, balancing long-and short-term returns.”
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