If the data quality is not good, a Business cannot use the information to make sound business decisions – no matter how many talented data analysts work upon it. Enterprise Risk Management, especially, requires high-quality, integrated data for making risk-based informed decisions. For this purpose, a data management system is the primary key for providing high-quality data. By treating data as a true enterprise asset, high-quality data for initiatives such as Enterprise Risk Management can be assured. This approach ensures compliance and accelerates the enterprise towards achieving competitive differentiation.
For compliance-driven risk programs such as SOX, data requirements play a vital role in governing the risk architecture and in providing quality, integrated data for a successful implementation of an ERM initiative. The important methodologies and processes in ERM are only as good as the data that they rely on. Having said that, let’s discuss some of the best practices for improving Risk data quality and management
1. Identifying the data elements necessary to manage risk.
Identifying all the data elements and sources necessary to calculate enterprise risk is of utmost importance. Risk data such as the probability of risk, risk impact, and risk value, can each require the identification of several different data attributes. These data elements may be spread across systems, databases, and departments within the organization. Hence, if an organization does not have a sophisticated data-management or data-governance framework, this task becomes complex.
2. Defining a data-quality measurement framework.
We now understand that the integrity of risk management requires data to be of high quality, and so it becomes necessary to define data quality. A formalized approach to defining data quality relying on a number of dimensions can form the basis of a data-quality measurement framework. The primary dimensions that data quality usually measures include completeness, relevance, consistency, accuracy, and duplication. Additionally, for risk calculations, dimensions such as continuity, timeliness, redundancy, and uniqueness can be important.
3. Having frequent audits to assess the data quality
Performing a data-quality audit to identify, categorize and quantify the quality of data is a vital process here. Depending upon the magnitude of the data, a sample or complete data can be used to perform a data-quality audit. The data-quality audit also allows an organization to build its business case for further action. An extrapolation of exceptions and observations found in the data sample can quickly identify and quantify the impact of poor-quality data.
4. Setting up a continuous monitoring program.
Data quality deteriorates with time as new and emerging processes, applications and systems get on-boarded. It is important to have a continuous monitoring program to ensure that data quality is sufficient to meet the enterprise risk management goals.
The above-mentioned steps ensure the successful deployment of a data quality program. High-quality data is an important factor in enterprise-wide programs such as ERM. Successful data-quality programs are extremely vital to mitigate operational risks associated with implementing a complicated business initiative such as ERM. Arriving at a common language and framework between business and IT can help both data-quality and ERM initiatives.
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