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
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