Arguably, COVID-19 has done for retail sector data strategy what the Global Financial Crash did for financial services data and analytics. The past 18 months have seen accelerated investment in digital, data and analytics capabilities across the sector. And this year’s CDAO Fall Virtual summit highlighted what this has meant for the data maturity of many of America’s top retail brands.
“It wouldn’t be a stretch to say [data is] the biggest asset of eBay,” said Ishita Majumdar, VP Data Analytics Platforms at eBay. “Data is what empowers our decisions, our insights, our analytics and, most importantly, data helps personalize your buying experience.”
However, speakers at the event said advancing data-driven business transformations takes time. For companies such as eBay and Domino’s, that are harnessing their data assets to differentiate themselves from their competitors, this success is the result of years of planning and investment.
“In my personal experience, you need to invest and make [data] culture happen,” noted Frederique De Letter, Director, Enterprise Data Intelligence at Domino’s. “All of us have to educate a lot of the organization. All of us have to identify what our relevant use cases [are that] can actually drive value. It takes a lot of time.”
Data democratization has become a hot topic in recent years. Increasingly, enterprises want to empower non-data staff to use data-driven insights and embed data-driven business practices across their whole organizations.
For many companies, data democratization initiatives start with delivering programs to improve the data literacy of non-technical staff. But in this week’s Business of Data podcast, eBay’s VP, Data Analytics Platform, Ishita Majumdar, shares how this alone has not been sufficient to entrench data-driven business practices at the e-commerce giant.
As many companies do in the early stages of data transformation, eBay established an internal analytics university. It provides a series of courses taught by Majumdar’s team. But over time, it became clear this academy was not driving change at scale.
“Everyone attended the classes and ticked all the boxes,” Majumdar explains. “But they were also saying the product manager’s job [for example] is so complicated that, if they must make the time to write these very complex SQL queries, it becomes a two-person job. That’s when I suggested we take the data where the user is.”