Hi, I'm Tom Blessing. I'm an expert at helping small to mid-sized companies adopt Power BI. I'm also a recovering investment banker, with deep experience applying technology to otherwise manual data problems. I have dual degrees in Finance and Technology Management from Indiana University.

Resources to help build your fundamentals: Learning Power BI? Data fundamentals are critical for this process to be easier. I've assembled a few DataCamp courses to help.

Introduction to Power BI course: Master Power BI basics and learn to build impactful reports.

Exploratory Data Analysis in Power BI course: Learn about Power BI's Exploratory Data Analysis (EDA) and enhance your reports.

Introduction to SQL course: Learn how to create and query relational databases using SQL in just two hours.

Introduction to DAX course: Learn the fundamentals of Data Analysis Expressions (DAX) such as calculated columns, tables, and measures.

Database Design course: Learn to design databases in SQL to process, store, and organize data in a more efficient way.

Also - need better equipment? For my loyal audience in India, check out Lenovo India.

I earn a small commission on these links if you click and purchase.

Someone once told me that if Airline Pilots use checklists to make sure they keep people’s lives safe, then it’s not a bad idea to use checklists elsewhere too. 

I created a comprehensive Power BI Project Checklist to guide you through the various stages of a data visualization project. 

Here are the high level categories in the checklist:

 Table of Contents

1. Project Planning

2. Data Gathering and Preparation

3. Data Modeling

4. Report Design

5. Report Development

6. Data Integration

7. Data Refresh and Automation

8. Testing and Validation

9. Deployment

10. Documentation

11. User Training

12. Maintenance and Support

13. Governance and Compliance

14. Performance Optimization

15. Scalability

16. Feedback and Continuous Improvement

Let’s jump in.

1. Project Planning:

[ ] Define project objectives and scope.

[ ] Identify stakeholders and their roles.

[ ] Establish a project timeline with milestones.

[ ] Allocate resources, including team members and tools.

[ ] Create a project charter or brief.

[ ] Develop a communication plan.

2. Data Gathering and Preparation:

[ ] Identify data sources and their locations. Need help? Read this.

[ ] Determine data extraction methods (e.g., APIs, databases, flat files).

[ ] Assess data quality and cleanliness.

[ ] Create a data preparation plan.

[ ] Extract, clean, and transform data using Power Query or other ETL tools.

[ ] Establish data refresh schedules.

3. Data Modeling:

[ ] Define the data model structure (tables, relationships, measures). Want to create a precise plan for your database? Read this.

[ ] Create calculated columns and measures using DAX.

[ ] Optimize data model performance.

[ ] Ensure data security and rolelevel security (RLS) as needed.

[ ] Test the data model with sample data.

4. Report Design:

[ ] Define report objectives and audience.

[ ] Design report layouts and visuals for userfriendly navigation.

[ ] Create a style guide for consistent branding.

[ ] Develop mockups or wireframes.

[ ] Design custom visuals and themes if necessary.

[ ] Choose appropriate visuals for data representation.

[ ] Set up slicers and filters for interactivity.

5. Report Development:

[ ] Build reports and dashboards in Power BI Desktop.

[ ] Create interactive elements like drillthrough pages and bookmarks.

[ ] Implement report interactivity using actions and bookmarks.

[ ] Add tooltips for additional context.

[ ] Set up custom measures for advanced calculations.

[ ] Incorporate dynamic rowlevel security (RLS) if required.

[ ] Optimize report performance for large datasets.

6. Data Integration:

[ ] Combine multiple data sources using Power Query.

[ ] Merge, append, or join tables as needed.

[ ] Handle data refresh schedules and data source credentials.

[ ] Implement incremental data refresh if applicable.

[ ] Test data integration thoroughly.

7. Data Refresh and Automation:

[ ] Schedule data refresh in the Power BI service.

[ ] Ensure that data sources are accessible and uptodate.

[ ] Monitor data refresh for errors and issues.

[ ] Set up email notifications for refresh failures.

8. Testing and Validation:

[ ] Perform unit testing of reports and visuals.

[ ] Validate data accuracy and consistency.

[ ] Conduct user acceptance testing (UAT) with stakeholders.

[ ] Address feedback and make necessary adjustments.

[ ] Test report performance with real data volumes.

9. Deployment:

[ ] Publish reports to the Power BI service.

[ ] Configure workspace permissions and sharing settings.

[ ] Set up workspace apps for distribution.

[ ] Establish content distribution and access policies.

10. Documentation:

[ ] Create user documentation and training materials.

[ ] Document data source connections and credentials.

[ ] Maintain a data dictionary for reference.

[ ] Document report design principles and guidelines.

[ ] Update documentation as changes are made.

11. User Training:

[ ] Conduct training sessions for endusers.

[ ] Provide guidance on report navigation and filters.

[ ] Offer training on creating custom visuals and reports if needed.

[ ] Share documentation and resources for ongoing learning.

12. Maintenance and Support:

[ ] Establish a support mechanism for user inquiries and issues.

[ ] Monitor report performance and data refresh regularly.

[ ] Implement updates and improvements based on feedback.

[ ] Review security policies and data access periodically.

[ ] Backup reports and data sources for disaster recovery.

13. Governance and Compliance:

[ ] Ensure compliance with data privacy regulations (e.g., GDPR, HIPAA).

[ ] Implement data governance policies and standards.

[ ] Review and manage user access and permissions regularly.

[ ] Audit data source connections and usage.

14. Performance Optimization:

[ ] Monitor report load times and query performance.

[ ] Optimize DAX calculations for efficiency.

[ ] Consider using aggregations for large datasets.

[ ] Review and optimize data source queries.

15. Scalability:

[ ] Plan for scalability as data and user requirements grow.

[ ] Evaluate the need for premium capacities or dedicated cloud resources.

[ ] Assess options for embedding reports in applications if necessary.

16. Feedback and Continuous Improvement:

[ ] Collect feedback from endusers and stakeholders.

[ ] Conduct regular project retrospectives to identify improvements.

[ ] Stay updated with Power BI updates and new features.

Conclusion

By following this Power BI project checklist, you can ensure a structured and organized approach to your data visualization and reporting projects, ultimately leading to more successful outcomes and user satisfaction.