Reporting and Dashboards AI Prompts

Write a clear narrative explanation of this data anomaly for a non-technical business audience. Anomaly: {{anomaly_description}} (e.g. 'Revenue dropped 23% week-over-week in the EMEA region during the week of March 10') Data provided shows the full context. 1. State the anomaly clearly in the first sentence — what happened, how large was the deviation, and when? 2. Provide immediate context: is this the largest deviation in the past 12 months? How does it compare to normal variance? 3. Diagnose the cause using the data: - Drill down by dimension to isolate where the anomaly is concentrated - Check for correlated changes in other metrics - Identify any known external events (holidays, outages, campaigns) 4. Assess business impact: what is the estimated financial or operational impact? 5. State whether this is: - A data quality issue (pipeline error, reporting lag) - A temporary one-off event - The start of a concerning trend 6. Recommend the next action: investigate further / monitor / escalate / no action needed Return: 200-word narrative suitable for a Slack message or email to leadership.

Write a board-level performance report for {{company_name}} for {{period}}. Board-level reports have specific requirements: - Strategic, not operational: focus on business outcomes, not activity - Every number cited must be compared to a target or prior period - Recommendations must be specific and actionable - Maximum 2 pages of text Structure: 1. Performance against strategic KPIs (table): - 4–6 strategic metrics only: revenue, growth rate, NPS, market share, or equivalent - Each: actual | target | vs target | trend 2. Strategic highlights (3 bullets max): - Major milestones achieved this period 3. Risks and challenges (3 bullets max): - Significant risks to the strategy with current mitigation status 4. Decisions required (if any): - Clearly state what decision is being asked of the board and by when - Provide the option set and a recommended option with rationale 5. Outlook: - Full-year forecast vs target - Top 2 tailwinds and headwinds going into next quarter Tone: confident, precise, no jargon. Use active voice. Every paragraph should answer 'so what?'

Write a dashboard specification document for a {{dashboard_name}} dashboard for {{audience}}. The specification should include: 1. Purpose and audience: - What decision does this dashboard support? - Who are the primary users and what is their technical level? - How often will it be used and in what context (daily standup, weekly review, ad-hoc analysis)? 2. KPIs and metrics to display: - For each metric: name, definition, formula, data source, refresh frequency 3. Dashboard layout (describe each panel): - Panel 1: [metric name] — [chart type] — [why this chart type] - List all panels with their position, size, and purpose 4. Filters and interactivity: - Date range selector - Dimension filters (region, product, segment, etc.) - Drill-down capabilities 5. Alerts and thresholds: - Which metrics should trigger alerts and at what thresholds? 6. Data sources and refresh: - Source tables or APIs, refresh schedule, SLA for data freshness 7. Access and permissions: - Who can view, who can edit, any data sensitivity restrictions Return: complete dashboard specification document.

Investigate why two reports are showing different values for the same metric: {{metric_name}} Report A shows: {{value_a}} | Report B shows: {{value_b}} | Difference: {{difference}} Systematically investigate each possible cause: 1. Definition differences: - Is the metric formula identical in both reports? - Are the same inclusion/exclusion filters applied? - Are the same business rules applied (e.g. how refunds are treated)? 2. Date range differences: - Are both reports using the same date range? - Is one using event date and the other using processing date? - Is one using UTC and the other using local time? 3. Data source differences: - Do both reports pull from the same source table? - If different sources, when were they last synced and could there be a lag? 4. Aggregation differences: - Is one report double-counting rows due to joins? - Is one report deduplicating differently? 5. Access differences: - Does one report include data the other user doesn't have access to? For each hypothesis: confirmed / ruled out / needs investigation. Return: investigation log, root cause finding, and recommended fix to align both reports.

Write a monthly business performance report based on the data provided. Structure: 1. Month in review (2–3 sentences): overall performance summary — was it a good month or a challenging one, and why? 2. Key metrics summary (table): - Each metric: this month | last month | MoM % | this month last year | YoY % | vs annual target (% complete) 3. Top 3 wins: specific achievements with numbers. What drove them? 4. Top 3 misses or concerns: what fell short of expectations? What is the root cause? 5. Deep dive (1 topic): - Choose the most important story in the data this month - 2–3 paragraphs with charts described in text - What is happening, why, and what should we do about it? 6. Forecast update: - Based on current month performance, update the full-year forecast - Are we on track, ahead, or behind the annual target? 7. Next month priorities: 3 focus areas with owner and measurable goal Length: 600–800 words. Tone: professional but direct. No bullet soup — use paragraphs for the narrative sections.

Step 1: Audit current reports — inventory all existing reports and dashboards. For each: audience, frequency, purpose, time to produce, and last known use date. Identify reports that are never used. Step 2: Define reporting needs by audience tier — board (monthly, strategic), leadership (weekly, operational), team (daily, tactical). Define the right format, length, and metrics for each tier. Step 3: Design the reporting hierarchy — create a single reporting framework where every metric rolls up consistently from team level to board level with no conflicting definitions. Step 4: Retire redundant reports — list reports to discontinue. Draft a communication plan so stakeholders are not left without information. Step 5: Build the reporting calendar — schedule all recurring reports with owners, data cut-off times, review deadlines, and distribution lists. Step 6: Define the data glossary — for the top 20 metrics in the reporting suite, write agreed definitions, formulas, and data sources so there is one version of the truth. Step 7: Write a reporting strategy document: current state assessment, proposed future state, transition plan, and governance model.

Design a self-serve analytics solution for {{business_team}} to answer their most common data questions without needing analyst support. 1. Conduct a question audit: - List the top 10 most common questions this team asks the data team - Classify each: answerable with a standard report, requires ad-hoc analysis, or needs a new data source 2. Design the self-serve layer: - Which questions can be answered with a pre-built dashboard? Specify the dashboard. - Which questions need a flexible exploration tool (e.g. Looker, Metabase)? Specify the data model. - Which require scheduled reports? Specify format and recipients. 3. Data literacy requirements: - What level of data skill does this team currently have? - What training or documentation is needed for them to use the self-serve layer confidently? 4. Governance rules: - Which metrics need a single agreed definition (to prevent different people getting different answers)? - Who approves new metric definitions? - How are errors or discrepancies reported and resolved? 5. Success metric: how will you know the self-serve solution is working? Return: question audit table, self-serve design spec, training plan, and governance rules.

Generate a weekly business review report based on this data. The report should follow this structure: 1. Executive headline (1 sentence): the single most important thing that happened this week 2. Scorecard (table format): - Each KPI: current week value | prior week value | WoW change % | vs target | status (🟢/🟡/🔴) 3. Highlights (3 bullet points): - What went well this week? Cite specific numbers. 4. Concerns (2–3 bullet points): - What needs attention? Cite specific numbers and why it matters. 5. Context: - Any external events, seasonality, or one-time factors that explain unusual movements 6. Actions: - 2–3 specific actions for next week with an owner and due date for each 7. Next week outlook: - What are we expecting? Any events or decisions coming up that will affect the metrics? Tone: factual, direct, no jargon. Written for a general management audience. Maximum 400 words excluding the scorecard.