For asset managers, a robust and ongoing data governance program is one of the most important factors in becoming a successful data-centric organization. We believe that a strong data governance program can bring business and IT together—providing the people and platforms to drive knowledge, accountability and transparency for enterprise data management through the business.
Establishing a data governance program often involves significant strategic and operational hurdles—but we have five “tried and tested” reasons that show why it’s worth the effort. These factors reflect our past years of experience from numerous data governance strategy and implementation engagements as well as feedback and insights from investment management firms.
1. A data governance organization (DGO) provides consistent firm-wide standards for business data transparency, data protection and audit integrity.
Business term definitions within one business group may have an entirely different interpretation and use in another. It is even more difficult for all interested parties to agree on a set of business definitions (a “data dictionary”) and business usage rules. This is where data governance can help centralize the process and distribute results.
Centralizing this function helps risk mitigation when taking on large-scale data transformation initiatives as it can provide for better control and oversight of complex data projects. Similar, yet smaller-scale projects also benefit. Moreover, adopting a consistent set of standards can allow the DGO to incorporate and adopt best practices while remaining agile in the face of industry and regulatory changes.
2. The data governance program establishes trusted, certified data (“golden sources”) for business users
By forming a data governance program, the enterprise is essentially making a firm-wide commitment to establish and maintain quality data via a data quality framework. This effort to be transparent and genuinely responsive will help the DGO change negative perceptions, and can help establish a sense of trust and confidence among users that the data has been through validation processes. This assurance can eliminate internal debates about data quality and when it can be considered “good enough” for business consumption.
Remembering that different consumers require different levels of data quality, the data quality framework process should be able to support a multi-level certification process. This certification level must be communicated to data consumers via metadata or data consumption services. Additionally, transparency and assurance reduce the time that data consumers must devote to individually acquiring and revalidating data.
3. Data governance offers the opportunity to centralize and consolidate data management and procurement services.
Acquiring data—particularly from external, third-party sources—is usually a significant expense for asset management firms. Add to this the risk that the data hasn’t been properly acquired and validated and might be subject to additional licensing fees. By centralizing this function, a firm could efficiently streamline the procurement of data, negotiate favorable rates, mitigate risks associated with storing sensitive data, and decrease operational costs by reducing data redundancies across the enterprise. This also offers the “quick win” option for governance to demonstrate cost-benefit analysis value.
4. Data governance helps establish a business information and data ownership model.
Asset managers use various names and models when defining the business data catalogue or information model with the associated ownership roles within their data management and governance structure. Firms can achieve a major governance goal by standardizing the information model and business terminology (or ontology). By mapping the business organizational data management model against the organization’s “data universe”, a data business ownership model in the form of a pseudo RACI (Responsible, Accountable, Consulted, Informed) matrix can be established. Owners and data stewards must be held accountable for overall content, delivery and quality within their respective business units. This way, the business clearly defines data usage rules, which could reduce IT exposure and minimize risk when making data available for business consumption and downstream IT systems.
5. Effective enterprise data management (EDM) and data governance should address both short-term data agility and long-term data stability.
A well-designed EDM strategy and platform could help firms become much more agile and responsive to business and industry demands. Firms must plan and execute against a well-designed, long-term roadmap while also building capacity to adjust to short-term market drivers. In the current cyber sensitive environment, the EDM model should ensure the overall security and safety of data, as well as transparency and audit integrity across all critical data assets. By combining effective governance with the implementation of an EDM platform, a firm could capitalize on this new strategic asset, and position itself for quicker development and delivery of new data sources and services to the marketplace.
These five drivers could help asset managers make the most of their data governance programs—right from the start. If you’d like to continue the conversation, contact me at email@example.com. For more Accenture insights on enterprise data management, read “Managing complex workflows with EDM”.