The lack of a data quality initiative
in enterprises today
can be driven by the perception that the
cost associated with poor data quality is
a mere cost of doing business,
said one analyst.
The lack of a data quality initiative in enterprises today can
be driven by the perception that the cost associated with
poor data quality is a mere cost of doing business, said one analyst.
Enterprises receive and process a plethora of data, but
are more concerned with the processes that handle
that data than the data itself, said Dean Wiltshire,
senior product analyst for data quality,
with location intelligence technology provider
Pitney Bowes Business Insight.
"They are more focused on processing the information
and hoping it's correct than they are on focusing on
whether the information is correct
before (they) process it," said Wiltshire.
Pitney Bowes Business Insight and automated data
mastering technology vendor Silver Creek Systems Inc.
released the results of a co-sponsored report
this week called The State of Data Quality Today.
The report is based on a survey conducted by
U.K.-based analyst firm The Information Difference.
The results revealed that while 70 per cent of respondents
believe the quality of their product data is good or very good,
only 37 per cent have implemented some manner
of data quality initiative.
Wiltshire is surprised by that statistic, a low number of
data quality initiatives that demonstrates
a sense of "false security."
Even then, the numbers are skewed, said Wiltshire,
because the 37 per cent could include organizations
whose initiatives are only in their infancy, having just
put a data governance framework in place
and started to understand data quality.
In fact, the survey also found that 63 per cent of
respondents have not even attempted to calculate
the cost on their business of poor data quality.
Yet data is a core asset of the business,
with modern businesses running on data more so
than anything else, said Martin Boyd,
vice-president of marketing with Silver Creek Systems.
"It's an intangible," said Boyd, referring to
data quality. "It is difficult to put your finger
on exactly what the overall quality of
the data is in the business."
That's particularly problematic considering
what can't be measured doesn't get improved, said Boyd.
The reason that measuring data quality is tricky,
especially concerning product data quality,
he said, is that product data can be more variable
and flows through many systems and
processes in a business, and getting
a holistic view of the business is not always easy.
For instance, validating information about
a resistor is different from validating information
about a handbag, explained Boyd.
validations change with different products.
"It's easy to say 'data quality' at a high level,
but not really get into
the different nuances of each type," said Boyd.
Wiltshire said that while data quality often exists
at the departmental level where unit managers toil
to ensure their data is of a high standard,
the same cannot be said for the enterprise
as a whole.
"This is where enterprises tend
to lack their focus," he said.
But besides measuring data quality, the hurdles
to good data quality lie in lack of
leadership support and in
defining the business case, said Wiltshire.
A data quality initiative should begin with
policy creation, while also having tools
to enforce the policies,
and measure and report success.
"It's a lifecycle, it's a loop," said Wiltshire.
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