Creating a data governance framework is crucial to becoming a data-driven enterprise because data governance brings meaning to an organization’s data. It adds trust and understanding to data, accelerating digital transformation across the enterprise.
However, many organizations struggle to build a data governance program because the practice can seem amorphous. A data governance framework eliminates the complexity by providing a guide for identifying organizational priorities and needs, developing a plan to address these priorities and needs with data, and executing that plan. Consequently, a data governance framework makes it easier for organizations to use their data as an asset and scale data governance across the enterprise.
What is a data governance framework?
A data governance framework maps out the components of a data governance strategy from ideation to execution. Using this framework encourages organizations to dive deeper into their enterprise goals and challenges, enabling them to precisely identify their needs and achieve fast, measurable, and scalable results.
4 pillars of the data governance framework
There are four pillars to the data governance framework that enable organizations to make their data a fruitful asset.
1. Distinct use cases
In order to get buy-in from stakeholders and drive adoption, it is essential to link the need for data governance to business results. Data governance champions should identify the business initiatives and the challenges that the organization faces in order to determine use cases. These use cases typically fall under three categories [Figure 2]:
- Grow the business (revenue focused)
- Run the business (cost focused)
- Protect the business (risk focused)
The number of use cases for data governance is practically limitless. But the data governance framework emphasizes that an organization must start small. They must prioritize addressing just a couple of use cases when first establishing data governance, rather than trying to boil the ocean.
2. Quantifiable value
Notice that all the use cases above are quantifiable. It is essential to have a value-quantified data governance program, meaning that the impact of data governance is measurable. This pillar of the data governance framework allows organizations to examine the success of the data governance practice and provide guidance on how to proceed in the future.
3. Targeted product capabilities
Organizations also should invest in technology to empower all teammates to make the most out of their data. When evaluating technology offerings, consider the needs of all the individuals involved in the data processes affecting the addressed use cases. The capabilities of a data governance solution should address each of those needs. Typically the capabilities revolve around helping teammates:
- Discover data assets with an intuitive and searchable catalog – including definitions, categorization and segmentation
- Understand the data’s origin, classification, content and use
- Trust the data’s accuracy and compliance with internal policies and external regulatory requirements
- Collaborate across the organization to share newly created data assets, provide feedback and resolve issues
- Access data while ensuring compliance with usage and sharing policies
4. Scalable delivery model
Lastly, a data governance program must have a scalable delivery model. Notice that the diagram for the data governance framework is not linear; it represents a cyclical process. After an organization addresses the first identified use cases, it should establish data governance as a scalable service. This way, organizations can address more use cases, impact more teams and tools, and bring incremental value each time, while reducing incremental effort.
Stakeholders in the data governance framework
Data governance is a cross functional effort. The data governance team acts as the facilitator for the rest of the organization, so it is essential to include stakeholders from different divisions and functions when fulfilling the data governance framework. Stakeholders vary by organizational structure and use case, but they typically fall under a few categories:
- Data governance – This team is the owner of the data practice and leads the creation of the data governance framework. Members on the data governance team:
- Own the enterprise data practice and processes
- Standardize definitions and business terms across the organization
- Coordinate and resolve data issues
- Empower users to get the most of their data
- Privacy and compliance – This team owns all things related to data privacy and its goal to ensure regulatory compliance regarding the organization’s handling of personal data. Members on this team:
- Run the data privacy program
- Document how data is used
- Conduct data protection impact assessments (DPIAs) to examine and manage risk
- Fulfill reporting and audit requests
- Line of business (LOB) – LOB can span many different departments, finance, marketing, analytics and more. LOB uses data everyday to support business decisions. Their team members:
- Analyze data from multiple sources to formulate reports and assist in trustworthy, data-driven decision making
- Create, own and maintain reports and dashboards
- Leverage data and BI tools for analysis and visualization
- Data science – Data scientists want easy access to certified, trusted data, so they can quickly build and deploy models that contribute to higher quality analysis that ultimately drives the business forward. Data scientists look to:
- Gather , clean and analyze data data from various data sources
- Build, train, deploy and optimize predictive and prescriptive analytic models
- Interpret data and models to uncover business opportunities
- Present findings to relevant stakeholders
- Information technology (IT) – The IT team manages the support, administration and design of computer systems. IT team members get involved with data because they:
- Manage, support and deploy internal technology systems
- Collaborate with data governance to standardize workflows and monitor activities
- Help the LOB understand the data architecture
- Ensure information security
To round out the data governance framework, it is critical to include data stakeholders across all teams for conversations about use cases, KPIs, capability requirements and scaling data governance for the future.
Defining data governance requirements
As data teams implement a data governance solution, they must create a list of requirements that address their organization’s data needs. It is important to remember that data governance equips the rest of the organization to use data productively. Therefore, a solution must be suitable for more than just the immediate data governance team; it must meet the needs of the LOB and IT as well. A comprehensive data governance solution offers the following capabilities:
- Active data governance: Standard solutions include governance features, such as a business glossary, a data help desk, and data dictionary, but best in class solutions offer active data governance, which minimizes manual processes and automates the building, monitoring and updating of these tools.
- Data catalog: A data catalog creates a central location for the line of business to discover data. A data governance solution should incorporate a data catalog to promote collaboration and data usage.
- Privacy by design: A data governance solution should embed policy management, access permissions and discovery and classification of sensitive information to ensure compliance with a growing set of privacy regulations.
- Collaborative workflows and management: Collaboration is crucial to getting the most out of data, so data governance must offer workflows to streamline processes and offer a space for communication around data.
- Connectivity: Data governance must break down silos, between both people and technology. Solutions therefore must integrate with existing systems in an organization’s data environment.
- Secure architecture: Data is an organization’s most valuable asset, so all technology must protect this precious commodity. Consider solutions built to adhere to industry standards, such as FedRamp Security Controls, ISO 27001 and PCI DSS.
Scaling the data governance framework for the future
Data governance is not a one-time project; it is a practice that an organization needs to embed in its business strategy and use on an ongoing basis. One of the principal pillars to the data governance framework is the scalable delivery model, because organizations should apply the data governance framework to additional teams and use cases over time, so they can make the most of their data assets and transform into data-driven enterprises.
Collibra Data Governance
With Collibra, organizations can implement the data governance framework and accelerate time to value. Collibra Data Governance automates governance activities and enables cross-functional teams to establish a common understanding around data, facilitating collaboration and innovation. Collibra delivers active data governance, an enterprise data catalog, wide-range connectivity, and collaborative workflows and management all in one secure enterprise grade platform. Collibra offers a number of templates and customizations on a flexible operating model to help teams design a data environment that evolves in tandem with organization. With Collibra, organizations can transform their data governance into enterprise wide Data Intelligence.