1. Why is data important for AI projects?

Artificial intelligence works by learning from data. Without data, AI cannot learn, analyze, or make decisions. Data is the “fuel” that powers machine learning and deep learning algorithms. From predicting customer behavior and optimizing production processes to automating customer service—every AI task relies on data to function accurately.

2. Types of data in an AI project

Data in AI projects can be categorized into several groups:

• Training Data: Used to train the AI model.
• Testing/Validation Data: Used to evaluate model accuracy.
• Real-time Data: Continuously updated data from production systems or users.
• Unstructured Data: Text, images, videos, audio, etc.
• Structured Data: Data in databases with clear formats.

3. How data quality affects AI effectiveness

High-quality data enables the AI model to learn correctly, accurately reflect real-world behavior, and generalize better. Conversely, incorrect, incomplete, or inconsistent data will cause the model to learn improperly, deliver inaccurate results, and potentially lead to serious consequences for businesses.

Example: In customer service, if an AI chatbot is trained with flawed or context-lacking data, it may respond confusingly, reducing user experience.

4. Common challenges in data processing

Main challenges when working with data in AI projects include:

• Data scattered across multiple sources
• Unclean data with errors, duplicates, or missing values
• Incorrectly labeled data (for supervised learning)
• Lack of standardized data management and storage systems
• Difficulties ensuring data privacy and user security

5. Steps for effective data management in AI projects

To ensure data quality and project success, businesses should follow these steps:

1. Define objectives and required data types: Clarify AI goals to determine necessary data.
2. Standardize and clean data: Remove incorrect or missing data and normalize formats.
3. Accurately label data: Especially important for supervised learning models.
4. Secure and store data efficiently: Use standardized data management systems like AWS, Azure, GCP.
5. Ensure legal compliance: Particularly with user data, comply with GDPR, Vietnam’s Decree 13, etc.

6. Real-world case study from a successful AI enterprise

A client in the logistics sector deployed an AI solution to forecast shipping demand. Initially, the model delivered inaccurate results due to poorly formatted and error-filled input data. After applying data standardization and cleansing steps, model accuracy improved from 62% to 91% in just three weeks.

This highlights that data quality directly determines the practical success of an AI solution—both technically and from a business perspective.

7. Conclusion: Data as the foundation of AI success

In the AI era, data is no longer a supporting resource but a critical factor in every digital strategy. An AI project, no matter how advanced the technology, will likely fail without appropriate, sufficient, and clean data. Therefore, investing properly in infrastructure, processes, and personnel for data governance is a long-term strategy every business should pursue to maximize AI’s potential.