The quality of any artificial intelligence or data science project relies primarily on the quality of the data used. Too often, organizations focus on choosing algorithms or tools without paying enough attention to the preliminary step: data preparation. This phase, which includes identifying data properties, understanding sources, selecting variables, and transforming them, directly determines the relevance and robustness of future analyses.

In a context where companies manage massive amounts of information from various systems, a structured approach to data preparation is essential. It transforms raw, heterogeneous material into an exploitable asset, ready to power predictive models, management dashboards, and strategic decision-making.

Read the complete guide : https://docs.eaqbe.com/data_preparation/data_properties

Three main types of data

The first step is distinguishing the main formats in which data appears. Structured data is the most common: organized into tables of rows and columns, it feeds most relational databases and is easily leveraged by business intelligence tools. A sales record or customer profile is a typical example.

Semi-structured data is a second category. Less rigid than tables, it maintains organization through metadata. JSON or XML formats, widely used to exchange information between applications, are common examples.

Finally, unstructured data encompasses all content stored in its raw form: images, videos, audio files, or free text. Today, this represents the majority of information generated by companies, but exploiting it requires advanced processing and transformation methods.

The nature of variables

Beyond format, it is essential to distinguish the nature of variables. A variable may be qualitative, expressing a state or category, such as gender, color, or country. Some qualitative variables are ordinal, with an implicit hierarchy (e.g., small, medium, large). Others are nominal, with no ranking (e.g., blue, green, yellow).

Quantitative variables, on the other hand, express measurable numerical values such as age, income, or temperature. They may be continuous, with infinitely divisible values, or discrete, limited to integers or fixed steps.

This distinction is crucial because it guides the choice of statistical or machine learning techniques. A classification model will rely on qualitative variables, while a regression model will use quantitative ones.

Exploring data sources

Read the complete guide : https://docs.eaqbe.com/data_preparation/data_sources

Business applications and ERPs : every piece of data has an origin, usually tied to a business application. HR systems, accounting tools, or sales platforms continuously generate and store information. The challenge arises when these applications operate in silos: combining their data becomes complex and hinders the creation of global analyses.

To address this issue, many organizations adopt Enterprise Resource Planning (ERP) systems. These software suites integrate several functional domains into a unified data architecture. They centralize information, reduce redundancies, and facilitate analytical use.

Relational databases and NoSQL : Relational databases remain the cornerstone of many information systems. They organize data into tables linked by keys. Through SQL, they provide power and flexibility to query and use data in dashboards.

With the explosion of data volumes, NoSQL databases have emerged as a complement. More flexible in structure, they prioritize speed and the ability to store massive amounts of heterogeneous information, such as key-value formats. They are particularly suited to Big Data environments.

APIs, data lakes, and data warehouses : Integrating multiple sources increasingly relies on APIs, which allow direct access to application data and automate transfers to other systems. They have become essential in modern architectures.

Two major storage environments then structure analytical use. Data warehouses centralize harmonized and modeled data, ready for decision-making analysis. Data lakes, by contrast, store raw data in its original format for flexible use later, especially by advanced machine learning models.

Combining these approaches allows businesses to benefit from both the rigor of a warehouse and the richness of a lake.

The importance of variable choice

Read the complete guide : https://docs.eaqbe.com/data_preparation/variables_selection

Once data is collected, the next step is deciding which variables to use in the model or analysis. This step is crucial, as it directly affects algorithm performance. Too many irrelevant variables increase computational load, add noise, and reduce reliability. Too few variables risk missing useful signals.

Reducing dimensionality

The “curse of dimensionality” arises when the number of variables grows so large that calculations become too heavy or results unstable. To avoid this, several approaches are possible. Correlation analysis can identify and eliminate redundant variables. Stepwise regression methods or decision trees help retain only the most discriminant variables.

Dimensionality reduction techniques, such as Principal Component Analysis (PCA), condense multiple correlated variables into a smaller set of synthetic components. This improves robustness and speeds up processing while preserving essential information.

Transforming and enriching data

Read the complete guide : https://docs.eaqbe.com/data_preparation/data_transformation

Before use, data must be coherent. Missing, outlier, or inconsistent values need correction. Strategies include replacement with mean, median, or most frequent values; random generation respecting distributions; or supervised prediction using other variables. The goal is always to reduce error impact while preserving representativeness.

Beyond cleaning, feature engineering enriches datasets by creating new information. This can involve simple calculations, such as multiplying quantity by unit price to obtain a total amount. It may also derive variables (e.g., calculating age from a birthdate) or aggregate several columns into a synthetic measure.

This step is strategic, as well-designed variables often add more value to a model than the choice of algorithm itself.

Some algorithms require numeric variables, while others only accept categories. Conversion is therefore often necessary: discretizing continuous values, assigning numeric codes to categories, or applying one-hot encoding to transform a modality into a binary variable.

To ensure fair comparison, quantitative variables must also be placed on the same scale. Normalization and standardization techniques address this, preventing a variable expressed in thousands from dominating another expressed in units.

Data preparation is not a secondary step: it often represents more than 70% of the time spent on a data science project. It ensures models are built on a reliable, coherent, and representative foundation. For businesses, it directly affects the quality of forecasts, the relevance of recommendations, and the accuracy of performance indicators.

By mastering these steps ; understanding data properties, effectively leveraging sources, rigorously selecting variables, and applying thoughtful feature engineering—organizations gain a competitive edge. They transform the raw complexity of their data into strategic assets that inform decisions and support innovation.