TOMIWA DAVID

Junior Analyst

Junior data analyst solving retail business questions in Excel: SUMIFS, PivotTables, data audits, and customer metrics

Scores reflect independent, automated grading of simulated business tasks completed on DataSimHQ.

Graded projects
5
Average best score
90/100
Career level
Junior Analyst
Core tool stack
Excel (5)

About

Working through retail scenarios for a fictional company called Meridian Retail, I have practised the full early-career analyst workflow: cleaning and auditing a multi-sheet dataset, writing SUMIFS formulas to separate gross from net revenue, and building PivotTables to break down order trends by status and month. Each submission was graded independently, and the feedback sharpened how I structure deliverables for non-technical stakeholders.

The customer count validation task pushed me to reconcile three different counting methodologies in a single summary table, using COUNTA, COUNTIF, and COUNTIFS together. Across these exercises I have developed a habit of cross-validating results, adding bonus breakdowns, and flagging data quality issues with severity ratings rather than just listing them.

Skills

Data Quality

  • Structured data audit layout with severity ratings
  • Data quality identification across multiple sheets
  • Multi-sheet workbook navigation and cross-sheet analysis

Excel formulas

  • SUMIFS with date-range boundary logic
  • COUNTA, COUNTIF, and COUNTIFS for multi-criteria counting
  • GETPIVOTDATA for cumulative total formulas
  • Named range referencing in formulas

Formatting and presentation

  • Nigerian Naira custom number formatting with thousand separators

Excel PivotTables and charts

  • PivotTable construction with row, column, and value field configuration
  • PivotTable date grouping by month
  • PivotTable filter configuration using boolean criteria
  • Stacked bar chart creation from PivotTable data

Featured work

Retail · Excel

Monthly customer acquisition PivotTable with cumulative growth totals

Independent grading: 72/100(verify, opens in a new tab)

Problem

Chidi Okafor at Meridian Retail (simulated retailer) needed a customer growth chart for an investor presentation. The requirement was a PivotTable of monthly new customer counts filtered to non-duplicate records, paired with a running cumulative total and a combination chart showing both series.

What I did

The PivotTable was sourced from the full Customers range and the is_duplicate filter was set to FALSE, producing a base of 2,131 non-duplicate customers grouped by acquisition month across all 12 months of the year. The cumulative total column was built using GETPIVOTDATA formulas that chain each month's count onto the prior running sum, so the column stays live if the underlying pivot refreshes. The combination chart was built with bars for monthly new customers, but the line series for the cumulative total was missing from the final submission, which cost marks on the chart requirement.

Finding

The PivotTable and cumulative column were correct, with the running total reaching 2,131 by December, matching the deduplicated customer count exactly. The chart component was incomplete because the cumulative line series was not added.

Outcome

Chidi received an accurate monthly acquisition breakdown and a correct cumulative total column, though the chart would need the line series added before the slide was presentation-ready.

Next step

Adding the cumulative line series to the chart and then segmenting the monthly counts by acquisition channel would let Chidi show investors not just how fast the customer base grew but which channels drove each month's intake.

  • PivotTable construction with row, column, and value field configuration
  • PivotTable filter configuration using boolean criteria
  • GETPIVOTDATA for cumulative total formulas
  • PivotTable date grouping by month

Retail · Excel

Structured data audit of a multi-sheet retail dataset with severity-rated issues

Independent grading: 88/100(verify, opens in a new tab)

Problem

Nura Ibrahim at Meridian Retail (simulated retailer) needed the dataset examined before any analysis began. The task was to explore the workbook, document its structure, and flag at least three data quality issues with enough specificity to guide downstream decisions.

What I did

A dedicated Data_Audit sheet was created with columns for issue number, sheet, affected column, description, an example value, and a severity rating. Four genuine issues were identified by browsing each sheet: a fully empty legacy_order_ref column in Orders, a dual-format category column in Order_Items mixing title-case and slug-case values, 200 duplicate customer records in the Customers sheet flagged by the is_duplicate field, and 66 product-store combinations in Inventory carrying negative stock_qty values down to a minimum of negative 25. Severity ratings of High were assigned to the category inconsistency and the negative stock values, reflecting their potential impact on any aggregation built on top of those columns.

Finding

Four data quality issues were identified spanning three of the four sheets, with two rated High severity because they would silently corrupt category-level and inventory-level summaries without a prior cleaning step.

Outcome

The audit sheet gave anyone picking up the dataset a structured starting point for cleaning decisions, with severity ratings that prioritised which issues to resolve first.

Next step

The next step would be standardising the 24 category values in Order_Items down to the 12 true categories using a lookup table, then rerunning any revenue-by-category aggregations to see whether the inconsistency was distorting results.

