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$15 million a year based on poor data quality, and the U.S. economy may suffer losses
2
exceeding $3 trillion annually.
The challenges and risks associated with enterprise data management can also depend on the
culture of the organization and its structure (factors such as if it is decentralized vs. centralized).
The more the organization’s individual divisions operate in silos, the more difficult it is to have an
effective enterprise data management strategy.
Other factors that could potentially affect data management include but are not limited to:

 Inaccurate or incomplete data and information asset inventory.
 Lack of enterprise data management policies.

 No one individual responsible for or capable of handling the organization’s enterprise data
architecture.

 Poor sources of data.
 Lack of procedures to identify the applications and systems that have data quality issues and

 lack of procedures to initiate projects addressing the issues.
Potential adverse outcomes from poor data management include:
 Customer displeasure when their data is inaccurately reflected in organization’s systems and
applications.
 Regulatory fines and/or penalties.

 Data breaches.
 Potential impact on an organization’s profitability.

Data Analytics

Data analytics can be used to identify trending key
indicators to help management see how well Resource
processes and controls are operating. More The IIA GTAG “Data Analytics
importantly, analytics may show ongoing degradation Technologies” provides insight on
of processes and controls that may prompt expedited assessing the maturity level of data
corrective action. As organizations mature, data
analytics strongly impacts the way they can assess analysis usage, with a focus on
and compile relevant information for decision making increasing the levels of assurance
and monitoring key risks. and other value-added services.
At the same time, data analytics has also increased in importance as a technique that the internal
audit activity may apply when executing audits. A formal data analytics program can be useful in
supporting an audit function in becoming more effective, more efficient, easily scalable, and
significantly reducing auditing errors while providing greater audit and fraud risk coverage. Data

2. Kaerrie Hall, “Customer Data Quality: The Good, the Bad, and the Ugly,” Validity, September 5, 2019.
https://www.validity.com/blog/customer-data-quality/.

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