This article is the second in a multi-part series on data governance by Arun Krishnamurthy, Managing Director at Grandview Analytics. The series will focus on how asset and investment managers can establish a robust data governance practice and a culture of data.
As part of our data governance series, our first article provided an overview of data governance key concepts and terms and made the case for a robust data governance practice and data culture in asset management organizations. In this second article, we dive deeper into the components of a strong Data Governance Framework including the organizational entities required to effectively execute data governance in an investment firm and the standards and policies needed to support a strong data culture. We will also discuss different approaches to assess the current state of your data governance maturity.
Data governance drives transformative, data-driven capabilities
In firms with less mature data governance practices, the processes that support data quality enforcement are often bespoke due to the esoteric nature of asset management workflows. They tend to share these characteristics:
- Processes are fragmented and have grown organically through the best efforts of staff in multiple functional areas
- Processes are frequently manual, reactive, and inconsistent
- Data ownership is unclear or overlaps across groups; data consumers do not know who to go to with data questions or challenges
In these situations, data quality issues and the need for manual data integration and reconciliation persist. This drains resources in your firm and introduces multiple points of risk. Furthermore, it can be difficult to monetize your data, launch new products, or enter new markets.
Only when organizations move toward more holistic and integrated data governance practices can transformative, data-driven capabilities and organizational scale be achieved. A data governance framework will provide a structured and consistent approach to managing and using data assets and create confidence in the accuracy and ownership of data.
Assessing data governance maturity in your asset management organization
To create or enhance a culture of data, we recommend first defining the current state of your firm’s data governance maturity.
To help you get started, consider using an established data governance maturity model, such as:
- The Data Management Association (DAMA) International Data Management Maturity Model (DMM)
- The Carnegie Mellon University Capability Maturity Model Integration (CMMI) for Data Management
- Gartner’s Enterprise Information Management (EIM) Maturity Model
Adopting one of these maturity models will help your organization achieve the following:
- Benchmark your data governance maturity
- Use standard and well-defined measures to determine the preferred maturity level for your organization
- Establish a path to get to the desired level of data governance maturity and continuously improve
Laying the foundation: Choosing your data governance model
Once you’ve identified your current state, the next step is to set up a framework and policies that define the guidelines, procedures, rules of engagement, decision-making and technology used to govern data within your organization. This framework enables a consistent, repeatable approach to managing your firms’ data. As a result, your firm will have a standard and scalable way to measure and report key status metrics across data domains and data stewards.
There are three common data governance models:
- Centralized Data Governance: Consolidates governance control within a dedicated team, which can be set up to “own” and drive all the data governance and data management activities in your organization.
- Decentralized Data Governance: Allows individual departments to manage their own data governance, promoting flexibility but requiring strong coordination to maintain consistency.
- Federated Data Governance: A blend of the two previous approaches, this model establishes a centralized data governance team that provides guidance and support to business units, which are empowered to tailor governance practices to their unique needs.
Based on the size and data governance maturity of your organization one of the approaches may be more suitable, and there are pros and cons to each, which we’ll discuss in our next article.
Regardless of the data governance model you choose, a strong data culture will require Data Governance Framework and Policies to govern data within your organization. This framework will give your firm a consistent, standard, and scalable way to measure and report metrics or performance indicators of data governance across the enterprise.
Components of a robust data governance framework
A robust data governance framework is built on several key components that work together, each playing a critical role in creating a cohesive system that governs how data is handled across your organization. In the following sections, we’ll delve into each component, exploring how each contributes to a comprehensive data governance strategy that drives operational excellence and supports your firm’s broader business objectives.
A data governance team “owns” the Data Governance Framework and Policies. This team defines the templates, guidelines, and guardrails. The rules of engagement and best practices defined cover each of the six functional components: data council, data steward working group, metadata management, data quality, data access and compliance, and monitoring and improvement opportunities.
- Data council: Chaired and run by the data governance team, this steering committee is comprised of senior stakeholders. They are responsible for reviewing accomplishments and challenges and helping prioritize business use cases. It is extremely important to have this senior sponsorship to affect a data culture, drive data ownership, and champion the value of data as a product.
- Data steward working group: Also chaired and run by the data governance team, this group shares progress, challenges, opportunities, use cases, and data governance effectiveness metrics for each data domain. The metrics and information collected and shared are defined by the data governance team. The intent is to cultivate a shared understanding of the “state of data” in your organization using standard and consistent methods.
- Metadata management: Owned by the data governance team, the metadata management platform is a system or tool designed to manage and organize metadata. The data governance team defines your organization’s best practices regarding the metadata that is collected and documented, how the metadata is organized and presented, and the minimum requirements around keeping each domain’s metadata active, refreshed, accurate, and useful. The data stewards “own” the content of the metadata for each data domain.
- Data quality: Also owned by the data governance team, this defines the minimum set of data quality measurement requirements and metrics that should be collected and reported, how data quality rules are defined, and where data quality rules are implemented. Typically, this falls to three groups closest to the data: the data steward team, the associated business owner, and the business unit technology partners. Data quality tests should be balanced between bespoke tests for specific data domains and common/shared data quality tests across domains. Typically, data quality rules are defined, maintained, and executed in a data quality platform. We’ll discuss this in more detail in future articles. The approach and workflows used to manage data issues are also part of the data quality framework.
- Data access and compliance: Rules and processes for ensuring the right people in your organization can access the data they need, and that data management policies and practices comply with appropriate regulations, such as GDPR (the European Union’s General Data Protection Regulation) or CCPA (California Consumer Privacy Act). A workflow-based technology platform can automate the process of requesting, reviewing, and granting data access.
The data governance team uses the data governance framework to define the processes and guidelines for requesting and granting access to your organization’s data and owns the platform utilized for this purpose. Typically, data access requests, audits, and authorizations are done utilizing a workflow-based technology platform. The definitions of the data classification of the data domains (at the attribute level if required) are typically maintained within the metadata platform and those classifications are leveraged in the authentication and authorization workflows. The Data Steward is responsible for managing user data access. Similarly, the Data Governance Framework and Policies with input from data stewards identifies the various regulations that need to be followed, and the data stewards ensure compliance with those regulations.
- Monitoring and improvement opportunities: A feedback loop to your organization’s people, process, and technology to improve the effectiveness of your firm’s governance performance indicators and overall data culture.
Consistency and scalability are key
Regardless of your firm’s specific approach to data governance, a well-defined framework and polices will set the foundation for a consistent, scalable, and repeatable data governance practice.
In future installments of this series, we will discuss:
- The pros and cons of the different models of data governance
- The specific activities for each of the Data Governance Framework components
- A suggested approach to start and then scale up an effective and pragmatic data governance practice
Grandview Analytics can help your organization with their data governance challenges, and help design, plan and implement a strong data culture. Please reach out to Arun with comments or questions: arun.krishnamurthy@grandviewanalytics.com.
Read Part 3 of this series, Choosing your data governance operating model, where we further define the most common models, including the pros and cons of each.
