How Danske Bank mastered data analytics
Global head of analytics urged brands to start advocating AI and develop proof of concepts to avoid overhype around the technology
Denmark-based Danske Bank Group is using a ‘one-tribe’ approach to push its data analytics strategy in order to improve operations and enhance customer experiences.
“When we set out on a project, we actually create a tribe across business, analytics teams, data engineers, platform engineers, even network engineers – so everyone is part of the tribe,” the group’s head of global analytics, Nadeem Gulzar, told attendees at the recent Teradata Sydney Summit.
“And we also go for a fully co-located team. Even though we’re in Denmark, Lithuania, Poland, Bangalore, India and so on, still we aim to go for a fully located team.”
The ‘tribe approach’ brings about instant answers and communication, helps fuel challenges of thought and opinion, and fosters expediency in terms of generating ideas and delivering projects, Gulzar said.
“It is so much more powerful for a business representative when he or she has a question to just stand up, go across the aisle, and ask the engineer right away,” he said. “I know it’s not doable for all organisations. But it’s a trend we’ve seen in other organisations as well. And it is one of the key things that we’ve done and is part of our success story.”
Danske Bank is a Nordic universal bank with 2.7 million personal customers, 19,800 employees, 1900 corporate and institutional customers, and 231,000 small and medium-sized business customers.
The bank is now using the power of AI and machine learning to improve internal operations and beef up customer service, taking the retail bank to the next level. According to Gulzar, getting to this point requires brands to seek opportunities and first enter the research stage; proceed to the applied research phase; then move onto the practitioner level (putting solutions into production); and then finally into the disruptor phase and transform the business.
The first step is to start advocating for AI. “We then set up a research team and the initial mantra was, ‘Look into this. Is it a technology we can use and what sort of value can it create for Danske Bank?” he explained.
The team pinpointed areas where the bank could add value. In doing so, however, it stumbled across several challenges, many of which related to AI overhype.
“We built a lot of proof of concepts [POCs]. And it was extremely hot. But everyone wanted to do something with us, but that’s actually part of the problem,” Gulzar said. “All of a sudden, I was getting requests left right and centre: ‘Can you take this data and do some AI magic?’
“AI is pretty good at many things, but it’s not the magic wand. That is a key learning: Do not apply AI to everything. Maybe you just need the data that you already have. It doesn’t need to be big data, or anything like that. You can even do a lot of magic by applying simple statistical models. It does not have to be machine learning or deep learning all of the time.
