How can you govern marketing data effectively?

1 Assess your current state

The first step is to evaluate your current marketing data landscape and identify the gaps, issues, and opportunities for improvement. You can use a data audit or a data maturity model to assess the quality, completeness, consistency, and accessibility of your data sources, systems, and processes. You should also document your data flows, dependencies, and integrations, as well as your data roles, responsibilities, and ownership.

2 Define your goals and metrics

The next step is to define your data governance goals and metrics based on your business objectives, stakeholder needs, and industry best practices. You should align your data governance strategy with your marketing strategy and ensure that your data supports your marketing goals and KPIs. You should also establish clear and measurable data quality indicators, such as accuracy, timeliness, relevance, and completeness.

3 Establish your data governance framework

The third step is to establish your data governance framework, which is the set of rules, roles, and tools that guide and enable your data governance activities. This framework should include a data governance charter outlining the vision, mission, scope, and principles of your initiative, as well as a data governance committee to oversee coordination and ensure accountability and alignment. Additionally, you should create a roadmap defining the priorities, timelines, and milestones of your projects and tasks. You should also develop a policy for data collection, storage, processing, sharing, and usage along with a toolset to support data governance functions such as cataloging, profiling, cleansing, validation, monitoring, and reporting.

4 Implement your data governance processes

The fourth step is to implement your data governance processes, which are the actions and tasks that you perform to execute your data governance framework. These processes should involve inventory and classification of data assets based on sources, types, formats, values, and sensitivity. Data quality management should be used to guarantee validity, reliability, and integrity through data cleansing, enrichment, standardization, and verification. Data security and privacy management should be established to protect from unauthorized access, use, modification, or disclosure through encryption, masking, anonymization, and compliance. In addition, data lifecycle management should manage the creation, maintenance, retention, and deletion of data according to business rules and legal requirements. Lastly, data access and distribution management should control who can access and use the data and how they can do so through roles, permissions, and workflows.

5 Monitor and measure your data governance performance

The fifth step is to monitor and measure your data governance performance, which is the evaluation and assessment of your data governance outcomes and impacts. You should track and report on your data governance metrics, such as data quality scores, data usage rates, data value creation, and data issue resolution. You should also collect and analyze feedback from your data users, stakeholders, and customers to understand their data needs, expectations, and satisfaction.

6 Continuously improve your data governance practices

The sixth and final step is to continuously improve your data governance practices, which is the ongoing optimization and enhancement of your data governance capabilities and results. You should review and update your data governance framework, processes, and performance regularly and adapt to the changing needs, demands, and trends of your business, market, and industry. You should also foster a data-driven culture and mindset within your organization and promote data literacy, awareness, and collaboration across your marketing teams and functions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?