Please enjoy this blog post authored by Emily Rushing, Director of Data Strategy, Holland & Knight LLP and peer reviewed by Suresh Annamalai, Senior Manager of Digital Innovation, Proskauer Rose LLP.
Robust risk management is essential to protect against cyber threats, changing workforce requirements, and reputational risks associated with environmental, social, and governance (ESG) considerations. Governance complements risk management by aligning data and AI with business goals, ensuring compliance, ethical integrity, and long-term success. By incorporating these practices, firms can design data strategies that drive their priorities and mitigate potential risks.
Data Governance: Balancing Enablement and Controls
Data governance plays a pivotal role in ensuring the effective use of data. It involves establishing policies and procedures to manage data access, quality, and security. Data governance is separate from compliance and risk management functions, but it does engage and support those functions, and all data functions in the firm, by enabling the organization to use data properly and well while maintaining control.
The goal of data governance is to strike a balance between enablement and controls. While controls are necessary to ensure data security and compliance, firms also need to enable stakeholders to access and use data appropriately.
To scale AI effectively, it is crucial to have actionable and reliable data, yet many organizations identify data quality as their primary challenge. Without addressing data quality issues, enterprise data will continue to be a liability rather than an asset, and the firm will struggle to realize the potential of AI and other emerging technologies.
With unified standards for data formats, definitions, and validations, organizations can establish centralized quality control and line-of-sight to key source data systems. Organizations must implement governance, quality, and observability within a single framework to leverage data lineage and track data as it moves through systems, ensuring transparency and real-time issue identification. Governance also enables regular validation of data integrity to support consistent and reliable AI models through real-time quality checks.
Data Priorities for AI
By focusing on several priority areas, firms can ensure that data governance and enablement is aligned to AI goals and objectives, namely:
• Data contextualization and accessibility, allowing for secure, permissible use of data across firm systems,
• Clear data standards and definitions to ensure consistent metadata with clear documentation going into AI tools,
• Sustainable operational conditions including data processing speeds and timing,
• Ensuring the trust, transparency, traceability, and compliance of the data sources and their downstream processing.
Engaging Stakeholders in Data Management
Effective data management requires collaboration among various stakeholders within the organization. These stakeholders include operations departments and practice groups. Engaging stakeholders in data management ensures that their needs and requirements are considered in the data strategy.
Operations Departments
Operations departments play a crucial role in maintaining and managing data systems. They are responsible for ensuring the smooth functioning of data infrastructure and implementing the data strategy. Engaging operations departments in the data management process ensures that they have the necessary resources and support to carry out their responsibilities effectively.
Practice Groups
Practice groups often work on specific data-related initiatives, such as API integration or data analysis projects both internal and client-facing. Engaging lawyers in the data management process ensures that their projects align with the overall data and client service strategies. This alignment helps in achieving the organization's data goals and objectives, ensuring scalability for firm data initiatives, and building trust and long-term relationships with lawyers and clients.
Conclusion
Information architecture in the era of APIs involves more than just transferring data from one system to another. It requires a robust data strategy, deep understanding of technologies supporting your data estate, effective data governance, collaboration among various stakeholders. By understanding the firm's strategies, navigating product API offerings, and engaging stakeholders, organizations can ensure the effective use and navigation of data.
Check out the first article, Forming a Data Strategy.
Check out the second article, Navigating Product API Offerings.
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