Copilot in Power BI: AI-Assisted Report Building

Copilot in Power BI revolutionizes how we build reports and analyze data. This guide explores how to effectively use Copilot for report creation and data analysis.

Copilot Capabilities

COPILOT_CAPABILITIES = {
    "report_creation": {
        "features": [
            "Generate report pages from descriptions",
            "Create visuals based on questions",
            "Suggest layouts and formatting",
            "Auto-populate with relevant measures"
        ],
        "examples": [
            "Create a sales dashboard with regional breakdown",
            "Build a customer analysis page",
            "Design an executive summary"
        ]
    },
    "visual_assistance": {
        "features": [
            "Recommend chart types for data",
            "Suggest visual configurations",
            "Add complementary visuals",
            "Optimize visual design"
        ]
    },
    "dax_help": {
        "features": [
            "Generate measures from descriptions",
            "Explain existing measures",
            "Suggest optimizations",
            "Create calculation groups"
        ]
    },
    "insights": {
        "features": [
            "Explain data patterns",
            "Identify anomalies",
            "Suggest drill-down paths",
            "Generate narratives"
        ]
    }
}

Creating Reports with Copilot

# Copilot prompt patterns for effective report creation

REPORT_PROMPTS = {
    "executive_dashboard": """
    Create an executive dashboard that shows:
    - Total revenue with YoY comparison
    - Sales by region map
    - Top 10 products by revenue
    - Monthly trend for the last 12 months
    Use a clean, professional layout with KPI cards at the top
    """,

    "customer_analysis": """
    Build a customer analysis page with:
    - Customer count and average order value KPIs
    - Customer segmentation by value tier
    - Purchase frequency distribution
    - Customer lifetime value trend
    Include filters for time period and region
    """,

    "operational_metrics": """
    Create an operations dashboard showing:
    - Order fulfillment rate gauge
    - Average delivery time by region
    - Inventory levels by category
    - Orders by status breakdown
    Add a time slicer for date filtering
    """
}

# Best practices for Copilot prompts
PROMPT_BEST_PRACTICES = [
    "Be specific about the measures and dimensions to use",
    "Mention the visual types you prefer",
    "Specify the layout structure",
    "Include filtering requirements",
    "Reference time periods explicitly"
]

DAX Generation with Copilot

# Copilot-assisted DAX creation

DAX_PROMPTS = {
    "time_intelligence": {
        "prompt": "Create a measure for same period last year comparison",
        "generated": """
        SPLY Sales =
        CALCULATE(
            [Total Sales],
            SAMEPERIODLASTYEAR('Calendar'[Date])
        )
        """
    },

    "running_total": {
        "prompt": "Create a running total of sales that resets each month",
        "generated": """
        Running Total MTD =
        CALCULATE(
            SUM(Sales[Amount]),
            DATESMTD('Calendar'[Date])
        )
        """
    },

    "pareto": {
        "prompt": "Create measures for Pareto (80/20) analysis of products",
        "generated": """
        Product Cumulative % =
        VAR CurrentProduct = MAX('Product'[ProductName])
        VAR AllProductsSales =
            CALCULATETABLE(
                SUMMARIZE(
                    Sales,
                    'Product'[ProductName],
                    "Sales", [Total Sales]
                ),
                REMOVEFILTERS('Product')
            )
        VAR RankedProducts =
            ADDCOLUMNS(
                AllProductsSales,
                "Rank", RANKX(AllProductsSales, [Sales], , DESC)
            )
        VAR CurrentRank =
            MAXX(FILTER(RankedProducts, [ProductName] = CurrentProduct), [Rank])
        VAR CumulativeSales =
            SUMX(FILTER(RankedProducts, [Rank] <= CurrentRank), [Sales])
        VAR TotalSales =
            SUMX(RankedProducts, [Sales])
        RETURN
            DIVIDE(CumulativeSales, TotalSales)
        """
    }
}

Smart Narratives

# Copilot generates smart narratives to explain data

NARRATIVE_EXAMPLES = {
    "sales_summary": """
    Copilot analyzes your data and generates:

    "Total sales reached $4.2M in Q1 2024, representing a 12% increase
    compared to Q1 2023. The North region led performance with $1.5M,
    driven primarily by a 23% surge in the Electronics category.

