FREE INTERACTIVE TEACHING DEMO · FICTIONAL DATA

Before the dashboard, check the data.

A sales table looks ready for a chart. It has a duplicate, missing information and values that will distort a summary. Find the issues, choose a defensible treatment and see what changes.

Start the audit ↓MBA workshop path ↗

12 sample rows4 quality checksNo upload or sign-in

01Inspect the source

02Choose treatments

03Defend the result

STEP 01 · INSPECT

What would you flag?

This sample deliberately contains problems. Red labels show issues in the source; they are not automatic corrections.

Fictional order records used for this exercise
Order	Date	Region	Channel	Revenue (₹)	Audit flags

STEP 02 · CHOOSE

Decide how to prepare it.

These are teaching choices, not universal rules. A real team should agree treatments with the data owner and preserve a record of excluded rows.

Remove the repeated order IDKeep its first occurrence; set aside the later copy for review.Exclude invalid revenue from this summarySet aside “N/A” and negative revenue. In real work, confirm whether a negative is a refund before excluding it.Label a blank region “Unknown”Keep the sale while making the missing category visible.Standardise channel labelsCombine “Web”, “ web ” and “WEB” as Web without changing the source table.

STEP 03 · INTERPRET

What does the result support?

Choose your treatments above, then compare the summary.

Rows retained—

Rows set aside—

Positive-revenue rows in chart—

Charted revenue—

Revenue by channel

Only positive numeric revenue enters this chart. Labels remain separate until you standardise them.

Explain your decision

A good business answer states which rows were removed, how missing values were handled and what the chart cannot prove.

Rows set aside

None selected yet.

MAKE IT A WORKSHOP

Teach data quality before dashboard design.

For MBA cohorts or professional teams, I can adapt this into a live Excel-to-Power-BI exercise around your learners' level and business questions.

Request a workshop proposal ↗

This is a fictional learning example, not an audit of your data. Nothing you select is uploaded or stored.