Organizations are collecting and storing more data than ever before. This data can be used to improve business processes, but it can also be a liability if mishandled. To protect the privacy of their customers and comply with the latest privacy laws, organizations need to implement a data governance framework that goes beyond basic data quality and management.
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Data governance frameworks are structured approaches to managing and utilizing data in an organization. They include policies, procedures and standards that guide how data is collected, stored, managed and used. These frameworks help with data quality, data integration, data privacy and security, and effective data architecture.
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In order to govern data effectively, organizations need to have a clear understanding of their data landscape. They need to know where their data comes from, who owns it, how it’s being used and where it’s stored. Gathering this information to build a data governance framework requires close collaboration between different departments and business units.
Below is a list of some commonly referenced data governance frameworks:
Each of these frameworks has its own pros and cons. Organizations should select the data governance framework that best aligns with their unique needs and goals.
There are two opposing philosophies to creating data governance frameworks that offer different pros and cons depending on an organization’s specific objectives.
The bottom-up approach to data governance, popularized by the growing big data movement, begins with raw data. Data is first ingested, and then structures, or schemas, are built on top of the data once it has been read. Governance rules, policies and quality controls are also added to the dataset at this time.
The advantage of this approach is its scalability; however, it can be difficult to maintain consistent quality control across a large volume of data.
For small businesses that might not have as much data as larger organizations, this approach allows for greater flexibility and scalability. It allows them to start small and scale their data governance efforts as their data grows. But as they grow and possibly face more stringent regulatory requirements, they may find value in shifting toward a top-down approach.
In the top-down approach, data modeling and governance take priority and are the first steps in developing a data governance framework. The process begins with data professionals applying well-defined methodologies and best practices to data. The advantage of this approach is its focus on quality control.
Banks, insurance companies, healthcare institutions and other large and highly regulated institutions are likely to use a top-down approach to data governance. This is because they often have a large volume of data and strict regulatory requirements to comply with, and a top-down approach allows for better quality control and compliance with regulations.
There are four primary components of a data governance framework:
Data governance frameworks are built on four key pillars that ensure the effective management and use of data across an organization. These pillars ensure data is accurate, can be effectively combined from different sources, is protected and used in compliance with laws and regulations, and is stored and managed in a way that meets the needs of the organization.
Data quality is the cornerstone of any data governance framework. It ensures that the data used in decision-making processes is accurate, consistent and reliable. Further, data quality management involves establishing policies and procedures for data validation, data cleansing and data profiling.
SEE: Explore these top data quality tools and software.
Data integration involves the combination of data from different sources to provide a unified view. This pillar ensures that data from various departments, business units or external partners can be effectively merged and used for analysis and decision-making.
Data privacy and security are crucial in today’s digital age. This pillar involves the implementation of policies and procedures to protect sensitive data and comply with data protection laws and regulations. It includes data encryption, access control and data anonymization techniques.
The fourth pillar is data architecture, which refers to the design and structure of data systems. It involves the planning and design of data systems to ensure they meet the needs of the organization. This includes the design of databases, data warehouses and data lakes.
A data governance framework provides a standard set of policies and procedures for managing an organization’s critical data assets. Without such a framework, those assets are at risk of becoming fragmented, inaccurate and non-compliant with relevant regulations.
Furthermore, a lack of governance can lead to confusion and duplication of effort, as different departments or individual users try to manage data with their own methods. A well-designed data governance framework ensures all users understand the rules for managing data and that there is a clear process for making changes or additions to the data. It unifies teams, improving communication between different teams and allowing different departments to share best practices.
In addition, a data governance framework ensures compliance with laws and regulations. From HIPAA to GDPR, there are a multitude of data privacy laws and regulations all over the world. Running afoul of these legal provisions is expensive in terms of fines and settlement costs and can damage an organization’s reputation.
Every organization wants to reap the benefits of becoming more data-driven, but getting there requires more than just collecting data. It requires a well-designed data governance framework to ensure data is managed effectively and remains compliant with relevant laws and regulations.
There is no one-size-fits-all solution for data governance frameworks. The best approach for an organization will depend on its specific needs and objectives. By following data governance best practices, organizations can create a data governance framework that meets their specific needs and industry requirements to help them achieve their desired business outcomes.
The first step in creating a data governance framework is to define the purpose of the framework. What goals does the organization want to achieve by implementing such a framework?
Understanding company-wide data management goals is an important first step in developing a data governance framework.
It is important to understand the current state of an organization’s data management processes and technology infrastructure before designing the framework. Apply a data maturity model to act as a benchmark and guide for improvement. This will help to identify any gaps that need to be addressed by the framework.
One of the most important things to remember when creating a governance framework is to engage stakeholders early and often throughout the process. This ensures everyone understands the framework’s goals and buys into its implementation.
It can also ensure that all current data usage and management best practices are accounted for and optimized for the new framework, regardless of what department is using the data.
Trying to cram too many rules and procedures into a governance framework can be tempting. However, it’s essential to keep things simple in order to promote organization-wide adoption and compliance.
No matter how carefully a governance framework is designed, there will always be unforeseen circumstances that arise. As such, it is important to create a flexible framework that can change with organizational needs over time.
SEE: For more detailed information, check out our guide on data governance best practices.
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