  • Structured data audit layout with severity ratings
  • Data quality identification across multiple sheets
  • Multi-sheet workbook navigation and cross-sheet analysis

Retail · Excel

October gross vs net revenue reconciliation for finance cross-check

Independent grading: 97/100(verify, opens in a new tab)

Problem

Nura Ibrahim at Meridian Retail (simulated retailer) needed October 2023 revenue calculated in Excel to cross-check against the Finance system. The specific question was how large the gap between gross revenue, covering all order statuses, and net revenue, excluding cancelled, returned, and refunded orders, actually was.

What I did

Both revenue figures were calculated using SUMIFS against the order_date and status columns, with date boundaries set as DATE(2023,10,1) and DATE(2023,11,1) to capture the full month precisely. Named ranges for datetime, revenue, and status kept the formulas readable and easy to audit. The gap was derived with a simple subtraction formula referencing the two results. A bonus breakdown table showing revenue across all seven statuses was added to cross-validate the totals and confirm the logic held across every category.

Finding

Gross revenue for October 2023 was NGN 56,876,792.48, net revenue after removing cancelled, returned, and refunded orders was NGN 38,450,382.35, leaving a gap of NGN 18,426,410.13.

Outcome

Nura received a single sheet with live formulas and full naira formatting that she could hand directly to the Finance team as an auditable cross-check against their system.

Next step

A useful follow-on would be comparing the cancelled and returned revenue shares month-over-month to determine whether October's gap was unusually large relative to the rest of the year.

  • SUMIFS with date-range boundary logic
  • Named range referencing in formulas
  • Nigerian Naira custom number formatting with thousand separators
  • Multi-sheet workbook navigation and cross-sheet analysis

Retail · Excel

Customer count reconciliation explaining why three teams report different totals

Independent grading: 97/100(verify, opens in a new tab)

Problem

Chidi Okafor at Meridian Retail (simulated retailer) faced a situation where marketing, operations, and finance each reported a different customer count from the same dataset. The task was to replicate all three counting methodologies in Excel and produce a table explaining why the numbers differ.

What I did

Three separate formulas were written in a single summary sheet: COUNTA for the raw row count representing marketing's total, COUNTIF filtering on is_duplicate = FALSE for the deduplicated operations figure, and COUNTIFS combining the duplicate filter with a check that total_orders is greater than zero to match finance's definition of an active customer. Column C of the table contains plain-language explanations of what each count means and why it differs from the others. The sheet was named Customer Count Validation and formatted as a labelled table that Chidi could use directly in a stakeholder presentation.

Finding

The three methodologies produced counts of 2,331 for marketing, 2,131 for operations, and 2,105 for finance, with each difference traceable to a specific data rule.

Outcome

Chidi received a ready-to-share table that resolved the apparent discrepancy and gave each team a clear reference for which figure applied to their use case.

Next step

It would be worth segmenting the 2,105 active customers by acquisition channel to check whether certain channels produce a higher share of duplicate or zero-order records.

  • COUNTA, COUNTIF, and COUNTIFS for multi-criteria counting
  • PivotTable filter configuration using boolean criteria
  • Multi-sheet workbook navigation and cross-sheet analysis

Retail · Excel

Q4 2023 revenue by order status with monthly comparison and stacked bar chart

Independent grading: 95/100(verify, opens in a new tab)

Problem

Nura Ibrahim at Meridian Retail (simulated retailer) wanted to move beyond the single October figure and see whether cancellations were worsening across the full fourth quarter of 2023. The deliverable needed to show revenue broken down by order status for October, November, and December, with a chart to make the monthly pattern visible.

What I did

A PivotTable was built with order status on rows and order_date grouped by month on columns, summing the total_naira field across all seven statuses. The Grand Total row was included to allow quick cross-checking against the standalone October calculation. Naira formatting using the custom code for NGN with thousand separators was applied to every value cell in the pivot. A stacked bar chart referencing the Status Breakdown sheet was added beneath the table, making the monthly split by status visible at a glance.

Finding

All seven order statuses were represented across each of the three months, and the October naira total from the PivotTable aligned precisely with the standalone revenue calculation completed in the prior task.

Outcome

Nura had a single sheet combining the numeric breakdown and the chart, giving her a view of whether October's cancellation volume was isolated or part of a Q4 pattern.

Next step

Calculating each status as a percentage of that month's gross revenue would make it easier to compare the cancellation rate across months without the absolute scale obscuring the trend.

  • PivotTable construction with row, column, and value field configuration
  • PivotTable date grouping by month
  • Stacked bar chart creation from PivotTable data
  • Nigerian Naira custom number formatting with thousand separators