    Key observations:
    - March showed the strongest growth at 18% YoY
    - Customer acquisition increased by 2,400 new accounts
    - Average order value improved from $125 to $142

    Areas requiring attention:
    - South region declined 5% due to supply chain issues
    - Returns rate increased in Apparel category"
    """,

    "anomaly_explanation": """
    When Copilot detects an anomaly, it explains:

    "The sales spike on March 15th (+340%) was primarily driven by:
    1. Flash sale event in Electronics (contributed 65% of increase)
    2. New product launch in Home & Garden
    3. Promotional email campaign reaching 50K subscribers

    Similar patterns observed on:
    - Black Friday 2023
    - End of year clearance"
    """
}

Building Interactive Reports

# Using Copilot suggestions for interactivity

INTERACTIVE_FEATURES = {
    "drill_through": {
        "copilot_prompt": "Add drill-through from regional summary to customer details",
        "implementation": {
            "source_page": "Regional Summary",
            "target_page": "Customer Details",
            "drill_fields": ["Region", "CustomerID"],
            "keep_filters": True
        }
    },

    "bookmarks": {
        "copilot_prompt": "Create bookmarks for different analysis views",
        "generated_bookmarks": [
            {"name": "Overview", "filters": [], "visuals": "all"},
            {"name": "Regional Focus", "filters": ["Region"], "visuals": "map, table"},
            {"name": "Trend Analysis", "filters": ["DateRange"], "visuals": "line charts"}
        ]
    },

    "tooltips": {
        "copilot_prompt": "Add detailed tooltip showing customer metrics on hover",
        "tooltip_page": {
            "size": "small",
            "visuals": [
                {"type": "card", "measure": "Total Purchases"},
                {"type": "card", "measure": "Average Order"},
                {"type": "sparkline", "measure": "Monthly Sales"}
            ]
        }
    }
}

Q&A with Copilot

# Natural language Q&A examples

QA_EXAMPLES = {
    "simple_queries": [
        "What were total sales last month?",
        "Show me the top 5 customers",
        "Sales by category as a pie chart"
    ],

    "comparative_queries": [
        "Compare sales between Q1 and Q2",
        "How did this month compare to last year?",
        "Which region had the biggest growth?"
    ],

    "analytical_queries": [
        "Why did sales drop in March?",
        "What's driving the increase in returns?",
        "Which products are trending up?"
    ]
}

# Q&A configuration for better results
QA_CONFIGURATION = {
    "synonyms": {
        "revenue": ["sales", "income", "earnings"],
        "customer": ["client", "buyer", "account"],
        "product": ["item", "SKU", "goods"]
    },
    "featured_questions": [
        "What's our monthly revenue trend?",
        "Who are our top customers?",
        "Which products are selling best?"
    ]
}

Tips for Effective Copilot Use

COPILOT_TIPS = {
    "prompting": [
        "Start with clear, specific requests",
        "Build complexity incrementally",
        "Reference existing visuals when modifying",
        "Ask for explanations when unsure"
    ],

    "data_model": [
        "Ensure good naming conventions",
        "Add descriptions to measures",
        "Use hierarchies for drill-down",
        "Define relationships properly"
    ],

    "iteration": [
        "Review generated content before accepting",
        "Ask follow-up questions to refine",
        "Combine Copilot output with manual edits",
        "Save useful patterns for reuse"
    ]
}

Conclusion

Copilot in Power BI dramatically accelerates report development while maintaining quality. Use clear prompts, iterate on suggestions, and combine AI assistance with your domain expertise for the best results.