The Art of Data Mining For Turning Data Into Insights
What is Data Mining?
Data Mining is the practice of using a wide range of algorithms to search and analyze large stores of data to predict trends and patterns that can influence the outcomes. You can use this data to mitigate risks, increase revenue, capitalize on new opportunities and improve customer relationships by using a broad range of techniques.
Uses of Data Mining
Data Mining is the process by which companies turn raw data into information that is useful and can be put into action. Data Mining includes collecting and assembling data from common areas such as data warehouses, relational databases, transactional databases, multimedia databases, and spatial databases, etc. Data Mining is used across various fields such as business, research, marketing, sales, understanding customer behavior, education, identifying credit risk, production development and healthcare, banking fraud detection, and risk analysis.
Process of Data Mining
The Data Mining process can be explored in 5 steps.
- Step 1: Collection – First data is collected, organized, and filled into a data warehouse. The data is stored and managed either in the cloud or in-house servers.
- Step 2: Understanding – In this step, data scientists and business analysts examine the properties of the data and conduct an in-depth analysis from the context of a particular problem statement as defined by the company. This is addressed using querying, visualization, and reporting.
- Step 3: Preparation – Once the data sources of the available data are confirmed, the data is cleared, constructed, and formatted into the required form. In this process, additional data can also be explored at a greater depth, which is well informed by the insights and uncovered in the previous stage.
- Step 4: Modeling – In this stage, for the prepared dataset, modeling techniques are selected. A data model is just like a diagram that reflects and describes the relationships between different types of information that are stored in the database.
- Step 5: Evaluation – In the context of the business objectives, the model results are evaluated. In this phase, due to new patterns that are discovered in the model results or other factors, new business requirements may be raised.
Let's understand more about Data Mining with Examples
The predictive ability of data mining has revolutionized the design of business strategies. You can understand and well anticipate the future. Let us understand the following examples of data mining in the present industry.
Example 1: Marketing – Data mining aims to explore large databases and focuses to enhance market segmentation. By performing analysis regarding the relationships between parameters such as customer age, gender and tastes etc, to know about their behavior to provide personalized loyalty campaigns. In marketing, data mining helps to predict what interests the customers based on the searches and what should be included in a mailing list to achieve a greater response.
Example 2: Retail – In any supermarket or showroom, Data mining helps to detect which offers are most preferred by the customers.
Example 3: Banking – Data mining is used by banks to understand the market risks. It is generally applied in intelligent anti-fraud systems, credit ratings to analyze card transactions, customer financial data, and purchasing patterns. Data mining helps banks to learn more about the customers’ habits and also focus on managing regulatory compliance obligations.
Example 4: Healthcare – Data mining helps to identify and forecast risks, predict illness in certain segments of the population. It also helps to detect fraud and irregularities with much-enhanced knowledge.
Example 5: Social Media – Data mining in social media helps to find hidden information in social media. It is used to unleash the hidden trends and patterns from the social media platforms such as Twitter, Facebook, etc. This is achieved through statistical techniques and machine learning.
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