Michael eniola

Analyst

Junior data analyst building revenue, customer, and data quality solutions in Excel

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

Graded projects
5
Average best score
76/100
Career level
Analyst
Core tool stack
Excel (5)

About

Working through retail scenarios for a fictional company called Meridian Retail, I have built PivotTables, written multi-condition formulas, and produced charts to answer real business questions about revenue, customer counts, and data quality. Each submission was independently graded against a model answer, so the scores reflect what I actually got right.

My focus so far has been on getting the fundamentals solid: correct formula logic, clean sheet structure, and outputs that a stakeholder can read without explanation. I am building toward more complex analyses, including combination charts and cumulative trend columns, where my earlier submissions show clear room to grow.

Skills

Data Quality

  • Data audit and order status enumeration from raw data
  • Naira number formatting with thousand separators

Excel Formulas

  • SUMIFS with date-range and multi-criteria conditions
  • COUNTA for full row counts
  • COUNTIF for single-condition filtering
  • COUNTIFS for multi-condition filtering
  • Cross-sheet formula referencing
  • Status-level revenue breakdown by exclusion logic

Excel PivotTables and Charts

  • PivotTable construction with row, column, and value field configuration
  • PivotChart creation linked to PivotTable data
  • PivotTable filter configuration using boolean fields
  • Monthly grouping of date fields in PivotTables

Featured work

Retail · Excel

Q4 2023 revenue breakdown by order status for a retail stakeholder

Independent grading: 92/100(verify, opens in a new tab)

Problem

Nura Ibrahim at Meridian Retail (simulated retailer) needed to understand whether order cancellations were worsening across Q4 2023 or whether October had been an outlier. She asked for a full three-month breakdown of revenue by order status, not just a single-month snapshot.

What I did

To answer this, I built a PivotTable on a dedicated Status Breakdown sheet with order status on rows, calendar month on columns, and sum of total_naira as the value field. Placing months in the column area let Nura scan across October, November, and December in a single view without filtering. A stacked bar chart linked directly to the PivotTable shows the monthly revenue mix by status, making the relative size of each status visible at a glance. All seven order statuses were present in the output because I left the dataset unfiltered, which was the right call for a question about whether cancellations were changing.

Finding

The October Grand Total of NGN 56,876,792.48 matched the expected figure exactly, and the Q4 Grand Total reached NGN 177,793,954.30 across all three months, with all seven statuses represented throughout.

Outcome

Nura received a single sheet combining the PivotTable and chart, giving her a month-by-month view of how each order status contributed to revenue across the quarter.

Next step

A useful next step would be to isolate the cancelled and refunded rows specifically and calculate their share of total Q4 revenue each month, to give Nura a cleaner percentage-based trend rather than raw naira values.

  • PivotTable construction with row, column, and value field configuration
  • PivotChart creation linked to PivotTable data
  • Monthly grouping of date fields in PivotTables
  • Naira number formatting with thousand separators

Retail · Excel

Reconciling three conflicting customer counts across marketing, operations, and finance

Independent grading: 92/100(verify, opens in a new tab)

Problem

Chidi Okafor at Meridian Retail (simulated retailer) needed to explain to an investor why marketing, operations, and finance each reported a different total customer number. The task was to replicate all three counting methodologies in Excel and make the differences legible.

What I did

Each count required a different formula to reflect each team's definition of a customer. COUNTA on the full customer range produced marketing's figure, which includes every row regardless of duplicate status or activity. COUNTIF on the is_duplicate column restricted the count to non-duplicate records for operations. For finance, I used COUNTIFS referencing both the is_duplicate column and the total_orders column to isolate non-duplicate customers who had placed at least one order. All three formulas reference the Customers sheet directly, so the summary table updates if the underlying data changes. Colour-coding the three result cells adds a quick visual separation for someone reading the sheet cold.

Finding

Marketing counted 2,331 customers, operations counted 2,131 after removing duplicates, and finance counted 2,105 active non-duplicate customers, all three figures matched the expected values exactly.

Outcome

Chidi had a single summary sheet he could show the investor, with labelled formulas and colour coding that made the reason for each discrepancy self-explanatory.

Next step

It would be worth checking whether any of the 26 customers counted by operations but not finance have total_orders of zero because of data entry gaps rather than genuine inactivity, since that distinction would matter for a retention campaign.

