Page 24 - Touhpad Ai

Identification, Acquiring and Exploring the Data

Artificial Intelligence (AI) depends heavily on data to make accurate and informed decisions. Before an AI system can
learn or make predictions, it is essential to identify the right data, collect it from reliable sources, and explore it thoroughly
to understand its patterns and quality. These three steps—Identification, Acquisition, and Exploration—form the foundation
of any AI project.

Let’s go through each step in detail with examples.

Step 1: Identification of Data

The first step in working with AI is understanding what kind of data is required. This is known as the identification
stage. This stage begins with clearly defining the problem you want to solve and deciding what information will help
solve it effectively.

For example, imagine you are trying to build an AI model to predict how well students
will perform in exams. To do this, you must think carefully about what factors influence
a student's exam performance. You need to decide which factors (called features or
variables) might affect exam results—such as study hours per day, attendance percentage,
previous exam scores, and number of practice tests taken.
Selecting the right data is crucial. If you choose irrelevant data or miss important features,
your AI model may produce inaccurate or misleading results.

This step is like planning a journey. Before you start, you must know your destination and what resources you need to
reach it. Similarly, by identifying the right data early, you set the correct direction for your AI project.

Step 2: Acquiring the Data
Once you know what data is needed, the next step is to collect it. This process is called
data acquisition. Depending on your project, data can come from two sources: Primary
data source and Secondary data source.
Primary Data Source

A primary data source refers to the original source from which data is collected
firsthand. This data is obtained directly from its origin, without any intermediary sources
or interpretations. Primary data sources include surveys, interviews, observations,
experiments, and any other method where data is collected directly by the researcher
or organisation for a specific purpose. This type of data is considered valuable because it is tailored as per the specific
research or business needs and is often more accurate and relevant than secondary data, which is obtained from sources
which have already interpreted or analysed the original data.

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