Mark Wilson: How to Solve Data Quality Problems at the Source

Handelsbanken UK Head of Data Quality Mark Wilson offers his advice
on getting to the root of data quality problems with frontline
staff Data quality has a profound impact on the daily work of
frontline staff. And when frontline staff identify data quality
problems they expect them to be dealt with promptly. Failure to do
so can seriously drain morale. In this week’s episode of the
Business of Data Podcast, Mark Wilson, Head of Data Quality UK at
Handelsbanken argues that fighting back against apathy is essential
to drive business improvements using data and analytics. For Wilson
to create a grass-roots view of data governance at Handelsbanken he
focused on working with frontline staff to improve data quality as
well as data governance structures. “We’re working with the
front-end staff on how we collaborate with them more in their
everyday work with customers,” Wilson says. “It’s really about
being in that real-life world, away from that ‘ivory tower head
office’ mentality.” Ascending from the Ivory Tower Understanding
the realities of how businesses generate and use data and analytics
on the ground is critical to fixing problems and improving results.
And there are few better ways to learn than by getting your hands
dirty. For this reason, Wilson recommends that head office staff
spend time working in the field to discover how frontline staff are
using data and analytics, and what their challenges are. “They’ll
tell you the problems that really need solving and what’s causing
the problems,” Wilson says. “Whereas if you sit too far back in the
business, you just see the results.” This distance can sometimes
lead to head office staff misdiagnosing problems. Problems like
blaming careless staff when, in fact, the problems may lie in
processes and technology. “A lot of our early wins, [came from]
speaking to our branches,” Wilson recalls. “You’re doing a
disservice to not flush this out and talk about these things.” He
continues:” And as always, by enabling communication about them,
you could find there is already something in place that a small,
slight shift on a project path to factor something in might solve
things that were never anticipated in the first place.” Fighting
Apathy in the Workforce Data problems when reported should be
resolved promptly. Failure to do so can seriously drain morale, and
this effect can spread quickly throughout teams. “I think this is
something we consciously have to think about. What is the message
we’re sending when we don’t respond?” Wilson remarks. “I think that
[responding] is important for morale, for people’s wellbeing or
belief that they can make a change. And for the company to show
that it’s listening.” Creating structure and feedback loops for
staff is therefore essential to providing agency to frontline staff
– and showing that the company cares about their input. “We should
[have] data quality issue management processes in place where any
employee can go into a place and record a data quality problem,”
Wilson says. “You should be reviewing that, digging into the root
cause, doing the evaluation, and perhaps then identifying who in
the business has the responsibility to take the corrective action.”
He continues: “We should have a data governance committee in place
who are keeping track of these open data quality issues. And that
should be part of the management structure of an organization so
you’ve got an escalation point.” Ultimately, data and analytics
teams are there to help companies meet their goals. Therefore,
fostering trust amongst staff at all levels is essential. “We’re
here to help the company grow and be better through better data
management,” Wilson concludes. “So we need to word things in a way
that says, ‘let us know if you’ve got a problem. This is who you
contact. These are the processes in place to help you.’” Key
Findings Leave the ‘ivory tower’. By working with the frontline
staff you can gain a true understanding of how data is generated
and wh