SQL for Data Analysis Questions

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 I would clean it without losing raw data

• Keep the original CSV untouched on a Raw sheet or, better, load it into Power Query as the source layer.
• Build a separate Clean output table for analysis.
• Never analyze directly from the raw import.

Cleaning steps

1. Dates: standardize them in Power Query or with helper columns so every date becomes a true Excel date.
2. Spaces and casing: use TRIM, CLEAN, and consistent case rules to normalize text.
3. Headers: unmerge cells in the header area, fill missing header labels, and promote a single clean header row.
4. Product names: create a mapping table for known variants, such as spelling differences or mixed casing, and convert them to one canonical name.
5. Structure: turn the cleaned output into an Excel Table so formulas and pivots can refresh reliably.

Why I prefer Power Query for this

• It preserves the source data.
• It makes the cleanup steps repeatable.
• It is easier to refresh when a new CSV arrives.

Example logic

• Raw product name: widget pro, Widget Pro, WIDGET PRO
• Cleaned product name: Widget Pro

If I had to do it manually, I would still keep the raw import separate, create a staging sheet, and only then publish the final analysis table. That protects the audit trail and makes the workbook easier to trust.

Situation: In a previous role, a recurring report was built by copying data into several sheets, manually fixing headers, and retyping formulas each week. It was slow, and small copy-paste mistakes caused mismatches in the final numbers.

Task: I was asked to make the process faster, easier to use, and more trustworthy for the wider team.

Action:

• I mapped the full workflow and identified where errors were happening.
• I turned the raw input into a structured Excel Table and moved the transformation logic into reusable formulas and a refreshable summary sheet.
• I added simple validation checks for blanks, duplicate keys, and out-of-range values.
• I documented the steps so someone else could refresh the report without knowing the original build logic.
• I ran the old and new versions in parallel for a couple of cycles and reconciled the outputs line by line.

Result: The new process removed most of the manual handling, reduced rework, and made the report easier for others to maintain. Just as important, the team gained confidence because the checks made issues visible before distribution.

Learning: I learned that a good reporting fix is not just automation; it is automation plus validation and documentation.

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.

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.