Associated Bank shifts from reactive to predictive data approach with DataOps methodology
As a plethora of data is produced every day, companies try to find the best way to manage it to generate business value. Associated Bank, the largest bank headquartered in Wisconsin, began a journey through the emerging DataOps methodology with the goal of adopting a more predictive data approach, according to Steven Lueck (pictured), senior vice president and director of data management at Associated Bank.
DataOps is an automated, process-oriented methodology to improve the quality and reduce the cycle time of data analytics. The idea is to operationalize the data pipeline and employ artificial intelligence to tackle the existing cost-cutting and revenue generation opportunities.
“I think that one of the key areas for us is that we’re trying to shift from more of a reactive mode into more of a predictive, prescriptive mode from a data and analytics perspective,” Lueck said. “And using our data to infuse and drive more business decisions, but also to infuse it in actual applications and customer experience.”
Lueck spoke with Dave Vellante , host of theCUBE, SiliconANGLE Media’s livestreaming studio, during the IBM DataOps in Action event . They discussed the bank’s journey toward DataOps, the challenges of this process, and the results already achieved, such as the responses to the COVID-19 crisis. (* Disclosure below.)
Being ahead of the needs of end users
The bank is focusing on creating a data lake-style strategy to ensure that it is ahead of the curve in trying to predict what end users will need and some of the advanced use cases it will have before they actually exist, according to Lueck. When the journey started, the bank had a data warehouse mindset.
“Tell me the data elements that you think you’re going to need, [and] we’ll go figure out how we map those in,” he said.
Although quality was already usually there, a rapid turnaround was missing. “It was also missing the ‘what’s next … what you haven’t thought of’ — and almost to a point of just discouraging people from asking for too many things because it got too expensive, it got too hard to maintain,” he explained.
To overcome this, the bank is building an enablement mentality, encouraging people to ask for everything. Therefore, the amount of data sent to the data team is no longer in question. “We’re getting all of the data,” he explained. “And we almost have to push that out and infuse it within our organization as opposed to waiting for it to be asked for.”
DataOps journey involves building a new culture
The challenges of the bank’s DataOps journey are in each of its three pillars — people, processes and technology. The difficulty stems from the need to establish a new culture, according to Lueck.
“Behavioral type changes have been difficult,” he stated.
