Data architecture around risk management
In
today's competitive
market, many organizations
are unaware of the quantity of poor-quality data in their systems. Some
organizations assume that their data is of adequate quality, although they have
conducted no metrical or statistical analysis to support the assumption. Others
know that their performance is hampered by poor-quality data, but they cannot
measure the problem.
Enterprise
are
spending most of their time reconciling
and validating
business
data since underlying data originate from disparate systems.
Data
quality is concerned not only with the structure of the dataset, but with the
usefulness and value of the information it contains, record by record and field
by field.
Most
organizations are struggling to measure ROI on risk management and
communicate
its process ,values and effectiveness to key stakeholders.
Most
organization struggles to access enterprise wide risk exposure
Regulatory
compliance posing greatest obstacles
against developing enterprise
risk management solutions
Top
executive do not articulate risk appetite properly\
Lack of
human resource and expertise
The
greatest setback posed by various business units inside an organization is to
unable to make Risk based
decisions
Find out more on "Rise
of Data Governance"
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