Preparing for AI: A Strategic Checklist for Sell-Side Institutions

As technology, specifically AI, accelerates at an unprecedented pace, organizations must adapt to leverage its benefits, particularly in the financial sector. Financial institutions must recognize the importance of data quality and governance to enhance the value of their data. To effectively utilize AI, they have to focus on ensuring high-quality, clean data is available for their AI initiatives. 

As financial organizations look to incorporate AI into their business strategy and to improve operational efficiency, they need a modernized, cloud-based data platform, that empowers them to make critical decisions from a stronger data management foundation. Firms should look for a data platform that has the below characteristics:   

1. Establish and Implement a Robust Data Governance Framework

A strong data governance framework is critical for ensuring data quality and compliance. A platform that supports these key components: 

• •Have clear and defined policies for data usage, storage, and access. This helps maintain data integrity and security. 
• •Create training programs to educate employees across all business lines on data governance guidelines. Informed staff are better equipped to handle data responsibly. 
• •Designate individuals or teams to oversee data quality and governance. Accountability is essential for maintaining high standards. 
• •Generate a comprehensive catalog of data assets and metadata to enhance understanding and facilitate usage across the organization. 
• •Ensure storage and maintenance of metadata, which is crucial for enabling users to navigate information and understand what they’re looking at.  

2. Continuous Monitoring of Data Quality 

A data platform that has data quality tools for data collection, quality oversight, and exception resolution will enhance data integrity and streamline processes. Having automated processes will reduce manual reconciliation efforts and associated costs resulting in improved efficiency and minimize the potential for human error. Ongoing data quality checks during the data cleansing and normalization is vital for maintaining the integrity of the data. A comprehensive data integrity process should take into account the six dimensions of data quality: 

• •Accuracy: Does the data reflect reality?   
• •Completeness: Does the dataset have all the required information? 
• •Consistency: Is the data synchronized across the organization? 
• •Timeliness: Is the data available when needed? 
• •Validity: Is the data in a specific format, follows business rules, and can be used with other sources? 
• •Uniqueness: Is the data only recorded once in the dataset? 

 3. Address Data Localization and Sovereignty 

The biggest blocker to creating centralized data repositories and governance is data localization. As organizations modernize their data platforms, they must navigate the complexities of data localization and sovereignty. Key considerations include: 

• •Implement data protection and privacy protocols to protect sensitive data across different geographic locations. Compliance with data protection regulations is critical. 
• •Document data lineage by tracking data movement from creation to storage. Understanding data lineage is crucial for compliance and auditing purposes. 
• •Use data masking for sensitive information that crosses geographic boundaries. This protects data while maintaining its usability. 
• •Enforce access controls based on geographic location to safeguard data security. Access controls help mitigate risks associated with data breaches. 

4. Create Integrated Data Management

Organizations with a centralized data platform can support integration across business lines. Integrated data management enables banks to elevate their data strategy into a more proactive approach. This facilitates the transition from reactive decision-making to proactive and predictive strategies, thereby enhancing capital risk management and regulatory compliance. A unified platform that can: 

Promote a data-driven culture that prioritizes data-driven decision making, while also enhancing collaboration and data accessibility across teams. This mindset shift is essential for maximizing the benefits of AI. 

• •Establish a centralized repository for your data sources to minimize duplicates and ensure consistent data across business lines. This foundational step supports effective data governance. 
• •Organize and make metadata accessible to empower users throughout the enterprise. Understanding data context is vital for effective utilization. 
• •Implement systems to correlate data from various sources, ensuring a holistic view of information. 

Conclusion 

The efficacy of GenAI tools hinges on high-quality, clean data, as errors or inconsistencies can lead to misleading and potentially harmful outputs. Organizations should commit to continuous improvement and monitoring of their data governance and management practices. Regularly revisiting these strategies ensures adaptability in a dynamic technological landscape. Arcesium’s AquataTM delivers solutions engineered to help firms integrate data and decision-making tools into the core of their organization. Aquata’s cloud-native technology and services are designed to enable firms to ingest, validate, harmonize, analyze, and distribute data across business lines.   

With a strong emphasis on data quality, governance, and management, financial institutions can unlock the full potential of AI, driving innovation and enhancing decision-making capabilities in an increasingly complex financial environment. As technology continues to evolve, staying proactive will be key to navigating future challenges and opportunities.   

Contact us to schedule a demo or learn more about how we can support your goals.