By DeVry University
November 29, 2023
4 min read
November 29, 2023
4 min read
Data mining is the process of analyzing large amounts of data in order to identify patterns, anomalies and correlations. People who work in the data mining field use this type of data analysis to help predict the outcome of business decisions such as moves to increase revenue or reduce risk.
As businesses rely more and more on digital processes, they accumulate a wealth of data. Companies today can track everything from customer contacts and sales transactions to internal processes. Gleaning insights from all that “big data” is the role of a data mining professional.
As a branch of data science, data mining represents an intersection of statistics (the study of data relationships), artificial intelligence (the theory of human intelligence displayed by machines) and machine learning (the development of algorithms that learn and predict behavior).
Data is collected and loaded into a data warehouse.
Data is mapped and stored either on owned servers or in the cloud.
Data is reviewed to determine how it will be organized for analysis.
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:
Business understanding
Establish the project objectives and scope, then identify the questions or problems stakeholders want to solve for.
Data understanding
Identify the data collected that is relevant to the question or problem being solved for.
Data preparation
Prepare the final dataset and identify the dimensions and variables wanting to be explored within the data.
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.
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.
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.
The amount of data in the world has grown at exponential rates over the last two decades, accumulating to an amount that is beyond comprehension. In addition to the new data being generated, new IoT and wearable devices have become and will continue to be non-stop data-generating machines. It’s estimated that there are expected to be 30.9 billion connected units by 2025. It’s for this reason, amongst many others, that it won’t be long before data mining is a gold standard for any needed business or performance analysis.
Telecom and technology
Predicting user behavior and targeting relevant campaigns.
Insurance
Predicting user behavior and targeting relevant campaigns.
Banking
Identifying market risks and detecting fraud faster.
Retail
Optimizing marketing campaigns and forecasting sales projections.
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.
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.
Take the first step in working toward a career in data mining with our Undergraduate Certificate in Data Mining and Analytics program. Contact us today to get started.
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