Why “Know Thy Data” Is a Rallying Cry in Retail - Danielle Crop

More Intelligent Tomorrow: a DataRobot Podcast

Danielle Crop is Chief Data Officer (CDO) at Albertsons Companies, a recent transition from her nine-year term as CDO for American Express. Throughout her career, she has orchestrated big data projects that have exponentially increased customer conversion rates and helped millions of customers make smarter purchasing decisions.

In this episode of More Intelligent Tomorrow, Ben Taylor talks to Danielle about the role of creative design thinking in data, how ethics can help us avoid unconscious bias, and how data science mitigates retail shrinkage. Danielle shares astute observations about what data scientists and business leaders need to learn from one another, why “Know Thy Data” remains the first law of statistical inference, and why you can never replace human relationships in data science.

Understanding Business Problems

85% of AI projects fail today, arguably due to a lack of understanding how an AI project should be driving toward business value. Yet often data science teams are far-removed from business teams. Danielle works to enable her data scientist teams to fully understand how their company makes money and what they can do to help their company make money.

“If you don't understand the problem, you can't solve it. You have to enable that understanding for data scientists. It's about creating an opportunity to learn about the business. I make sure that my teams get into the stores and actually work there. ”

Retail is very complex, both in the way that it makes money and in the way that it needs to be managed. There are a lot of people involved and there is a lot of room for error. Knowing how data is created and used, where it comes from, and how it can be used to optimize value can make or break the success of any AI project.

“Know Thy Data is the first law of statistical inference. You have to know where your data comes from.”

Shrink occurs throughout the retail supply chain and illustrates this complexity. Perishables expire, purchases are returned, or items arrive damaged from a vendor: this “entropy of retail” is extremely difficult to forecast and control. Having an intimate understanding of data sources and data gathering techniques has been shown to help control this endemic problem.

In the end, retail is about people. Data science can be used in retail to help customers make informed, contextual choices at the point of a purchasing decision. For example, there is a lot of data available on the link between what you eat and how long you live. Making that data actionable could change peoples’ lives.

Listen to this episode of More Intelligent Tomorrow to learn:

• How business value thinking can improve AI project outcomes
• The important distinction between the discovery aspect and the modeling aspect of data
• How ethics can tackle the problem of unconscious bias in data science
• How data science is used to mitigate the “entropy of retail”
• How AI can help people make better buying decisions