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.

Best design for this problem

I would treat the customer table as a history table with effective start and effective end dates for each segment record. That makes the join logic much safer than trying to guess which duplicate row is current.

Table design

• CustomerID
• Segment
• StartDate
• EndDate

Each order then matches the row where the customer matches and the order date falls inside the effective date range.

Formula approach

=IFERROR(
  INDEX(CustomerHistory[Segment],
    MATCH(1,
      (CustomerHistory[CustomerID]=[@CustomerID]) *
      (CustomerHistory[StartDate]<=[@OrderDate]) *
      (CustomerHistory[EndDate]>[@OrderDate]),
    0)
  ),
"Unknown")

Why this works

• It handles customers who change segments over time.
• It avoids accidental matches to the wrong historical record.
• It gives a clean fallback when no record exists.

If I wanted a simpler model

• I would use Power Query to merge orders to the customer history table and filter by the date range.
• That is often easier to audit than a very long formula.

If there are duplicate history rows with the same date range, I would fix the source data first, because the lookup should not guess between conflicting records.

Checks I would run

• Completeness: count blanks in required fields like ID, date, amount, and region.
• Validity: confirm dates fall inside the reporting period and amounts are numeric.
• Duplicates: look for repeated transaction IDs or impossible repeated records.
• Negative values: separate true refunds or reversals from likely errors.
• Outliers: use filters, conditional formatting, or a PivotTable to spot unusually large amounts.
• Reconciliation: compare totals against a control total if one exists from the source system.
• Consistency: make sure categories, statuses, and date formats are uniform.

How I would judge fit for analysis

• If missing values are isolated and explainable, I would flag them and continue.
• If negative amounts are clearly coded as refunds, I would keep them but classify them separately.
• If dates are outside the period, I would confirm whether they belong to late postings or true errors.
• If the workbook fails reconciliation or has many unexplained anomalies, I would not trust it until the issues are resolved.

Practical Excel tools

• Filters and sort
• COUNTBLANK, COUNTIF, and SUMIF
• Conditional formatting for blanks, negatives, and outliers
• PivotTables for exception counts by category

My goal is not just to find bad rows, but to decide whether the dataset is clean enough to support the business question without misleading the reader.

How I choose the lookup method

• XLOOKUP is my default in modern Excel because it is easier to read, can look left or right, and has built-in handling for missing values.
• INDEX/MATCH is my backup when I want maximum compatibility or a more flexible array-based setup.
• VLOOKUP I only use for legacy files or when the table is simple and the lookup column is the first column.

Exact vs. approximate match

• For business reports, I usually want exact match for IDs, names, and codes.
• I use approximate match only for bands or thresholds, like commission tiers or tax brackets, where the lookup table is sorted and the rule is "next smaller" or "closest match."

Left lookups and missing keys

• XLOOKUP and INDEX/MATCH both handle left lookups cleanly.
• If a key may be missing, I return a friendly default like "Not found" or 0 instead of an error.
• If duplicates are possible, I first decide whether the business wants the first match, last match, or all matches. If duplicates are not allowed, I flag them with COUNTIF or a duplicate check before trusting the result.

Example

=XLOOKUP(A2, Customers[CustomerID], Customers[Segment], "Not found")

This is my first choice for a report because it is clear, exact by default, and easier to audit than VLOOKUP.