By Elliot King

Elliot King

Here are some simple truths that too many companies ignore. Concerns about data quality cannot be confined to the IT department. No purely technical solution can insure high quality data. And, oh yes, no matter what you do technically, data will never be perfect.

The real question is not whether data matches some pre-established and perhaps
arbitrary mark. Instead, perhaps the most important challenge for data quality
professionals is to determine if the quality of the data meets the standard
needed to complete the task at hand. And the only way to know the answer to that
question is to ask. Remember, IT does not own corporate data. The business users
do.

With that mind, a regular, ongoing program to query key consumers of data should
be integrated into the overall data quality initiative. Data consumers should be
asked if they can access the data they need efficiently and easily. Is the data
they access timely? Is the data accurate? Is the data relevant? And is the data
consistent?

That feedback can be used to identify and address pressing data quality needs.
In many companies, different business units can tolerate poorer data quality and
still complete their tasks at hand–or so they think.

Front-line data consumers are only one constituency of interest, however. The
person responsible for the data in a business unit must be queried as well about
data quality issues. Front-line users may feel satisfied with the level of data
quality, but a manager with a broader perspective may feel otherwise and vice
versa.

Finally, senior decision-makers must be brought into the conversation. Effective
decision-support represents one of the most potent potential payoffs for
investment in data quality and also represents one of the most devastating
potential pitfalls. The data quality stakes in decision-support can be very
high.

Data quality improvement programs can be laborious and costly. To be effective,
they must be crafted to identify, target and address the areas in which poor
data quality has the biggest impact on company operations. To identify those
weaknesses, data quality specialists must ask their customers, the users of the
data.


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