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Adrian Pearce

Credit Suisse’s Approach to Driving Organization-Wide Data Strategy Goals

Adrian Pearce, Group Chief Data Officer at Credit Suisse, outlines how he balances consistency with flexibility while advancing his data strategy across the firm’s many and diverse business units

For organizations with tens of thousands of employees, getting everyone pulling in the same direction on data strategy can be a huge challenge. Orchestrating a group-wide vision of the future requires a delicate balance of consistency, transparency and flexibility.

In this week’s episode of the Business of Data podcast, Credit Suisse Group Chief Data Officer Adrian Pearce shares his approach to striking this balance to achieve the firm’s data strategy goals.

“If you’re overly prescriptive, you end up with 80% of the people telling you why it doesn’t work for them,” he says. “The challenge is being flexible enough, while making sure you drive a common direction.”

Balancing Data Strategy Consistency with Flexibility

Today, Credit Suisse is focusing on three data strategy objectives: 1) fixing data quality issues and democratizing the data, 2) industrializing data management processes and 3) ensuring data is source from the right places and used correctly.

While these goals are simple, executing them is not. Pearce gives the example of the firm’s investment banking division and its retail operation in Switzerland to illustrate the differences between how the company’s many divisions and business units use data.

“In an organization like Credit Suisse, data isn’t the same for everybody,” he says. “The way we interact with both of those client sets is just completely different.”

“You have to do [things] in a careful way,” he adds. “You can’t change direction. You can’t come up with a bigger, better goal every 10 minutes. You need to really be giving consistent information.”

For Pearce, the key to success lies in balancing the “non-negotiable” steps toward achieving these consistent organizational goals with flexibility in other areas. This helps divisional CDOs to buy into these big projects without compromising their ability to serve the needs of their units.

To illustrate this idea, he gives the example of Credit Suisse’s organization-wide data quality initiative.

“We have a tool called Data Quality Issue Management,” he says. “It’s non-negotiable. Everybody has to enter their data quality issues in it.”

“We’ve managed to drive that consistently across the firm,” he continues. “By being able to explain to the organization the benefits of fixing [data quality issues], the individual CDOs of each divisional function have clearly bought into it.”

Key Takeaways

  • Consistency is key in large enterprises. It takes time for a big ship to turn. So, data leaders should pick clear goals that aren’t going to change or move too much
  • Don’t be too prescriptive. Group data leaders much allow divisional or functional data teams the flexibility to meet the needs of different stakeholder groups across the enterprise
  • Secure buy-in for key strategic projects. Affording data leaders flexibility in some areas can make it easier to secure buy-in for ‘non-negotiable’ objectives that will have tangible business benefits