Connecting to Data Sources: Linking Power BI to various data sources, including Excel, SQL databases, cloud storage (Azure, Google BigQuery), APIs, and other online services.

Data Import and Dataflows: Importing data into Power BI or setting up dataflows to automate data extraction and transformation for multiple reports.

Real-Time Data Connection: Establishing live connections to real-time data sources (e.g., streaming data from IoT devices or web analytics tools) for up-to-date insights.

Data Cleaning: Handling missing data, removing duplicates, and filtering out unnecessary rows or columns to improve data quality.

Data Transformation: Using Power Query to reshape and transform data, such as by pivoting, merging, or splitting columns for better analysis.

Data Aggregation: Summarizing data by grouping, counting, or aggregating values to create a dataset suitable for high-level analysis.

Relationship Management: Creating and managing relationships between tables in the data model to enable cross-table queries.

Calculated Columns and Measures: Creating custom calculated fields and measures using DAX (Data Analysis Expressions) to add advanced calculations to the data model.

Hierarchies: Setting up data hierarchies (e.g., year > quarter > month) to allow users to drill down into different levels of data.

Building Visualizations: Creating various charts and graphs, such as bar charts, line charts, pie charts, scatter plots, and maps, to represent data insights.

Custom Visualizations: Adding custom visuals from Power BI's visualization library or creating new custom visuals using R or Python scripts.

Formatting and Styling: Customizing colors, fonts, labels, and other visual elements to make reports more engaging and aligned with branding.

Dashboard Creation: Combining multiple visualizations into a single, interactive dashboard to provide a comprehensive view of key metrics and KPIs.

Adding Interactivity: Using filters, slicers, and drill-throughs to create interactive reports where users can explore data at different levels.

Tooltips and Details: Adding tooltips or pop-up details that appear on hover, providing additional context and data points for each visual.

Trend and Forecasting Analysis: Applying time series forecasting or trend analysis to visualize future trends or patterns in the data.

Statistical Analysis: Using DAX to perform statistical analysis and create metrics like median, standard deviation, and percentiles for in-depth insights.

What-If Analysis: Implementing parameters and scenario analysis to explore hypothetical situations and their impact on data metrics.

Geo-Mapping: Displaying data on geographic maps using Power BI’s built-in mapping tools, such as Bing Maps, to visualize location-based data.

Shape Maps and Filled Maps: Using shape maps to highlight specific regions, like countries or states, based on performance or activity levels.

Custom Geographic Visuals: Creating custom geographic data visuals for specific use cases, such as heat maps or radial charts.

Row-Level Security: Implementing row-level security (RLS) to restrict data access at the row level, allowing users to see only relevant data based on their roles.

User Permissions: Setting permissions and managing access to specific reports or dashboards to protect sensitive data and control user access.

Data Compliance: Ensuring compliance with data protection regulations (e.g., GDPR, CCPA) when handling sensitive data within Power BI.

Publishing to Power BI Service: Publishing reports and dashboards to the Power BI Service for access across the organization or external users with permissions.

Embedding Power BI Reports: Embedding reports in web applications, SharePoint, or Microsoft Teams to make insights accessible in other business contexts.

Sharing Reports and Dashboards: Granting users access to specific dashboards or reports and configuring sharing settings to control user permissions.

Automated Refresh: Setting up scheduled data refreshes to ensure data in Power BI reports is up-to-date.

Data Alerts: Creating data-driven alerts that notify users of specific thresholds, such as when sales dip below a certain level.

Power BI Subscriptions: Setting up email subscriptions to send regular report snapshots to stakeholders.

Data Model Optimization: Optimizing data models by reducing the number of columns, creating indexes, or using aggregations to improve report speed.

Query Performance Optimization: Refining queries and using techniques such as query folding to speed up data processing.

Reducing Visual Complexity: Simplifying visuals and minimizing the number of visuals on a report to improve loading times and user experience.

Comments and Annotations: Adding comments and annotations within reports to provide additional insights or context for other team members.

Collaborative Sharing: Allowing multiple users to work on a report or dashboard and share insights through the Power BI workspace.

Feedback Collection: Gathering feedback from users on report usability and effectiveness and making improvements based on their input.

Data Source Maintenance: Regularly updating data sources, verifying data connections, and handling any issues that arise with source integrations.

Content Management and Versioning: Managing report versions, archiving outdated content, and keeping data models up-to-date.

Ongoing Quality Control: Regularly reviewing data accuracy, consistency, and performance to ensure reliable and high-quality reports.