What is Data Mining?

In a nutshell, the process of data mining can be broken down into four basic steps:

1. Data is collected and loaded into a data warehouse.
2. Data is mapped and stored either on owned servers or in the cloud.
3. Data is reviewed to determine how it will be organized for analysis.
4. Data is sorted and presented in an easy-to-read format such as a table or graph.

There is also a cross-industry standard process for data mining, known as CRISP-DM. This provides an appropriate guide for beginning and working through the data mining process.

The CRISP-DM is a six-phase workflow:

1. Business understanding – Establish the project objectives and scope, then identify the questions or problems stakeholders want to solve for.
   
   
2. Data understanding – Identify the data collected that is relevant to the question or problem being solved for.
   
   
3. Data preparation – Prepare the final dataset and identify the dimensions and variables wanting to be explored within the data.
   
   
4. Modeling – Select the appropriate modeling technique. This may require moving back to phase 1 if the model requires expanded dimensions or variables or gathering data from different sources.
   
   
5. Evaluation – Test and measure the success of the chosen model at answering the questions identified in phase 1. This may require moving back to previous phases if data modeling is not meeting business goals.
   
   
6. Deployment – Once they are accurate and reliable, findings are then shared with stakeholders in a way that is easy to understand and put into place.

The concept of data mining originated in the 1990s and is a result of evolution in database and data warehouse technologies. Previously, NASA and similar organizations were the only ones able to analyze big data. Back then, doing so required supercomputers. But today, analyzing big data is the cornerstone of modern business and more affordable than ever.

Back in the ‘90s, data mining was a manual, tedious and time-consuming process. Fast forward several decades to today, and data mining technology has evolved. The increased processing power and speed of today’s computer systems allow industries to uncover correlations and patterns in even vast quantities of data. The information unlocked through data mining helps organizations make better decisions that can help improve their operational efficiency and customer relationships and, ultimately, increase their revenue.

Data mining is at the heart of data analytics and is leveraged across a wide range of industries and disciplines from telecommunications and technology, to insurance, banking and even retail.

Here’s how data mining is used in just a few industries:

1. Telecom and technology – Predicting user behavior and targeting relevant campaigns.
2. Insurance– Predicting user behavior and targeting relevant campaigns.
3. Banking– Identifying market risks and detecting fraud faster.
4. Retail– Optimizing marketing campaigns and forecasting sales projections.

Here are a few examples of how data mining is used in services you might be familiar with:

• Data mining allows companies to align marketing tactics with customer preferences by analyzing terabytes of raw customer data in real time.
• Commercial airlines use data mining to gain deeper customer insights and create personalized travel experiences that integrate search data, previous booking data, current flight operations, web visits, social media and airport interactions.
• Free grocery store loyalty card programs provide grocers with tracking on what users buy, when and at what price in order to analyze behaviors, provide customers with targeted coupons and manage inventory and sales pricing.

Data mining is utilized in information technology and computer sciences in a variety of ways, providing a wide array of careers you can pursue.

Here are a few data mining jobs you might consider:

• Software application developer – This position is responsible for developing and modifying source code for software applications.
  
  
• Software programmer and analyst – Programmer and analyst positions are responsible for developing and testing custom applications, creating software patches and performing routine maintenance and updates on systems.
  
  
• Software developer data analyst – Development and data analysts create new software from concept and perform ongoing data analysis once built.
  
  
• Data analyst – Data specific analysts use data to solve business problems.