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DATA MINING

Data mining is the process used by companies to turn raw data into
useful information. By using software to look for patterns in large
batches of data, businesses can learn more about their customers
to develop more effective marketing strategies, increase sales, and
decrease costs. Data mining depends on effective data collection,
warehousing, and computer processing.

How Data Mining Works?
Data mining involves exploring and analysing large blocks of information to glean meaningful patterns
and trends. It can be used in a variety of ways, such as database marketing, credit risk management,
fraud detection, spam email filtering, or even to discern the sentiment or opinion of users.
The data mining process breaks down into five steps:
1. Organisations collect data and load it into their data warehouses.
2. They store and manage the data either on in-house servers or in the cloud.

3. Business analysts, management teams, and information technology professionals access the data
and determine how they want to organise it.
4. Application software sorts the data based on the user’s results.
5. The end-user presents the data in an easy-to-share format, such as a graph or table.

The Data Mining Process

To be effective, data analysts generally follow a certain flow of tasks along the data mining process. The
data mining process is usually broken into the following steps.
Step 1: Define the Problem

Before any data is touched, extracted, cleaned, or analysed, it is important to understand the underlying
entity and the project at hand. What are the goals the company is trying to achieve by mining data?
What is their current business situation? Before looking at any data, the mining process starts by
understanding what will define success at the end of the process.

Step 2: Identify Required Data
Once the business problem has been clearly defined, it’s time to start thinking about data. This includes
what sources are available, how it will be secured and stored, how information will be gathered, and
what the final outcome or analysis may look like. This step also critically thinks about what limits there
are to data, storage, security, and collection, and assesses how these constraints will impact the data
mining process.

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