Maximizing the Potential of Data Governance and Data Management in Financial Services
Financial services firms are vigorously adapting to the wave of technological advancements like machine learning and artificial intelligence (AI). In this digital age, the role of data governance (DG) and data management (DM) is emerging as a crucial component of successful enterprise data and analytics platforms. However, the significance of DG and DM often remains underestimated in the rush towards technological advancement.
It is essential for these firms to understand that DG and DM should align with their investment philosophy and structure. By harnessing business domain knowledge, experience, and expertise, organizations can effectively incorporate the management of big data (BD) alongside traditional small data.
While advanced technologies promise greater efficiencies and competitive advantages, it is imperative to remember the principle of “garbage in, garbage out.” Flawed or poor-quality data inputs will inevitably result in faulty outputs. Therefore, AI models must be meticulously trained, validated, and tested with high-quality data.
The Data Science Venn Diagram
BD serves as the primary input for AI models. Data Science, at its core, encompasses a blend of mathematics, statistics, computer science, domain knowledge, and expertise. This interdisciplinary field emphasizes the need for human teams to adapt to the evolving landscape to remain relevant in the industry. The convergence of these functions can be visualized in a Venn Diagram, showcasing the collaboration between AI professionals, technology experts, and investment professionals fueled by data professionals.
Key Components of Data Governance
For organizations to unlock the potential of BD and AI, it is vital to establish robust data governance frameworks. Key components of data governance include:
- Alignment and Commitment: Across the enterprise, alignment on data strategy is crucial, backed by management commitment.
- Security: Data security measures must be put in place to protect against unauthorized access and corruption.
- Transparency: Policies and procedures should be transparent and auditable.
- Compliance: Ensuring controls are in place to comply with policies, regulations, and legislative requirements.
- Stewardship: Assigning data stewards to monitor and control data access.
Key Elements of Data Management
Data management involves preparing raw data for analysis. Key elements of data management include:
- Preparation: Cleaning and transforming raw data to ensure completeness and accuracy.
- Catalogs, Warehouses, and Pipelines: Data catalogs, warehouses, and pipelines aid in consolidating and transferring data efficiently.
- Extract, Transform, Load (ETL): Transforming data into a format suitable for loading into organizational data warehouses.
Data governance and data management frameworks play a pivotal role in handling complex big data effectively. Both functions must work in harmony to enable organizations to make data-driven decisions and drive efficiency and productivity.
It is crucial for financial institutions to understand the symbiotic relationship between data governance and data management. While DG lays the groundwork with policies and procedures, DM and its implementation enable effective analysis of data and informed decision-making.
By integrating DG and DM seamlessly, organizations can leverage their data assets more effectively and make smarter business decisions in this era of rapid technological evolution.
References:
- Larry Cao, CFA, CFA Institute (2019), AI Pioneers in Investment Management
- OECD (2021), Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers