The guiding process to data governance
Now that you have identified a good use case, it’s time to follow a set of best practices to ensure a comprehensive process that allows you to deliver intelligent, scalable data governance. With years of experience working with hundreds of use cases, we have identified five key steps to build a governance model tailored to your unique needs.
Five key elements ofsuccessful data governance
Step 1: Align stakeholdersStep 2: Establish confederationStep 3: Foster a culture of accountabilityStep 4: Execute continuous improvementStep 5: Define data standards and practices
Use this step to identify and engage the stakeholders specific to the use case you selected. This includes clearly understanding who owns the data, who consumes it and how they will interact with your data assets.
First, list the people in your organization who are involved in your chosen project, including those who use the data. Then, explain how this project and the data will benefit these users.
Connect these benefits to the goals of your organization. This will help you get the support you need from your leaders. Important points to consider:
List the people and critical users for this project
Explain how this project will help the users achieve their business goals. For example, it could help them find and analyze data more quickly or make compliance checks easier
Share this information to get support from the people involved so you can start the project. Also, the first project’s scope must be decided to keep it manageable
What defines success?
You have identified the main people who are part of the project
They agree on the project’s scope, expected business outcomes and the necessary resources
Create a common business glossary to:
Standardize business terms
Set data standards
Make things work better and faster
Capture the success metrics
Make data governance inclusive for everyone
Moving to a cloud-native hybrid data landscape can help companies become more agile and capable of scaling and pivoting in uncertain markets.By sharing data, context, and analyses, businesses can create innovative solutions to solve today’s most difficult business problems.
However, modern enterprises have different departments, teams and business units, eachwith its own operating style and needs. This diversity can make it challenging to maintain consistency in data governance.
That’s why the best approach to data governance is not a one-size-fits-all. Instead, it will allow everyone to participate in one unified system of data governance while allowing everyone to design a model that enables them to define roles, responsibilities and terms while still integrating into the broader goal of becoming data-driven.
Future-proof your data governance processes
Now that you’ve aligned your stakeholders and established confederation, the next step is to ensure clear ownership of critical roles in your business processes. The primary way to scale your data governance processes is to implement higher-level business processes to help your organization maintain the system.
These processes can include setting up a regular data standards refresh cycle, creating an update process for curated data and implementing best practices for handling certified assets. The main goal for this step is to establish and enforce policies and processes that allow you to separate raw data from curated or certified data.
Key considerations:
Define the ownership and stewardship models for your data: data owners, approvers and consumers
Refine the communities and domain structure that will serve as the basis for data governance
Assign the roles identified in Step 1 to govern the data hosted in these communities and domains
Document the key processes required to govern data
Build automation for the most common workflows to improve efficiency and ensure process compliance
Prioritize, iterate and monitorcontinued success
To ensure good data governance, it’s essential to customize governance priorities for each area and use iteration to adapt quickly. This means promptly enabling priority use cases and addressing longer term development.
Once a certified set of well-monitored data assets is available to departmental data consumers, it’s a good idea to establish a data governance council. This council, consisting of department and central data team members, can oversee the ecosystem of data standards, policies and new data product requirements. The council can track relevant metrics to monitor data quality, availability and delivery to data consumers and approve new investments in projects that positively impact these metrics.
Ideally, the council meets monthly or quarterlyfor business reviews to guide continuous improvement initiatives governing the data governance process.
Establish common data definitions and best practices for your data assets
The last essential step in building a mature governance process is to define and implement standardized data definitions and practices for your data assets.
Understand and document key data policies for access and retention
Categorize data assets based on their sensitivity, criticality and usage
Associate data policies with relevant data assets
The Collibra Data Governance solution enables you to establish data policies aligned with your operating model and helps set boundaries ondata asset utilization.
Success entails:
Clearly defined data policies for all data assets in the use case
Access to data policies for all users
Automated workflows to ensure policy compliance
Congratulations! You have established a governance model for your first use case. Now, implement this five-step process for each of your remaining critical use cases so you can reap the full benefits of intelligent data governance.
