By Elliot King

Elliot King

When it comes to data quality, so many companies need a change of attitude–or to put it bluntly, they just need to grow up. Too often, organizations approach data quality reactively, addressing their efforts to fixing what they discover as broken as quickly as possible. A proactive approach is generally more effective. In this perspective, data is viewed as a strategic asset and data quality is seen as a strategic initiative rather than a cost.

A data quality maturity model measures the sophistication of companies’ data
quality initiatives. At the most immature level, data quality is viewed as an ad
hoc process. Serious deficiencies in the data are revealed and the enterprise
scrambles to fix them. They operate under a “status quo” model until the next
problem erupts.

At the next stage, data management process discipline is introduced. By
standardizing processes, data quality activities become routine and repeatable.
Tools and technology to address data management issues are introduced but these
tools may be used inconsistently across the organization and deployed unevenly
in different functional areas.

The ultimate goal of a data maturity model includes three stages when
enterprises can effectively define, manage and then optimize their data
management and data quality activities. At the defined level, good practices are
documented; data governance policies are established; and data quality
expectations are clearly laid out. These practices, policies and definitions,
among other controls are then propagated throughout the organization. A
framework for accountability is in place and monitored.

The next step in the “maturing process” is to incorporate business impact
analysis. Measures are established to identify the impact of data quality on
business outcomes. Over time, the relationship between data quality and business
performance can be predicted. At this level, data quality issues are often
identified early and appropriately in time to avoid a negative impact on
processes results.

The companies with the most mature data quality initiatives in place can
identify opportunities to improve data quality in ways that will then improve
business processes and results. Data variations can be identified, processes
improved and standards developed strategically and systematically.

There are different ways of describing the stages of a data quality maturity
model. Gartner, for example, labels the stages slight differently. But the
message is the same. A mature data quality program does more than just fix
things. It improves the bottom line.


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