What is Data Preparation? Process & Examples

As companies' and individuals' needs continue to grow, so too does the need to glean insight from large quantities of data to help guide decision-making and overall business strategies. But in order to make sense of all the chaos found in raw data, data scientists and engineers have to figure out the best ways to organize data in a way anyone can understand. 

Data Preparation involves organizing data to ensure it integrates seamlessly into an existing database or can be analyzed without issues.

What is Data Preparation?

Since raw data can come with inconsistencies, errors, redundant information, or just different ways of interpreting the same data, some processing is required to make it all usable. This is where data preparation comes in. 

Data preparation is the act of organizing, cleaning, and ultimately combining data for later analysis. Ideally, data preparation will bring the data up to an “analytics-ready” standard so that it can be properly analyzed and visualized.  

The end result of your data preparation will leave you with an easily readable data set that can also be fed into any analytics software for a more detailed analysis and visualization.  

Why Is Data Preparation Important? 

• Ensures accurate data for analytics
• Standardizes raw data, making it easy to read and consistent
• Removes unwanted or poorly recorded data

Raw data can come from many different sources and databases, and as a result, these data sets can all have different categories, inconsistent data entries, and irrelevant data to what you’re hoping to analyze. Worse still, adding this unorganized data together without any form of preparation can often create a chaotic jumble of meaningless information.

Data preparation is important because it organizes data for accurate analytics, prepares it to be combined with other datasets, and ensures only relevant data is shown and taken into account. It can be as simple as removing all duplicate entries to something a little more complex like converting all measurements to metric: all are valuable tools to make a dataset both easier to analyze and more representative of the information it’s trying to portray. 

Additionally, preparing data future proofs your dataset. The raw data is now more legible for anyone planning to read through the data themselves later, and adding more data in the future is a much easier task as long as the new data is prepared in the same way the old data was prepared. 

Data Preparation Steps

Step 1: Objectives For Analysis

The first step for data preparation is to ask yourself what data you need and what data you don’t. Since you’re starting with a large amount of raw data, you’ll want to plan out what information you want to focus on. A precursory glance at the data itself is important here since you may find relevant data you hadn’t originally considered. 

Step 2: Data Collection

After identifying your objectives, start gathering your data. Look through pre-established databases, scrape through APIs, or sift through files of raw data. 

What matters here is that you pull from multiple sources. This helps combat the potential for inherent bias in a dataset and helps verify the data that you do find. Don’t worry if the data isn’t all exactly the same either. Remember, you’ll be cleaning up the raw data later, so make sure to grab whatever data is relevant first.

Step 3: Data Profiling

This step will have you look through the data and profile it. This means looking for commonalities, discrepancies, missing values, or any odd anomalies that could affect the analytics later. Data sets that contain either too much irrelevant data, or have extremely low quality entries will usually be dropped here. 

A precursory analysis can also be done here just so analysts can see what range and average values they can expect later in the process. 

Data profiling will give you a general idea of how all of your data can come together, but also it gives you an overall view of what steps you’ll need to take to prepare the data for analysis. 

Step 4: Data Cleansing

Once the data has been profiled and the next steps have been identified, it’s time to start cleansing the data. This means removing any irrelevant, incomplete, redundant or irregular entries in your data set. Missing entries in the data set are either filled, or dropped altogether, while mistakes like obvious typos or mismatched entries are fixed.

An important note here is that sensitive information is also censored, masked, or removed in this stage. Information like Social Security numbers, addresses, and full names can be turned into alternate ID codes so individuals can still be identified, but their sensitive information can be safely removed from the overall dataset. 

Step 5: Data Structuring and Transformation

With the data cleansed, it's now time to restructure and organize what remains. This is the step where the data is changed into the same format for later combining, and columns are standardized across the entire data set. 

An example would be if one data set has a column labeled as “price” while another is labeled “USD.” If they’re both saved in the same measurement (in this case, price as US dollars) then combining these columns and renaming them shouldn't affect the dataset overall. However, if the column labeled “price” has data in different currencies, then you’ll have to convert each one to USD before combining it with the other data sets. 

Step 6: Enriching Data

Enriching Data involves creating new columns, adding new data to the dataset, or just adding data that can be inferred from the original data set to make it easier to read. 

Going back to the consumer data, for example, if birthdays were recorded in the original data set, then this step is where you can turn those birthdays into numerical ages instead of dates. Additionally, if another dataset had prices, you can now organize the data by how much was spent on a particular day, or how much was spent based on the common groups of consumers (campaign segments, for example). 

