Wealth Access Report FINAL - Flipbook - Page 19
Use Case:
“Pure Efficiency Play”
The biggest data-driven win at Brentwood, Tennessee-
whether the information is accurate. He calls these gains
based Sonata Bank has been cutting down the time
a “pure efficiency play” and says the time saved helps
employees spend doing administrative tasks, says Chief
producers focus on clients and grow loans.
Innovation Officer Will Rhoads. Instead of creating
“We have saved dozens of hours for departments, man-
reports from spreadsheets, he says some employees at
agers and members of the leadership team,” he says.
the $222 million bank are using natural language data
“We’re giving our producers time back; we’re giving
queries where they can type in a question and get an
management time to be strategic instead of writing
answer. Workers have dashboards that update automat-
reports.”
ically with the most current information, freeing them
from having to make manual updates or worrying about
teams that use the data understand how and why they made
identifying these areas, institutions can then prioritize allo-
those decisions.
cating resources that will have the biggest impact on reducing
the risks of using bad, incomplete or inaccurate data.
At MSU Federal Credit Union in East Lansing, Michigan,
governance meant developing a “true common language”
Maxim remembers one challenge MSU Federal Credit Union
and formal definitions of critical terminology that could
encountered early in its data journey was trying to figure out
be used across departments within the organization, says
why so many members were listed as being born in 1969.
Chief Technology Officer Benjamin Maxim. For instance, an
That didn’t make sense given that many of the credit union’s
active customer might mean one thing to an employee who
members are college students at nearby Michigan State
works in a branch compared with one in a call center or one
University.
who supports digital banking. The $8.2 billion credit union
now assigns ownership of certain data fields to particular
employees or groups, who are then responsible for annually
reviewing the data definitions for updates and certifying that
they are accurate.
“We’re trying to pull demographic data about our members,
and we had all these members born in 1969. That’s weird,”
he says. “It turns out it’s because someone left this data
field empty and that’s what [the system] defaulted to.”
The credit union has also encountered seemingly all the ways
someone could write the word “Michigan” as it attempted
The Consequences of Bad Data
to validate data needed for lending applications. Maxim says
there would be entries with typos, inconsistent capitalization
Data governance should leverage a risk-based approach
to identify where an organization is “most vulnerable” to
and various abbreviations. All of that needed to be scrubbed
and made consistent.
using bad data and what could happen as a result, Sud says.
Incorrect information can lead to poor communications with
customers or inaccurate or misleading results. Bad information also reduces the usability of the data and can make these
initiatives less successful if employees don’t trust it. After
Effective Vendor Due Diligence
Data analytics projects may lead institutions to use their
EFFECTIVE DATA MANAGEMENT: WHAT’S YOUR STRATEGY? | 17