Productizing Data Science for Business Value Creation with Walmart Global Technology

Srujana Kaddevarmuth, Sr. Data Science & Value Creation, Walmart Global Technology

The phenomenal volume and velocity of data generated in this digital world is exponentially increasing. Today, data collected across multiple feeds is heterogenous, complex and nonintuitive. The ultimate objective of collecting, storing, and analyzing data at Walmart is to deliver value to customers, associates, and our business. Value derived from data can be tangible, leading to increased sales and a higher return on investment, or intangible, leading to brand uplift and customer retention. “Productizing” data science is a journey that involves translation of insights obtained from exploratory analysis into scalable models that can power data products. This involves focusing on deploying models into production systems and effectively automating and scaling them. Productizing data science can have many benefits. It can help embed data science across all enterprise products and democratize its applications by placing it in the hands of even nontechnical business users. While productizing data science offers many benefits to organizations, it also presents numerous risks and challenges, like losing resource investments, risk of concept drift and risks of unintended consequences. This talk focuses on the nuances of productizing data science associated benefits and risks and also suggests strategies to overcome these risks.