Monday, January 13, 2014

Data architecture around risk management

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

Find out more on  "Rise of Data Governance"!