  • COUNTA for full row counts
  • COUNTIF for single-condition filtering
  • COUNTIFS for multi-condition filtering
  • Cross-sheet formula referencing
  • PivotTable filter configuration using boolean fields

Retail · Excel

Monthly customer acquisition PivotTable with non-duplicate filtering for an investor slide

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

Problem

Chidi Okafor at Meridian Retail (simulated retailer) needed a customer growth chart as the centrepiece of an investor presentation slide. The chart had to show only non-duplicate customers, grouped by the month they were acquired.

What I did

The PivotTable was built on a dedicated Customers Growth sheet, pulling from the Customers sheet and filtering is_duplicate to FALSE so that only genuine new customers were counted. Grouping the created_at field by month gave a clean row-per-month layout. The bar chart uses orange bars to match Meridian's brand colour, which was an explicit requirement. The cumulative total formula column and the combination chart format with a secondary line were part of the brief but were not completed in this submission, so the score reflects a partial delivery.

Finding

The PivotTable Grand Total of 2,131 non-duplicate customers matched the expected count exactly, and the monthly distribution across January through December appeared plausible for a full-year spread.

Outcome

Chidi received a correctly filtered PivotTable with a branded bar chart showing monthly acquisition counts, though the cumulative trend line that would have completed the investor slide was missing.

Next step

The missing piece is a cumulative SUM formula column next to the PivotTable and a secondary axis line series on the chart, adding those two elements would complete the combination chart format the investor slide requires.

  • PivotTable construction with row, column, and value field configuration
  • PivotTable filter configuration using boolean fields
  • PivotChart creation linked to PivotTable data
  • Monthly grouping of date fields in PivotTables

Retail · Excel

Initial data audit of a multi-sheet retail workbook

Independent grading: 62/100(verify, opens in a new tab)

Problem

Before any analysis could begin on the Meridian Retail (simulated retailer) dataset, Nura Ibrahim needed a structured record of what the file contained, including its sheets, columns, and any data quality issues visible from a first browse.

What I did

Navigating the workbook, I identified all six data sheets plus the README tab: Orders, Order_Items, Customers, Products, and Inventory were all present and visible. Browsing the Orders sheet, I enumerated all seven distinct order statuses from column F: delivered, returned, confirmed, cancelled, refunded, shipped, and pending. One genuine data quality issue I flagged was inconsistency in the status column values. The findings were documented on a dedicated Data Audit sheet, keeping them separate from the raw data.

Finding

The file contains six data sheets plus a README, and the Orders sheet holds seven distinct order statuses, with at least one data quality issue visible in the status column.

Outcome

Nura received a Data Audit sheet documenting the file structure and flagged issues, giving the team a starting reference before any downstream analysis was built.

Next step

The reviewer noted that the Data Audit sheet content could only be evaluated from a screenshot because the extracted workbook data returned null, so a concrete next step is to verify that the sheet saves and extracts correctly before submitting future audit tasks.

  • Data audit and order status enumeration from raw data
  • PivotTable filter configuration using boolean fields

Retail · Excel

Gross versus net October revenue calculation with status-level breakdown

Independent grading: 62/100(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. She specifically wanted to see gross revenue across all orders and net revenue after removing cancelled, returned, and refunded orders, along with the gap between the two.

What I did

Rather than producing two single totals, I built a status-level breakdown that showed revenue for each of the seven order statuses separately, then summed the eligible statuses (delivered, confirmed, shipped, and pending) to form the net figure. This approach makes it clear which statuses are included and excluded, rather than burying the logic inside a single SUMIFS chain. A GAP row was added to show the difference between gross and net. The reviewer credited the structural approach but noted that the final figures did not match the expected October totals and that naira formatting was not visibly applied.

Finding

The status-level breakdown correctly identified which statuses to exclude for net revenue, but the gross and net totals did not match the expected values for October 2023.

Outcome

The submitted sheet gave Nura a readable structure for comparing gross and net revenue by status, though the figures would need correction before the sheet could serve as a reliable cross-check against the Finance system.

Next step

The most useful correction would be to rebuild the date-range criteria using DATE(2023,10,1) and DATE(2023,11,1) as the lower and upper bounds in SUMIFS, then confirm that the gross total aligns with the NGN 56,876,792.48 already validated in the Q4 PivotTable task.

  • SUMIFS with date-range and multi-criteria conditions
  • Status-level revenue breakdown by exclusion logic
  • Naira number formatting with thousand separators
  • Cross-sheet formula referencing