Trusted Data

Organizations are racing to adopt AI and meet increasing regulatory demands. However, without well-governed and trusted data, achieving these objectives remains elusive. Compromised data quality severely jeopardizes model performance, invites non-compliance risks, and undermines confidence in reporting and analytics.

In today's data-driven economy, the mantra is clear: if you can't trust your data, you cannot compete.

As companies strive to develop sophisticated AI applications and embrace technological innovations, the foundation of trusted data is non-negotiable. Financial institutions, often rich in data, must prioritize investments in robust data management solutions to maintain their competitive edge. By doing so, they can effectively extract insights, personalize customer experiences, and automate critical decisions.

The stakes are high; without trustworthy and well-governed, well-managed data, analytics and AI initiatives are likely to falter. Furthermore, neglecting data quality not only incurs significant financial costs but also accelerates customer churn. It is imperative for organizations to recognize the vital role of trusted data in driving performance, compliance, and customer satisfaction.

Data governance has been a hot topic for years, yet financial institutions struggle with designing effective structures and measuring data quality. KPMG has created a premier data governance and management framework, which includes a systematic approach to maintain high-quality data throughout the organization and its data aggregation processes. KPMG customizes this framework to meet specific organizational needs, implementing data governance and quality standards through an agile rollout strategy that incorporates change management to foster an effective data culture.

AI technologies are paving the way for advanced data management solutions. KPMG can assist financial institutions in implementing cutting-edge AI tools for data governance and quality. Our digital offerings include machine learning, natural language processing (NLP), and generative AI.

Machine learning algorithms can detect and correct data errors by identifying patterns and anomalies for human review. NLP helps analyze unstructured data to spot inaccuracies, while AI continuously monitors data for updates, ensuring accuracy and recency. Enhancing data quality through AI enables organizations to make better decisions and minimizes the risk of errors or biases in their analyses.

Regulators have reinforced the importance of compliance with data management regulations. Data management practices are now subject to inspections on-site, fire drills, and supervisory evaluations. Financial institutions must show that they have appropriately addressed regulatory requirements in light of their specific business and risk profiles, ensuring that their data management is robust and effectively overseen with clear accountability from top management.