Digital Transformation and Agile Decision-Making in Commerce

Dr. Kanishka Bhattacharya spoke at ODSC London 2018 on how data science is changing commerce and enabling large companies to become more “agile” in their decision process.

Dr. Bhattacharya, who works as a senior director at Publicis.Sapient, explained that large corporations with ‘traditional’ business models are failing to meet customers’ expectations for service and convenience because of a reluctance to embrace advances in how data is managed and exploited. In his telling, many such companies are resistant to major overhauls in their business model because of preferences in upper management towards traditional approaches.

[Related Article: 5 Ways AI Can Upgrade Your eCommerce Business]

Dr. Bhattacharya explained, “If you’ve got people right at the top of a large bank getting their multi-millions pounds paychecks, they believe they’re very good. And here’s the problem: they’ve been very successful. When you’re very successful, it’s very hard for someone to just turn up and say ‘you know what? You should do things differently.’”

“That makes it very hard for them to do things in a more agile manner. That makes it very hard for them to take those larger risks and make those very strategic investments that need to be made from time to time in order to radically move on,” he said.

As evidence of the widening gap between tech-driven organizations and traditional businesses, Dr. Bhattacharya pointed to the fact that ‘brick and mortar’ storefronts, such as Walmart and Exxon, have been supplanted by tech giants over the past two decades as the highest-valued companies on international stock markets. In 2001, the five highest-valued corporations on the Fortune 500 included three automakers, along with Exxon and Wal-Mart. As of Q2 2018, all five were tech companies: Facebook, Alphabet (Google’s parent company), Microsoft, Apple, and Amazon.

Dr. Bhattacharya explained that the massive infrastructure that underlies major corporations can be hobbled by a “stranglehold” on data moving through an organization because of concerns about leaks and data security. In one such case, Dr. Bhattacharya had to wait 14 months before his employer at the time was able to provision him with data he needed to move forward on a project. In his telling, this is relatively common; big banks are struggling to work with the massive datasets they have on reserve, while smaller startups are capable of experimenting cheaply, even though they lack the same resources as their larger counterparts. The ideal combination, he claimed, would be a niche startup capable of working with the sorts of massive collections that banks store.

[Related Article: How Artificial Intelligence Can Reduce Stress From eCommerce Pricing]

To get to that point, larger corporations must acquire talent capable of addressing a huge range of needs that enable a corporation to exploit their data to the fullest. “You’ve got a bit of a complex world out here. You need quite a lot of different skill sets. The art or the science, if you like, is to be able to pull that together and deliver very tangible, concrete solutions in an iterative manner,” he said. According to Dr. Bhattacharya’s prescription, the solutions he envisions would need to draw from a pool of talented employees that would include strategists, artificial intelligence experts, data scientists, big data engineers, visualization experts and more.

The ability of major corporations to accelerate their business using data analysis can be both helped and inhibited by their ability to access and exploit their data in the first place. To find out more, listen to Dr. Bhattacharya’s full talk on YouTube.