A Useful List Of Audit Tests For Data Quality

It’s the time of year when many companies start wrapping up their numbers ready for reporting and audit.

Here are six checks that we find useful when processing data for our clients.

1. BAD DATA

If you’re collating data in spreadsheets there’s a decent chance that hidden in there are #N/As and #VALUEs that have been missed. Make sure all errors are out.

2. CROSS CHECKS

Build in cross checks into every sheet. Do the summary totals match the detail totals?

3. YEAR ON YEAR VARIANCE

Check for variance at multiple levels.

At a total level, do the numbers look in line? What if you drill down a bit more?

What’s your threshold. Any variance over 20% will need a closer look.

4. COMPLETENESS

High level variances often occur because data is missing.

Do a completeness check – is everything there that you expect or are there holes?

And if there are holes, can you get the missing data in time or do you need to patch with estimates?

5. OUTLIERS

Now drill into the detail.

Say you have 12 numbers for the year for a particular source, like electricity usage.

Do they look reasonable, in line with seasonal trends, or are there outliers that need explaining?

6. LIKE FOR LIKE VARIANCE

Next look at like for like detail.

Compare each source this year to the same period last year.

Your auditors won’t tell you the exact tests they’re using or the thresholds they use to identify anomalies.

But if you do these checks you’ll be in a good position for an audit – and hopefully the observations and improvement points will be minor.

One final thing – where should you do these checks?

They need to be done BEFORE you load data into a system. It’s the GIGO principle – get the dataset ready and clean before you try and feed it into the next stage of a process.

Get in touch if you’d like to see how we do this in practice.