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.
- What is a data governance framework?
- Types of data governance frameworks
- Components of a data governance framework
- Examples of data governance frameworks
- Why is a data governance framework necessary?
- Best practices for creating a data governance framework
- Applying data governance frameworks and best practices to your business
What is a data governance framework?
Data governance is the process of managing data throughout its entire lifecycle, from acquisition to archival. It includes defining policies and standards for handling data and ensuring compliance with those policies. On the other hand, data management is the process of storing, organizing and retrieving data. While data governance frameworks are concerned with the overall management of data, data management focuses on the more practical tasks of day-to-day administration.
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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. For data to be reliable for decision making, it must be relevant, trustworthy, accurate, high quality and easy to understand. This requires close collaboration between different departments and business units when creating a data governance framework. Data governance frameworks also need to account for regulations and compliance requirements.
Types of data governance frameworks
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, 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 data set 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.
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; however, it can be challenging to apply in organizations with a large volume of data.
Components of a data governance framework
There are four primary components of a data governance framework:
Data stewards ensure that an organization’s data assets are accurate, consistent and compliant with all relevant regulations, especially over the course of company projects.
Data quality management
Data quality management includes all processes and procedures used to ensure that an organization’s data assets are free from errors and inaccuracies. lt also includes methods for identifying and correcting any errors or inaccuracies.
Data management processes
These processes define how an organization’s data assets are created, stored, accessed and used. They also establish the rules for how those assets will be shared with internal and external stakeholders.
This refers to the hardware and software systems used to collect, store and manage data. It includes databases, enterprise resource planning systems and data warehouses. It also includes the network connections that facilitate the exchange of information between stakeholders.
Examples of data governance frameworks
Below is a list of some commonly referenced data governance frameworks:
- PwC enterprise data governance framework
- DAMA DMBOK
- The SAS data governance framework
- The DGI data governance framework
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.
Why is a data governance framework necessary?
A data governance framework is necessary because it 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 that all users understand the rules for managing data and that there is a clear process for making changes or additions to the data. A good governance framework unifies teams, improving communication between different teams and allowing different departments to share best practices.
Finally, a data governance framework helps to ensure 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.
Best practices for creating a data governance framework
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. However, there are some best practices that all organizations should keep in mind:
Define the purpose of the framework
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.
Understand the organization’s current state
It is also 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.
Engage stakeholders early and often
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 that 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.
Keep it simple
Trying to cram too many rules and procedures into a governance framework can be tempting. However, it is essential to keep things simple to promote organization-wide adoption and compliance.
Plan for flexibility
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.
Applying data governance frameworks and best practices to your business
Every organization wants to reap the benefits of becoming more data-driven, but getting there requires more than just collecting data. It also requires a well-designed data governance framework to ensure that data is managed effectively and remains compliant with relevant laws and regulations. By following the best practices outlined above, organizations can create a data governance framework that meets their specific needs and industry requirements to help them achieve their desired business outcomes.
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