SQL for Data Analysis Questions

Approach

I would automate the steps that are repetitive and easy to validate first: import the CSV, clean the headers, normalize the data types, refresh summary sheets, then hand off PDF export to Power Automate or a similar wrapper if I am using Office Scripts. That is because Office Scripts is great for workbook logic, but PDF distribution is usually better handled outside the script.

Robustness

• Check that the expected columns exist before doing anything else.
• Trim header names, remove extra spaces, and standardize case.
• Reject or log rows with missing required fields.
• Parse dates carefully and flag values that do not convert.
• Write problems to an error sheet instead of failing silently.
• Refresh pivots or formulas only after the raw data passes basic validation.

Sample Office Script pattern

function main(workbook: ExcelScript.Workbook) {
  const raw = workbook.getWorksheet('Raw');
  const errors = workbook.getWorksheet('Errors') || workbook.addWorksheet('Errors');

  // Read used range and validate headers
  const range = raw.getUsedRange();
  if (!range) throw new Error('Raw sheet is empty');

  const values = range.getValues();
  const headers = values[0].map(h => String(h).trim().replace(/\s+/g, ' '));

  // Example: ensure required columns exist
  const required = ['Date', 'Amount', 'Region'];
  required.forEach(col => {
    if (!headers.includes(col)) throw new Error('Missing column: ' + col);
  });

  // Clean summary sheets after validation
  workbook.refreshAllDataConnections();
  workbook.getWorksheets().forEach(ws => {
    if (ws.getName().startsWith('Summary')) {
      ws.getUsedRange()?.getFormat().autofitColumns();
    }
  });
}

That structure makes the automation easier to debug and safer when the source CSV is messy.

My build approach

1. Convert the transaction range into an Excel Table so the source expands automatically.
2. Insert a PivotTable on a separate summary sheet.
3. Put Date in Rows and group it by Months and Years.
4. Add Region, Product, and Channel as filters or slicers depending on how much detail the audience needs.
5. Put the measure, such as Sales Amount or Units, in Values and format it as currency or number.

For trend lines

• I would create a PivotChart from the PivotTable, usually a line chart for monthly trends.
• If the audience wants comparison across regions, I would use region as a legend series or create separate small charts.

Making it usable for non-technical stakeholders

• Add slicers for Region, Product, and Channel.
• Add a timeline for date filtering if the source is a real date field.
• Hide field buttons and use friendly labels like "Monthly Revenue" instead of technical field names.
• Use a clean layout with one summary tab and one detail tab.
• Keep number formatting consistent and avoid showing raw pivot clutter.

Performance and usability

• Refresh from the Table, not a copied range.
• Keep calculations simple inside the pivot.
• If the workbook is large, I would avoid lots of extra formulas on top of the PivotTable and let the pivot do the aggregation.

How I would investigate it in Excel

1) Validate the export

• Check that the CRM extract has the right date range, required columns, and no obvious duplicates.
• Confirm whether sales means booked revenue, closed-won deals, or another definition.

2) Compare the right time windows

• Look at last week versus the prior week, but also compare against the same days in previous weeks so day-of-week effects do not mislead us.
• Separate the drop into volume, average deal size, and conversion rate if the CRM has pipeline stages.

3) Segment the problem

• Build PivotTables by region, rep, product, channel, and deal stage.
• Check whether the decline is broad-based or isolated to one segment.
• Look for missing large deals, lower activity, or a stage where opportunities stalled.

4) Check for operational causes

• Lost deals, fewer new opportunities, delayed closes, or a sudden change in source mix.
• If the CRM has timestamps, I would also check whether the drop is just a timing issue from late-week logging.

5) Final answer

• I would summarize the biggest driver first, then give one or two supporting pivots or charts.
• If the root cause is unclear, I would be explicit about that and recommend the next data point to collect.

The key is to move from "sales are down" to a specific explanation like "the decline came mainly from one region and a few large deals slipping out of the close window."

How they compare

• SUMIFS / COUNTIFS / AVERAGEIFS are best when I need a formula-driven output in a specific cell, such as one KPI tile or one row in a report.
• PivotTables are better when I need fast exploration, multiple groupings, drill-down, and a layout that non-technical users can filter.

When I prefer conditional aggregates

• The report needs a fixed template with stable cells.
• I want formulas that are easy to copy across months or categories.
• I need results tied directly to dashboard tiles or other formulas.
• I want transparent logic that can be audited cell by cell.

When I prefer PivotTables

• The question is exploratory or may change often.
• I need summaries by many dimensions, like region, product, and channel.
• I want slicers, timelines, drill-through, or a chart-ready summary.
• The dataset is large enough that formulas would become slow or cluttered.

When I avoid conditional aggregates

• If the workbook has many rows and many formulas, recalculation can get heavy.
• If the grouping needs to be dynamic, PivotTables are usually easier to maintain.
• If stakeholders need self-service filtering, a PivotTable is more user-friendly.

In practice, I often use both: a PivotTable for exploration and a few SUMIFS or COUNTIFS formulas for the final dashboard tiles.

Structure I would use

• Raw data tab: one Excel Table holding the source data only.
• Calc/helper tab: normalized fields, date groups, and any lightweight helper columns.
• Dashboard tab: KPI tiles, a few trend charts, and slicers/timelines.

Formulas and layout

• Use the raw table as the single source of truth.
• Build KPI tiles with SUMIFS, COUNTIFS, or AVERAGEIFS when the numbers need to react instantly to filters.
• If the workbook is large, I would summarize first in PivotTables and point the dashboard to those results.
• Use named ranges or table references instead of hard-coded cell addresses.

Performance choices

• Avoid volatile functions unless they are truly needed.
• Prefer table references over whole-column formulas.
• Keep chart series small and focused.
• Use one or two helper columns for grouping instead of many nested formulas.
• Reduce duplicated logic by calculating once and reusing the result.

Drill-down and usability

• Add slicers for the main dimensions.
• Add a timeline for date filtering.
• Keep labels plain-language and numbers formatted consistently.
• Hide technical sheets so stakeholders only see the dashboard and the raw source if needed.

My rule is to make the dashboard fast to open, easy to refresh, and simple enough that a non-technical user can filter it without breaking the workbook.