What Is Data Preparation? Intro, How-to, and Best Practices

Data preparation is an important aspect of data management. It helps your organization by combining data from multiple raw sources into one valuable, high-quality dataset. Data preparation is an essential step toward improving processes and making informed, data-driven decisions.

This article will help you understand what data preparation is and when to use it. Also, this article will explain four simple data preparation steps to get you started.

Let’s first learn the basics of data preparation.

What Is Data Preparation?

Data preparation means collecting data, processing or cleaning it, and consolidating it. The focus of data preparation is mostly on the consolidation of data. Different techniques exist to help you transform one or multiple raw datasets into one usable, high-quality dataset. The goal is to transform a raw data set into high-quality data that’s ready for analysis. Further, you can use this data to help you validate a business case or make business decisions.

However, the data cleaning step is also important. During this step, you’ll measure the quality of the data. In some exceptional cases, you may even decide to discontinue the data formatting process because the data quality is too low.

Why Does Data Preparation Matter?

Why would you want to prepare data? You might argue, “My organization already captures tons of data, including Twitter statistics, product usage, and session time on our website. Why would we need further data processing?”

Data processing adds value by combining different sources of data, creating a richer dataset. This allows your organization to make more informed decisions.

For example, you may capture data about how users use your digital service and also capture heatmap data for your service. You might analyze both datasets separately, but it’d make much more sense to combine those datasets to get deeper insights. In addition, by combining datasets, you’ll get results that are more accurate.

What are some other reasons data preparation is important?

  • You can fix errors in your dataset. It’s easier to fix errors in your initial, raw dataset up front rather than fixing them later, after you’ve combined them. Once you’ve merged them, it’s much harder to solve those data inconsistencies.
  • You can create a data-driven culture. Data is the most valuable asset for your organization. Data allows you not only to identify problems but also to improve existing processes. It forms the ground floor for innovation within your organization. Besides, a data-driven culture allows sharing data across departments so everyone can benefit.

Next, let’s learn when you should use data preparation.

When Should You Use Data Preparation Techniques?

There are four main reasons to use data preparation techniques. (However, many other reasons exist.)

  1. First of all, data preparation is useful when merging data from different raw sources into one dataset.
  2. Second, data preparation helps with converting messy, inconsistent, or unstandardized data into a high-quality dataset.
  3. Next, it helps with cleaning or improving manually entered data.
  4. Last, it makes sense to use data preparation techniques when dealing with unstructured data that’s been scraped from different sources. Let’s say you’ve used a bot to scrape contact details from websites. Often, you’ll end up with unstructured and messy data that needs to be prepared before you can use it effectively.

Next, let’s take a look at how data preparation works in practice.

4 Easy Steps to Get Started With Data Preparation

Let’s explore these steps to get you started.

#1: Understand Your Data.

Before you can start clean or format your data, you need to understand it. Learn about the different fields your data holds. If you’re combining data from different sources, it makes sense to write down links between different data fields. For example, in one dataset you’ll find fullName, but the other dataset defines the name field as full_name.

After you’ve gained a clear understanding of your data, you can start the next step.

#2: Profile Your Data.

During the data profiling step, you’ll decide if the data is worth further processing. Start by performing data sanity checks. This includes checking whether the data makes sense. Let’s say the address field in your dataset only contains numbers, and the price for a bottle of wine is set to 10,000 euros. This is a clear red flag that your data isn’t worth further processing.

During this step, you can also apply metrics. For instance:

  • Count the number of missing or null fields.
  • Count the number of incorrectly formatted fields.

However, these metrics require more effort, such as writing a script to count those values or validating data types. You should be able to determine the quality of your data by quickly scanning your dataset for extreme anomalies. When you receive a dataset that has a lot of missing fields, then it’s clear you shouldn’t spend further effort on processing this data.

Tip: Don’t perform data profiling on a large dataset. Start small by profiling only a small segment of your data to avoid wasting further efforts.

#3: Clean Your Data.

Obviously, data cleaning matters. It’s an iterative process that focuses on producing a high-quality dataset.

Important tasks related to data cleaning include:

Tip: Don’t try the above techniques all at once. Start with one technique, and then evaluate the data quality. If your first technique doesn’t work out, then try another technique on the original dataset. Data preparation is an iterative process in which you strive to find the optimal technique or combination of techniques.

How Should You Handle Missing Values?

The main problem with most datasets is the number of missing values. Earlier in this article, you saw why it often makes sense to fill in those fields manually if possible. However, for a large dataset, this isn’t very scalable. Therefore, some data engineers prefer to remove incomplete data if it’s not of high value. For smaller datasets, it’s a good idea to manually fill in missing fields to increase the data quality.

Another technique tries to compute missing values. Let’s say you have a dataset that holds the number of passengers per flight. For some flights, this number is missing. You can easily calculate the mean or median values for this dataset.

These computation techniques can be very accurate. If you have data about the flight destination, luggage weight, or time of departure, then you can filter data records for calculating the mean or median value. Let’s say you miss a value for a flight from Berlin to Brussels. You could calculate the average number of passengers by using data from other flights from Berlin to Brussels.

The main disadvantage of this approach is the possible inaccuracy of the data. It’s better to not calculate too many values using this technique, or you’ll end up with a biased dataset that may lead to poor decisions.

#4: Store Your Data.

Now you’ve filtered down your data to a usable, high-quality dataset, so it’s time to store the data. You can do so in a database, or you can pass it to a data intelligence tool for further analysis.

Also, document the data preparation process you’ve conducted. This gives you valuable insights into why a certain technique didn’t work or which combination of techniques works well. This information is key to improve and streamline further data preparation operations.

As you can see, when you understand data preparation, it’s actually not that difficult. In essence, you’ll want to clean data to end up with a high-quality dataset. Remember, data preparation is an iterative process. Not every technique will work well for your dataset. Good luck experimenting with data preparation!

This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!