How do you govern and analyze data effectively?

3 How do data governance and data analytics relate?

Data governance and data analytics are closely related and interdependent. Data governance provides the foundation and framework for data analytics, by ensuring that your data is of high quality, trustworthy, and accessible. Data analytics leverages the data governance capabilities, by using your data to create value, impact, and outcomes. Data governance and data analytics also influence and support each other, by providing feedback, learning, and improvement.

4 How do you implement data governance and data analytics?

To ensure effective data governance and analytics, you should establish a clear vision, mission, and goals for your data initiatives that align with your business strategy and stakeholders' needs. Additionally, define and document your data policies, standards, and procedures and communicate them to data owners, custodians, users, and consumers. Assign roles and responsibilities to data stewards, analysts, scientists, and champions with adequate training, tools, and support. Implement processes for data quality management, security management, privacy management, and compliance management. Additionally, adopt a data lifecycle management approach that covers all stages from creation to disposal. Use descriptive, diagnostic, predictive, and prescriptive analytics to answer questions and test hypotheses. Visualize data findings with charts, graphs, dashboards, or stories. Lastly, evaluate results to improve data governance and analytics practices.

6 What are the challenges of data governance and data analytics?

Data governance and data analytics are not simple to implement and maintain, and they can face a variety of challenges. These can include a lack of data leadership, resources, and alignment, as well as a lack of trust, transparency, and value. Furthermore, there can be unclear or conflicting data vision, strategy, and priorities, as well as insufficient or outdated data infrastructure, technology, and tools. Additionally, diverse or incompatible data sources can present an issue when attempting to standardize the data. Lastly, it can be hard to measure the impact of the data and demonstrate its benefits and ROI.

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?