Data Mining: Turning Raw Data into Useful information
Data Mining

What is data mining

In short, this is a process used by many companies and organizations to effectively turn raw data into useful information. This is done by using software to look for patterns in large batches of data for businesses to learn more about their customers in order to develop targeted marketing strategies, increase sales and eliminates wasteful spending.

To do data mining properly, it depends on effective data collection, warehousing of data and computer processing.

How data mining works

Large blocks off information gleaned from exploring and analyzing large data sets will show meaningful trends and patterns. These can be used in a variety of ways including:

  • Search engines
  • Website recommendation programs
  • Database Marketing
  • Credit Risk management
  • Training and Support
  • Fraud Detection
  • Healthcare Bioinformatics
  • Sentiment Analysis
  • Qualitative Data Mining
  • Spam Filtering


The data mining process breaks down into five major steps:

1.     Organizations collect data and load these data sets into their warehouses.

2.     The data will be stored and managed either on-site or in the cloud.

3.     Business analysts, management teams and technology professionals now access the data and determine how they want to organize it.

4.     Application software begins to sort the data based on user results.

5.     The end user will now be able to present the data in an easy to share format such as a graph or table.


Using data warehousing and mining software

Data mining programs analyze all relationships and patterns in data from user requests. Let’s take for example, a company that can use data mining software to create classes of information. Imagine a restaurant that wants to use data mining to determine when it should offer certain specials. They would look at the data set (collection of data in a specific sort or filtered order) and from here, create classes based on when customers visit and what they order.

Other examples would have data miners find clusters of data information based on logical relationships or associations or sequential patterns to draw conclusions about trends in consumer behavior.

An important aspect of data mining is warehousing. This is when companies centralize their data into one database or program. When a business uses a data warehouse, they may section off segments of data for specific users to analyze and make us of. Other cases though, may have analysts start with the data they want and create a data warehouse based on those specs. Regardless of how the data is sectioned off or managed it is always organized and used to support managements decision-making process.

Ensuring data validity

One example to look at would be loyalty cards. These cards make it easy to track customer engagement regarding buying habits in order to decide what to put on sale or what items to keep at full price for a period of time. Data mining agencies must be careful though as this can be a cause for concern when analysts will only use selected information which is not representative of the overall sample group to prove a hypothesis. Critical to any data mining information discovery is to identify the goal, method of data inclusion and vetting of the final data set.


Data mining is the process where analyzing a large batch of information is done to discern trends and patterns. This will eventually be used by companies to determine, strategy, target markets, new business potential and more. In short, data mining can be used for everything from discovering what customers are interested in or want to buy, to fraud detection and spam filtering.