Data preparation is an essential process when you want to analyse information collected from multiple sources. It consists of cleaning and transforming raw data prior to analysis.

Poor data quality will have a huge negative impact on every data set it is used in. If your data is incorrect, outdated or inconsistent, any insights or analysis derived from it will be flawed.

For example, an organisation may misjudge customer behaviour and make poor marketing or investment decisions based on inaccurate data. As a result, you may be targeting the wrong customers to drive your business.

To give you an idea of the impact, IBM estimates that data quality issues cost US businesses an estimated $3.1 trillion each year in 2016.

Therefore, it is necessary to have cleaned datasets in order to produce reliable and relevant analyses. In this article, I'll explain the key steps to achieving data cleanliness.

Where It All Begins: Gathering Your Data

The first step is to collect your data from sources, it is the very beginning of the process. Without data, of course, you can't do any analysis.

You can use many sources, such as databases, APIs, spreadsheets, or simply records scraped from the web. A good tip is to document in a shared repository and let your colleagues know where the data came from. You can also include the purpose of the collection.

It is also useful to determine the type of data being collected, whether it is structured or unstructured, such as video or images.

From Many to One: Merging Datasets for a Clearer View

Once you have collected your data from sources, the next step is to integrate them into your existing databases. To do this, you'll need to combine the recently collected data into a single dataset.

This will give you an initial, reliable picture of the potential of your data. It can be quite a challenge due to different naming conventions or disorganised and unrelated schemas.

Messy Data? Time to Clean It Up

Great! Now you have a fully integrated database, but when you have a closer look, you see numerous errors, missing information or even worse, duplicate or incomplete records. You have no other solution to clean up your data....

Fortunately, there are many languages, such as Pandas in Python, that can sort your data to make it ready for analysis. Pandas has several methods that you can use to remove duplicates, for example by replacing the inaccurate or inconsistent information.

Also, some software like Power BI have built-in tools to perform these actions if you are used to programming capabilities.

Converting Raw Data into Smart Features: The Power of Transformation

One last step can be useful for machine learning algorithms is the transform of your dataset. In that way, you’ll be able to convert data into the right format or structure for analysis.

This includes tasks such as scaling numeric values, encoding categorical variables, converting date formats, and normalising text. Transformation ensures that different types of data are compatible with analytical tools or machine learning algorithms.

Data Prep Done Right: Final Steps and Optimisation Tips

Consistently following these steps will help you turn messy raw data into a clean, structured and analysis-ready format. Whether you're building dashboards or training machine learning models, solid data preparation sets you up for success.

As a bonus, you can practise data reduction by, for example, removing irrelevant columns to minimise processing time, or creating new variables to better represent the problem you want to analyse.