By Elliot King

Elliot King

Too often, data quality is seen as a technical issue. You know the drill: profile, measure, remediate, integrate, augment, control and so on. In many ways, a company’s data is like an ecosystem, and the data quality team is analogous to the environmental specialists. They conceptualize an ideal state for data, measure the actual state, and then fix the problems they find.

The problem with this approach is that the data quality team often is not made
up of the people who actually produce or consume the data within the
organization. Indeed, in virtually every setting, non-technical staff members
are both the primary creators and primary users of data. If those two groups do
not understand its importance, data quality will be an ongoing issue.

The first step in raising non-technical personnel’s awareness of data quality is
to offer them a holistic view of the information ecosystem. As can be expected,
most front-line staff members are focused on the task at hand.

Customer service representatives, for example, understandably concentrate on how
many people are successfully managed within a given time period, which is a
typical evaluation metric for CSRs. If some of the data entered into the system
is faulty, their attitude may well be, that it is the price to be paid for fast
service. To address this, frontline employees need to understand the impact data
errors have throughout the entire information ecosystem.

The second step is to teach non-technical people about the data quality
procedures in place. The reason companies invest in data quality is because of
the impact poor data quality has on the organization’s performance.
Non-technical personnel will better understand the link between good data
quality and the organization’s success if they become aware of the investment
being made into ensuring that data meets the expected standards.

However, educating non-technical people about the importance of data quality is
not enough. Staff members have to be trained so they factor data quality into
their activities whether it is producing, managing or consuming data. The truth
is that the data quality team cannot safeguard the quality of data itself. That
must be the mandate of the entire organization.


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