Data Quality: Getting Data Preparation Right is Key! Here’s How

Some 80% of work on data projects is data preparation. Yet, most people pay little attention to it, jumping straight to analysis.

Using poor quality data or poorly understood data leads to poor data analysis.

Or, to quote an old industry adage, “garbage in, garbage out”.

This video addresses this issue by raising awareness and providing general data preparation guidelines, so you have top-notch data quality.

Let’s jump right in!

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Data quality is so important in analytics that there’s an adage for it: “garbage in, garbage out!”. In spite of this, dirty data seems to be a prevalent problem.

We reduced the data prep process to three main dimensions:
Data Understanding
Data Preparation, and
Statistical Preprocessing

1) Data Understanding

First things first, you need to familiarise yourself with the data. The main things to consider are the data source, biases, and missing data.

2) Data Preparation

If there are some obvious mistyped entries, try to fix them first. For the missing data, see if you can fill it in.

There’s no one-size-fits-all when it comes to dealing with missing data - what’s key is that your choices are backed by strong logical reasoning, and all your assumptions are noted down.

3) Statistical Preprocessing

This can range from simple applications, like replacing a missing value with the average, to advanced statistical methods.

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Documentation is a critical component of good quality data. To put a stop to the bad-data cycle, you‘ll need to document all of the transformations in a data dictionary.

If the above sounds like a lot, that’s because it is. It is estimated that somewhere between 50 and 90% of the time taken to complete an analytics project is spent on preparing the data.

That’s a lot! But without it, you risk performing a flawed analysis - remember? Garbage in, garbage out!

Getting data preparation right is key to high data quality and high-quality data analysis.

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We hope this video will help you and your team with accessing and preparing the data for analysis.

Hit like, subscribe and share any comments.

And don’t forget to download our Data Preparation Guide - link above!

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