How to create a Data Governance Framework

To create a data governance framework, you can follow these steps:

1. Define your goals and objectives. What do you want to achieve with your data governance framework? Do you want to improve the quality of your data, reduce the risk of data breaches, or make it easier to share data across your organization? Once you know your goals, you can start to develop policies and procedures that will help you achieve them.

2. Identify your data assets. What data does your organization collect, store, and use? Once you know what data you have, you can start to develop a plan for managing it.

3. Classify your data. Not all data is created equal. Some data is more sensitive than others, and some data is more valuable than others. Classifying your data will help you to prioritize your data governance efforts and to develop appropriate security and access controls.

4. Define roles and responsibilities. Who will be responsible for different aspects of data governance? You will need to identify roles and responsibilities for data ownership, data quality, data security, and other areas.

5. Form a data governance team. Include representatives from IT, data management, compliance, legal, business units - areas that generate and rely on data.

6. Document existing data assets and architecture. Catalog data sources, systems, policies and controls. Identify gaps.

7. Develop policies and procedures. Once you have identified your data assets, classified your data, and defined roles and responsibilities, you can start to develop specific policies and procedures for managing your data. These policies and procedures should cover a wide range of topics, such as data access, data collection, data storage, and data destruction.

8. Establish processes and rules. Set formal procedures for requesting data access, resolving issues, and enforcing policies through training, reviews and sanctions.

9. Implement your data governance framework. Once you have developed your policies and procedures, you need to implement them and train your employees on how to follow them.

10. Implement tools and technologies. Deploy data catalog software, data quality tools, and master data management platforms to automate governance.

11. Create metrics and reports. Track key indicators around data quality, policy violations and incidence response to monitor effectiveness.

12. Promote through training. Educate stakeholders on the framework - their obligations and the value of governance for managing risk.

13. Monitor and improve your data governance framework. Data governance is an ongoing process. You need to regularly monitor your data governance framework to make sure that it is effective and to make adjustments as needed. Ongoing reviews and updates. Evaluate processes regularly. Adjust governance as data environment evolves, ensuring it remains relevant.

Additional Tips

Here are some additional tips for creating a successful data governance framework:

• Get buy-in from leadership. Data governance is a strategic initiative, so it is important to get buy-in from senior leadership.

• Involve stakeholders across the organization. Data governance affects everyone in the organization, so it is important to involve stakeholders from all departments in the development and implementation process.

• Make it easy to follow. Your data governance framework should be easy to understand and follow. Avoid using jargon and technical language.

• Automate as much as possible. There are a number of data governance tools and technologies available that can help you to automate tasks such as data quality monitoring and data security auditing.

• Be flexible. Your data governance framework should be flexible enough to adapt to changes in your business and your data landscape.

Creating a data governance framework can be a complex task, but it is an important investment for any organization that wants to get the most out of its data. Create a data governance framework that will help you to improve the quality, security, and accessibility of your data. An effective, tailored data governance framework improves trust in data, reduces costs and ensures regulatory compliance - providing ongoing strategic value.

​Here are some examples of how some companies have created data governance frameworks:

• Google: Google has a comprehensive data governance framework that covers all aspects of data management, from data collection to data destruction. Google's data governance framework is based on the following principles:

• Data ownership: Google data is owned by the business unit that collects and uses it.

• Data quality: Google data is accurate, complete, and timely.

• Data security: Google data is protected from unauthorized access, use, disclosure, disruption, modification, or destruction.

• Data compliance: Google data is used in compliance with all applicable laws and regulations.

• Netflix: Netflix has a data governance framework that is focused on protecting the privacy of its customers and ensuring that its data is used ethically and responsibly. Netflix's data governance framework is based on the following principles:

• Data minimization: Netflix only collects and uses the data that it needs to operate its business.

• Data transparency: Netflix is transparent about how it collects and uses data.

• Data choice: Netflix gives its customers choices about how their data is used.

• Data security: Netflix protects its data from unauthorized access, use, disclosure, disruption, modification, or destruction.

• Walmart: Walmart has a data governance framework that is focused on improving the quality of its data and making it more accessible to its employees. Walmart's data governance framework is based on the following principles:

• Data accuracy: Walmart data is accurate and complete.

• Data accessibility: Walmart data is accessible to the employees who need it to do their jobs.

• Data consistency: Walmart data is consistent across all systems.

• Data security: Walmart data is protected from unauthorized access, use, disclosure, disruption, modification, or destruction.

Sash Barige

Sep/22/2018

Photo Credit: Unsplash.com