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