Key Trends in Data Quality
By Elliot King
“If you learn how to do it, you will do it better.”
“And most companies have a long way to go.”
Those are the most striking trends in data quality, according to several
studies conducted by market research companies over the past several months.
Let’s start this tour of recent research with Gartner’s latest Magic
Quadrant report on data quality vendors. Leaving aside Gartner’s assessment
of different data quality vendors–which most people can take or leave at
their choosing–two significant findings emerge from the study.
First, the market for data quality tools continues to grow sharply.
According to Gartner, the sector will grow at a compounded annual rate (CAGR)
of 12 percent through 2014. The growth is being driven both by traditional
uses including business intelligence and master data management, as well as
new applications such as information governance initiatives. Moreover, data
quality is breaking out of the narrow domains to which it was once confined.
Organizations are finding multiple uses for the technology across the
As the use of data quality technology spreads, a report for IDC quantified
what should be obvious, but apparently isn’t, to many companies. The
better-trained employees are in data quality, the more effectively data
quality programs will be implemented.
IDC studied data quality teams in which team members had 24 hours of
training and compared them to teams in which team members had 16 hours of
training. It turns out that the team members with the more extensive
training performed data quality tasks 50 to 90 percent more frequently than
those with less training. If people know what to do, they are apparently
more willing to do it.
Finally, even as attention to data quality grows, companies still are having
trouble developing comprehensive programs. Forrester Research recently
reported that in a survey of companies using data quality tools and staffed
with professionals responsible for those activities, a mere 12 percent rated
their organizations high or very high in terms of data quality maturity.
Very high data quality maturity is defined as having standardized on a
single data quality platform across the enterprise using the same data
quality rules and having skilled professionals responsible for data quality.
On the other hand, 49 percent rated their organization as either low or very
low. Low was defined as having no standard data quality tools, a lack of
skilled professionals and paying little attention to advanced data quality
issues. Very low represents a complete lack of attention to data quality
Taken together, the overall trend looks like this. Organizations are
beginning to pay more attention to data quality, but there is a long way to
go in making data quality an integral part of the organizational structure..