Week 4: Missing Data and Fields

Not such a common issue in today’s data rich world…is it?

We are running a series of blogs on what the most common issues are around data analytics for auditors, so from a lack of knowledge/direction and strategy through to false positives and inconsistent output along with how to handle missing data sets and fields, so, without further ado, here’s blog number 4 on Missing Data and Fields

Tip 1: Know what you need

Taking time to plan effectively should flush out any issues you may have with any missing data or fields. If there was a problem last time, were there solutions or was the problem just carried over?

Tip 2: If you don’t know what you need…ask someone that does

Is there anyone within the organisation who has data analytics experience? If not, try potential sources such as more experienced business users, systems or user documentation and external user forums or even Google. (You can always ring us, of course!)

Tip 3: Data Management

We have touched on part of this in previous blog posts. Get to know who is responsible for the data that you are about to analyse. Who has overall responsibility for your organisation’s data quality or data integrity (DQ/DI)? Who are the feet on the ground – like the database administrator?

Tip 4: When you validate your data, check again

Are the data and fields that you think are missing actually absent? Could they be somewhere else? This is more common than you might think!

Tip 5: Relevance of what’s not there

Consider how critical any missing data or fields are to your analysis. How much of an issue will this cause? What can you do in the meantime?

Author: David Ryan

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