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In the 30th session of Chandoo.org podcast, let’s learn how to uncover fraud in data.

What is in this session?

In the wake of hedge fund scams, accounting frauds and globalization, We, analysts are constantly second guessing every source of data. So how do you answer a simple question like, “am I being lied to?” while looking at a set of numbers your supplier has sent you.

That is our topic for this podcast session.

In this podcast, you will learn

• Quick announcements about 50 ways & 200k BRM
• Introduction to fraud detection
• 5 techniques for detecting fraud
  • Benford’s law
  • Auto correlation
  • Discontinuity at zero
  • Analysis of distribution
  • Learning systems & decision trees
• Implementing these techniques in Excel
• A word of caution

Who is the most consistent of all?

Imagine you are a category manager at a large e-commerce company. Your site offers various products, but you don’t really make these products. You list products made by other vendors on your site. Every day, these vendors would send you invoices for the amount of product they have sold. Above is a snapshot of such invoices.

Looking at this list, you have a few questions.

1. Who is the best seller?
2. Who is the most active seller?
3. Who is the most consistent seller?
4. Which seller has fewest invoices?

Let’s go ahead and answer these using Excel. Shall we?

Situation: Our commissions are growing way faster than revenues
Let’s say you are looking revenues & sales commissions of your company for last few years. The data looks like this:

And you want to highlight the fact that commissions are growing faster than revenues.

So you plot YoY growth rates for revenues & commissions.

Problem: The chart of YoY growth rates is not convincing

Take a look at the chart. It doesn’t convey the message that we want. At best it says “revenue growth is less than commission growth”

How to convey the message “Commission growth is a problem for us”?

Last week, I asked my email newsletter readers to submit “one data analysis problem you are struggling with”. We called it BYOD – Bring your own data. More than 100 people have emailed various interesting (and often very difficult) problems. This week (between 16th of February to 20th of February), let’s take a look at some of these problems and solve them.

Consolidating data in different shapes

We can use either VBA or Excel’s consolidation features to combine data that has same shape (ie same number & type of columns). Here is one way to do it.

But what if we need to consolidate data that is in different shapes?

Something like above.

In such cases, we can use 3 powerful tools.

1. Multiple Consolidation Ranges – Pivot Tables
2. VBA
3. Power Query

So let’s examine how to use these approaches to consolidate data in different shapes.