Step 7: Data Validation 

After the data has been transformed and enriched, the next step is to validate the data to make sure the data is still consistent and free from any errors or missing entries. While a version of this was done in the cleaning step, validation is done here to make sure the data set is still valid even after all the changes and reorganization performed by an analyst.

It might sound like an unnecessary step, but double checking the data and running validity tests ensures all of the data is in the right place and following the correct guidelines outlined in step 1. The last thing any analyst wants is to make a mistake that a validity check could have corrected like providing consumer insights regarding their client’s domestic customers in California only to later discover that those same insights were based on data from Canadian markets instead. A check for country code, or for item pricing (USD vs CAD) could have caught this error. 

For large data sets like those used for machine learning algorithms, it’s better to take a sample size of the entire data set and run validity tests on the sample. This will save processing power and time compared to running a test on the entire data set.  

Step 8: Analysis and Publishing

At this point, almost all of the data preparation is completed. The final step is to run the analysis on your now prepared data and see what insights you can glean from it. 

If you plan on publishing your data, sharing it with other teams to use, or just for the sake of future-proofing your clean data set, you’ll want to also provide documentation and proper sourcing of your data set. Answer questions like where did all of this data come from? What objectives did you focus on for this data set? And more importantly, don’t forget to explain any abbreviations you have used. 

This step is useful to have for data preparation since part of the benefit of this entire process is to keep it accessible to anyone who wants to use that data you’ve cleaned. 

Step 9: Analytics Ready Data

Data being analytics-ready means that the raw data has been standardized, useless data has been removed, and the data should be ready for any kind of analytic software. 

Here’s an example of what you might have to consider to get your data prepared for analytics.

Example of Data Preparation

First, let’s say you’re trying to combine consumer data from several different databases to analyze the purchasing habits of your customers. One database may include a consumer’s favorite color and may have saved all of its data in metric, while another database could have saved everything in imperial units and also displays what pets they have. 

Yet another database may have saved dates as month/day/year, while another saved it as year/day/month. It could also be that you’re pulling data from different sources that also have a few duplicate entries. All of these factors will require standardizing, organizing, and in the case of irrelevant data, expunging before all of this data can be combined and used for future analytics.  

Standardizing the dataset’s categories such as dates and units of measurement, removing unwanted data, and ultimately combining all of these datasets into a single legible database would be considered preparing the raw data. 

Big Data And AI Data Preparation

This is also best seen when it comes to big data, especially for AI or machine learning algorithms. Data preparation for these systems may require an extra step of also tagging the data so the algorithm can properly label everything as it learns. 

However, preparing data for machine learning also has the additional benefit of removing any blank or inconsistent entries that could affect the algorithm’s accuracy. Knowing how to transform and clean big data so an algorithm can easily and accurately learn from it is a key factor in how to properly train these models with as few errors as possible.

What To Look For In A Data Preparation Tool 

A good data preparation tool should have a few necessary components to make it comfortable to use. The most obvious one is that it should be able to incorporate data from a variety of different sources, including formats JSON and CSV, or from large databases like SQL Server, MongoDB, and Oracle. By the end of the day, all of these tools should be used to save you time and make data preparation an easy task to accomplish. 

These components include:

• Good security features: Good data preparation tools should provide options to limit users' access to data pipelines and have security measures to prevent unauthorized access to more sensitive data sets.
• Easy ways to transform data: Since there’s so much data to prepare, good tools will have easy ways to get rid of duplicates, convert inconsistent measurements, drop blank cells, and normalize data that may have several different storage options.
• Automated Data Management: If a data preparation tool has an automated means to clean and standardize data, it’ll streamline the entire process and save you hours of work. More complex transformations and conversions may require some coding, but in the least, the data preparation tool has handled the straightforward parts of cleaning the data. 

While not necessary, these characteristics will make your data preparation much easier:

• Code-free tools: Having tools that don’t require any coding makes data preparation much easier and more easily accessible to team members. However, these tools may struggle with more complex datasets that require a specific form of preparation. As a result, be aware of times when these code-free tools may create more of a mess than if you had just written the code yourself. 
• Integration with other Business Intelligence tools: Whether it’s for your own analysis, or if you’re exporting your prepared data to a client, everything will be much easier if the data preparation tool you’re using has connectors or native integration with the Business Intelligence tools you will be using in the future. While there are ways to work around this if these connectors are missing, it certainly makes life much more convenient for all parties involved.