A data governance framework enables you to define and document your data governance policies and compartmentalize the steps required to provide high-quality, trustworthy data to everyone in your organization whilst maintaining regulatory and internal compliance. Developing a high-quality framework is a critical first step in any data governance initiative.
A data governance framework is the blueprint that defines the roles, responsibilities, policies, and procedures of the data governance initiative, so everyone in the organization knows the plan and is in agreement. It enables you to outline the components of your Data Governance implementation strategy based on the most important use cases.
When you develop a data governance framework for your business the aim is to set out a series of goals and objectives that span the lifecycle of your implementation efforts. Your framework will be unique to your organization and focus on the most critical business requirements.
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A well-designed data governance framework will enable you, from the get go, to define policies and rules, standardize the most important data terms in your organization, document your decision-making process, share your findings, identify data owners, disseminate roles and responsibilities, and construct a solid roadmap based on the most important use cases in your organization.
For an organization to govern a variety of data, it needs to have specific policies in the following area.
A traditional company can move towards self-service, or in a reverse scenario, a fast-moving start-up can start having more controls. In both cases, we need a change management program. Data Governance team should be equipped to provide various training to adapt to these changes.
A company has to align overall data strategy with the business strategy of the company. Only then the data governance programs are successful.
Ethical data handling can increase the trustworthiness of an organization and the organization’s data and process outcomes. Like W. Edward Deming’s statement on quality, ethics means “doing it right when no one is looking.”
Data classification helps the teams find, organize, and secure relevant data. We can classify data as per various categories:
A business glossary helps to solving communication problems by creating a common vocabulary across the entire organization. It additionally ensures the consistency of these terms by synthesizing all of the information of the organization’s data assets through an array of data dictionaries. It then rearranges it into a more understandable and straightforward format.
Data quality must be tracked, managed, and monitored if that data is to drive better business decisions. Therefore, being able to measure and monitor data quality throughout the lifecycle and compare the results over time is an essential ingredient in the proactive management of ongoing data quality improvement and data governance.
There are various elements that constitute an effective data governance framework. These range from the specific roles and responsibilities required to roll out a program, to the management structures and technology you will require to achieve the best results.
When you develop a data governance strategy, you need to ensure you have the right team to implement it. The following roles and responsibilities should be present in your framework.
A Chief Data Officer will oversee the development and organization of a data governance strategy. They are the key point of contact for both the data team and business executives.
A Data Governance Manager will oversee the development and organization of various data governance programs. They are the key point of contact for both the data team and business executives.
Data owners are responsible for maintaining specific data assets and making them accessible to other users. A data owner is not just responsible for data, but accountable for it.
Data stewards oversee the policies laid out in a data governance framework. They must ensure everyone is accessing, using, and maintaining data in a way that complies with the framework.
Related: Data Governance & Data Stewardship Explained
A data governance committee should include data owners and meet monthly or quarterly, depending on your company's requirements.
The aim of the committee is to decide on data policies and standards, manage budgets, determine business terms, and more.
While optional, it's good practice to set up a data governance group that will implement your data governance initiatives. The group will be led by an independent data governance manager and should include the data architect, data analysts, and compliance experts.
There are numerous theoretical frameworks for data governance. The trouble is, most of them are too in depth for regular data users to understand, let alone implement. For example, they could include multiple frameworks for different areas of governance such as data security, metadata management, or data integration.
The framework will provide you with a practical way to implement data governance measures in your organization quickly and comprehensively.
The first thing you need to do before building a data governance framework is to establish what the state of data governance is in your organization. This step will inform how you proceed and the measures you need to take to build a framework.
When you know what data you have in your organization and who is responsible for it, you must set up a data governance committee to implement your data governance program.
There are three initiatives that will help you to set up a successful data governance strategy in your organization. These initiatives are the responsibility of the data governance committee.
Related: How Chief Data Officers overcome three key challenges they face
One of the core purposes of end-to-end data governance is to ensure that data is secure. Although most of the work conducted in this area is performed by IT staff, a company's data governance committee must ensure that security standards are followed.
Data security is largely the responsibility of the IT team, which should conduct regular audits to ensure the data is secure and implement encryption methods to defend the data while it travels from location to location.
The three major aspects of IT data management are:
If you follow all of the measures we have documented in this article, you should have the means to roll out a successful data governance program. Just remember to be patient, taking the implementation phase step by step is a proven way of avoiding failure.
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