Practical Ways to Improve Data Governance for Financial Institutions

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Good data governance is expected to assist financial services organizations to control their data, increasing compliance, and adapting more swiftly to the financial services regulatory landscape’s near-constant changes. However, especially when firms are using legacy systems, this may be a time-consuming and costly procedure. It is estimated that firms spend between 10% and 30% of their revenue dealing with data quality issues.

Outdated data governance increases vulnerability to regulatory, security, and operational concerns. As technology advances and more institutions use AI and ML services, inadequate data governance can be viewed as a setback for the banking sector, which is regarded as one of the most digitally mature. From an efficiency aspect, there is no doubting AI’s promise; nevertheless, issues arise due to AI’s reliance on data. Poor data governance can result in security issues, noncompliance, higher expenses, and decreased productivity.

Best practices for data governance to help you succeed

It is critical to avoid overcomplicating data governance. At the same time, data governance services cannot stand alone as a corporate strategy. Following are seven best practices that will help organizations play defense while still using data to achieve their financial and productivity goals without resorting to shortcuts to develop & manage a successful governance program and cope with the corporate anxiety associated with security, compliance, and privacy issues.

Assess the effectiveness of your governance program

Data governance is concerned with the process of making decisions rather than the outcome of those decisions. It is also true that traditional corporate success measures are not directly applicable. The following metrics can help track the success of a governance program and demonstrate that the organization is resilient, better informed, and accountable:

  • the number of people covered by the program – those allocated specific responsibilities, instructed in processes, or made aware of the policy
  • the amount of data sources for which a governance policy has been created and is being used to make operational, tactical, or strategic choices
  • observed increases in data quality and reuse the data

Secure your data near the source

Security becomes a specialized field. Threats are becoming more sophisticated. Keeping business systems safe from external threats is a full-time task. Keeping track of access rules and permissions in a huge, ever-changing corporation is a genuine issue.

Collaboration between the security and data governance teams is critical. The governance team should make every effort to develop data access policies as close to the source data as possible. Customer data, for example, can be created and retained in the database of a transactional system but evaluated and reported on in a data warehouse. The data is retrieved from the transactional system and placed in the warehouse regularly. When security and privacy standards are applied to the source system, unneeded data is removed from the data warehouse, and data governance is considerably simplified.

Client tools such as business intelligence or data visualization platforms should not be used to impose security policies. The data may have already gone via easily accessible, unprotected routes by the time a BI user sees it. Security for BI is an important feature,

Regularly review your data governance policies

An effective data governance program necessitates ongoing work. New roles will be created. Regulations will be altered. Determine what is required to keep up and implement.

Regularly assess governance policies. At the very least, an annual assessment makes sense because a lot can change in a year. Other assessments will be ad hoc, such as when a merger or acquisition brings in new data, people, and tools. Some industries, such as financial services, may see rapid changes in data regulation, as well as rules governing money laundering, sanctions, liquidity, and credit, among other things.

Taking charge of your governance program

Important questions must be raised, particularly in huge corporations: Where does governance fit in the overall structure? Is the governance team responsible to the CTO or CIO, or a CSO or chief compliance officer (CCO)

A reporting hierarchy can be configured in a variety of ways. Some have not worked because the person did not fit into their roles well. Others pitched in since everyone was invested in the program’s success. Because security and compliance are tied to governance, a CCO may find themselves in charge of a governance team. If not, a CIO or CTO should oversee a data governance initiative. Data governance is not a technological issue that requires a technical solution. Rather, it’s about how people, processes, and technologies interact, which may make the CIO the greatest fit.

Data governance is a difficult process, especially when you initially begin. However, a well-governed data infrastructure that adheres to these seven best practices will benefit all business units, IT, customers, and business partners.

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