Page 35 - Ai V2.0 Flipbook C8
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Data Acquisition
Data Acquisition is the foundation of a successful AI project. In this stage, relevant data is
identified, collected, and organised for further analysis and modelling. Since AI systems learn
from data, the quality, quantity, and variety of data directly impact how accurately the AI
system can perform.
Without proper data acquisition, even the most advanced algorithms will produce poor results.
Hence, this stage ensures the AI model has a reliable and representative dataset that reflects
real-world conditions.
Following are the points that shows why is data acquisition important:
• It provides the raw material for building AI models.
• Helps in identifying patterns, trends, and relationships.
• Influences the accuracy, fairness, and efficiency of AI outcomes.
• Aids in detecting biases and making the system more inclusive.
Types of Data Collected
The success of any AI system depends heavily on the quality and variety of data collected
during the Data Acquisition stage. Different problems require different types of data, and each
data type brings unique value and challenges.
Textual Data
Textual data consists of information presented in written or printed form—typically as letters,
words, sentences, or paragraphs.
Stages of AI Project Cycle 33

