How can you align your Data Governance program with your data visualization and storytelling initiatives?

1 Data Governance dimensions

Data Governance covers four main dimensions: data quality, data security, data architecture, and data literacy. Each dimension has its own objectives, principles, and best practices that guide how you collect, store, access, analyze, and use your data. For example, data quality involves ensuring that your data is accurate, complete, relevant, and timely. Data security involves protecting your data from unauthorized access, modification, or disclosure. Data architecture involves designing and maintaining your data structures, models, and integrations. Data literacy involves developing and enhancing your data skills, knowledge, and culture.

2 Data visualization and storytelling goals

Data visualization and storytelling have two main goals: to inform and to persuade. To inform, you want to present your data in a clear, concise, and accurate way that helps your audience understand what the data means and what insights it reveals. To persuade, you want to craft a compelling narrative that connects your data to your audience's needs, interests, and emotions, and that inspires them to take action or change their perspective. To achieve these goals, you need to use the right tools, techniques, and principles for creating and delivering your visuals and stories.

3 Data Governance alignment benefits

Aligning your Data Governance program with your data visualization and storytelling initiatives can bring several benefits to your organization. First, it can improve your data quality and trust, by ensuring that your visuals and stories are based on reliable, consistent, and relevant data sources that follow your data standards and policies. Second, it can enhance your data security and compliance, by ensuring that your visuals and stories respect your data access rights and permissions, and that you follow the appropriate data protection and privacy regulations. Third, it can optimize your data architecture and performance, by ensuring that your visuals and stories use the most efficient and effective data structures, models, and integrations that support your data analysis and delivery. Fourth, it can boost your data literacy and culture, by ensuring that your visuals and stories foster data awareness, curiosity, and collaboration across your organization.

4 Data Governance alignment challenges

Aligning your Data Governance program with your data visualization and storytelling initiatives can also pose some challenges that you need to overcome. First, it can create data silos and conflicts, by creating different data definitions, rules, and expectations among different teams, departments, or stakeholders that use or produce visuals and stories. Second, it can increase data complexity and volume, by generating more data sources, formats, and outputs that need to be managed and governed. Third, it can limit data creativity and innovation, by imposing too many data restrictions or constraints that hinder your data exploration and experimentation. Fourth, it can require data skills and resources, by demanding more data expertise, training, and support to create and share visuals and stories that comply with your data governance standards and policies.

5 Data Governance alignment strategies

To overcome these challenges and achieve the benefits of aligning your Data Governance program with your data visualization and storytelling initiatives, you need to adopt some strategies that can help you balance your data needs and goals. First, you need to establish a data governance framework that defines your data vision, mission, values, and objectives, and that aligns them with your data visualization and storytelling goals. Second, you need to create a data governance team that includes representatives from different data roles, functions, and domains, and that collaborates with your data visualization and storytelling experts. Third, you need to implement a data governance process that covers the entire data lifecycle, from data collection to data delivery, and that incorporates your data visualization and storytelling requirements and standards. Fourth, you need to monitor and measure your data governance outcomes, by using data quality, security, architecture, and literacy metrics and indicators, and by evaluating how they impact your data visualization and storytelling results and value.

6 Data Governance alignment best practices

To make your data governance alignment strategies more effective and successful, you need to follow some best practices that can help you optimize your data governance performance and value. First, you need to communicate and educate your data governance principles, standards, and policies to your data visualization and storytelling stakeholders, and to solicit their feedback and input. Second, you need to automate and streamline your data governance tasks and activities, by using data governance tools and platforms that can help you manage and govern your data sources, formats, and outputs. Third, you need to adapt and update your data governance framework, team, process, and outcomes, by keeping track of your data changes, needs, and opportunities, and by adjusting your data governance alignment accordingly. Fourth, you need to celebrate and reward your data governance achievements, by recognizing and acknowledging your data governance team and stakeholders, and by sharing your data governance success stories and learnings.

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?