In the world of data, there's a rule everyone knows: 80% of the time and effort in a project is spent on obtaining and preparing data, while only 20% is used for analysis, exploration, and visualization.
Boring, right? Well, yes. Data preparation is often seen as the least glamorous stage of the Data Journey; however, it's an essential and vital process, if not the most important one. It's the art of turning chaos into knowledge, the necessary step to uncover the hidden truths lurking beneath the surface of raw data.
But what is it, why is it so important, and how does it work? In this article, we'll tell you everything. Let’s go!
First of all, what exactly is data preparation?
As you know, data is everywhere and has become the true engine of the digital age: the exponential growth of applications, our enormous reliance on the Internet in our daily lives, the explosion of IoT, social networks, e-commerce... All of these factors explain the constant development of data-centric activities.
In this context, new professions and roles have emerged and continue to emerge in companies, such as analysts, data scientists, engineers, or architects, among many others. Each of them specializes in a different part of the process, yet they all share a common need: high-quality information. Raw data is often unstructured, duplicated, incomplete, chaotic, and inconsistent, making its effective use challenging. This is the fundamental role of data preparation: to fix all of that.
We can define it as the process of cleaning, organizing, and transforming raw, unprocessed data into a format that can be analyzed and used to derive business insights. It's the crucial first step in any data analysis project, and its main goal is to ensure that data is clean, structured, and ready to reveal significant insights and maximize its quality and utility.
And why is it so important within businesses?
Your decisions depend on the data that supports them, so it's essential that this data is of high quality; otherwise, everything built on top of it is likely to be incorrect. Therefore, data preparation is that vital first phase of any data project, ensuring the cleaning, validation, and guarantee of quality, reliability, and coherence at the data source that you will use to ensure good decision-making.
Data comes from different sources and is in different formats. Thanks to data preparation, your data will be integrated and transformed into a consistent and compatible format. This will allow you to combine and use data effectively, facilitating your analyses and the generation of valuable information. But you need to know that not all data is relevant.
Data preparation also involves identifying and removing data that is not useful for your analyses, reducing noise and unnecessary information. By eliminating incorrect or inconsistent data, the overall quality of the data is improved, saving you time and resources and preventing these errors from negatively impacting your final results.
This phase will also help you discover hidden patterns and trends since cleaning, transforming, and aggregating data can reveal relationships and correlations that were not evident in the raw data.
Therefore, data preparation is crucial because it provides you with a solid and consistent foundation for obtaining meaningful insights, informed decisions, and better results. It will give you greater confidence and understanding of the data you are using, allowing you to ask better questions, conduct more precise analyses, and thus optimize your decisions. It's like laying down solid foundations for a building before construction; it will help ensure that everything is correct and of quality before starting any of your projects.
In summary, without proper data preparation, any analysis or decision-making would be based on incomplete, inconsistent, or erroneous information, meaning that anything built on top of it is likely to be flawed. The quality of data at the source is fundamental in any project you undertake. By investing time and effort in data preparation, you are ensuring that the entire subsequent analysis process is carried out on a solid and reliable basis and that the decisions you make are the most accurate.