[Guide] Designing a Data Governance Program in Financial Services -
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[Guide] Designing a Data Governance Program in Financial Services

Data is the lifeblood of any company. It is the basis of management decision-making, regulatory supervision, taxation, and investor and market behavior. In recent years, firms have recognized data as an independent asset that should be managed and leveraged to fully reap its benefits.

As firms began to recognize the power of data for its ability to optimize business processes, as well as to provide a strategic advantage, new disciplines for its governance emerged. Senior-level management positions such as chief data officer and chief data scientist were created to focus on maximizing the potential of this crucial asset.

New strategies for categorizing, structuring, controlling, and warehousing data were developed to ensure its consistent and timely use across an enterprise. Technologies such as data mining and artificial intelligence (AI) have matured to create predictive analytics models from data resources. Firms began to consider previously overlooked sources of data, such as machine data (e.g., logs from transaction systems, routers, servers, firewalls) to bolster security and generate real-time alerts for issues potentially affecting enterprise operations or system and network performance.

Today, it’s all about data.

Data that is accurate, timely, complete, and secure is paramount to providing customers with the ultimate experience they demand. It’s never been more critical for a firm to ensure its data assets are properly managed throughout the enterprise. As there are different classifications of data, each with its own characteristics, management will need to implement specific controls and governance.

Types of Data

Broadly speaking, data can be categorized as:

  • Transactional Data: Data created in association with a business or accounting event
  • Master/Reference Data: Semi-static, internal data referenced by transactional data (e.g., customers/clients, employees, chart-of-accounts, products)
  • Meta Data: Data about data (e.g., data dictionaries, data field descriptions/usage)
  • Machine Data: Data created by the logging activities of business applications and operating platforms (e.g., database software, servers, routers, firewalls, security appliances)

As the characteristics (size, volatility, usage, etc.) of each data type vary, so does its optimal governance.

To learn more about the components of a data governance program and the steps to take to remediate any weaknesses that can compromise the quality and security of a firm’s data; download our guide here or click the link below.

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