Introduction:
Ambika D’Souza
Is data governance the silver bullet for all your data issues? Has the marriage of performance departments and the data office been a successful one? In this blog, we’ll explore the significance of data governance and its impact on addressing data challenges in the performance measurement and client reporting industry.
Understanding Data Governance:
Data Governance is a comprehensive framework consisting of principles, standards, and practices that guide the end-to-end lifecycle of data, covering aspects such as collection, storage, use, protection, archiving, and deletion.
Key elements of data governance include establishing accountability structures, confirming data owners, enforcing rules and policies, documenting processes, and measuring the success of the governance program through metrics.
Differentiating Data Governance and Data Management:
While data governance sets the blueprint for managing data effectively, data management is the technical implementation of these governance principles.
To illustrate, think of data governance as the blueprints for a building, and data management as the physical construction. Without data governance, the process is less efficient and effective, leading to potential issues down the line.
The Significance of Data Governance in Performance & Client Reporting:
The performance and client reporting industry has increasingly recognized the critical role of data governance in ensuring data reliability and quality. But what does an effective enterprise data governance framework entail?
Critical Aspects of an Enterprise Data Governance Framework:
- People: Getting buy-in from these stakeholders is crucial when establishing a governance framework for the first time:
- Subject Matter Experts (SMEs): Determine standardized business terms and set quality thresholds.
- Data Stewards: Remediate data quality issues.
- Technologists: Manage databases and applications.
- Legal: Address data privacy and protection issues.
- Governance Council: Resolve issues between functions within an organization.
- Policies: Establish clear policies on data usage, retention, and security to reduce ambiguity for users. Define the “single source of truth” for each data domain.
- Metrics: Document successes through established metrics, such as data accuracy, completeness, and the number of duplicate records.
The ‘Why’ behind Data Governance in Performance:
Performance data relies heavily on upstream sources, making it essential to understand data lineage. This understanding ensures the quality of data entering the performance ecosystem.
With various sources for the same data, having a robust data governance framework at the enterprise level becomes paramount. This framework resolves issues related to data normalization and ensures consistent data consumption by downstream users.
In conclusion, embracing data governance is not merely a trend but a strategic move toward efficiency and effectiveness in managing and utilizing data.
As the performance and client reporting industry evolves, a strong data governance framework will be the key to overcoming data challenges and ensuring the reliability of performance data.
(The author Ambika D’Souza CIPM, CSPO has more than 25 years of performance and analytics experience, including membership in various industry associations such as CFA Institute (CFAi) USIPC, CFAi GIPS Technical Committee, CFAi GIPS Investment Manager Subcommittee, CFAi USIPC, and CIPM Standards Setting. She is also an advisory board member for Financial Technology Forum (FTF) and will be a speaker at FTF’s Performance Measurement & Client Reporting event on Feb. 29, 2024 at Etc. Venues, 601 Lexington Ave. in New York City.)
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