What is Data Cleaning

Definition

Data Cleaning is the removal, correction, or normalization of errors, omissions, duplicates, and strange values in data. Simply put, this concept helps build reliable services around models: data, compute, access, deployment and monitoring. In practice, it helps to understand what capabilities the tool actually has, what data it will need, and what limitations are worth checking before implementation.

Example

Before training the model, the team removes duplicate requests, corrects dates, and processes empty fields.

Why it matters

Without data cleaning, even a strong model can learn from garbage and produce unreliable results. This helps you choose AI tools not by big promises, but by how they work in a real problem.

How it works

Typically, the process starts with data sources and the environment, then sets up calculations, access, automation, monitoring, and security rules. In the case of the term “Data Cleaning”, it is important to look separately at the data, quality criteria and application conditions.

Where it is used

• It is found in projects where data storage, computing, integration, deployment, security and stable operation of AI services are important.

Limitations

Limitations are related to computational cost, security, data quality, latency, service availability, and maintenance complexity.