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A guide to data mining, the process of turning raw data into business insights


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Summary List Placement

Data mining is a process that turns raw data into actionable insights for businesses and institutions — it is a branch of analytics that finds patterns and correlations within large sets of data which can be used to predict outcomes and make decisions. 

The field of data mining isn't especially new. The term dates back to the 1980s and represents a more automated version of what has traditionally been a process of manually sifting through data for trends and patterns that has a history of more than 200 years. In modern times, data mining combines statistical methods with artificial intelligence and machine learning to rapidly assess huge volumes of data. 

The process of data mining

Data mining is sometimes said to be a misnomer because you are not actually mining for data, you are mining through data in search of patterns, trends, and anomalies that can help inform business decisions. Moreover, data mining isn't akin to a fishing expedition in which analysts review data without an overall plan; data mining is most successful when it's used with rigidly defined goals. 

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is one of the leading approaches to data mining. The process can be broken down into six steps.

  1. Business understanding: This is the phase in which the primary business objective is defined, along with project parameters and criteria for success. 
  2. Data understanding: Analysts determine what data is needed to solve the problem identified in the business understanding. 
  3. Data preparation: Frequently, the data needs to be prepared — it needs to be formatted and sanitized, fixing problems like removing corrupt data, irrelevant data, and duplicates.
  4. Modeling: Algorithms are developed to identify patterns in the data. 
  5. Evaluation: In this phase, analysts review the results to assess if it's addressing the objectives identified in the business understanding. The flow might need to be repeated iteratively, with the algorithm and data adjusted until the results conform to expectations. 
  6. Deployment: In the final phase, the results are provided to business leaders or decision makers.  

Data mining functions and concepts

There are a lot of industry-specific terms used in relation to data mining. Here are the key concepts and functions that play a role in this process.

  • Artificial intelligence (AI): AI is a computer system that can mimic some aspects of human intelligence such as planning, learning, reasoning, problem solving, and sometimes, social intelligence or creativity.

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  • Association rule learning: This is an analytical technique in which a system searches for relationships among variables in data. An example of this is known as market basket analysis, which Amazon uses to figure out what products are typically purchased together to make recommendations. 
  • Clustering: This technique partitions data into meaningful groups or classes. It helps people and systems understand how the data should naturally or organically be structured. 
  • Data analytics: The overall process of evaluating data into business intelligence.
  • Data cleansing: Used in the data preparation phase, this is when raw data is put in a format suitable for analysis by eliminating data that is incorrect, incomplete, or irrelevant.
  • Machine learning: This is a kind of artificial intelligence that enables computer systems to solve problems without being given an explicit algorithm. Machine learning systems can be trained or can train themselves based on examples, and the exact algorithm the software develops to solve the problem is typically unknown. 
  • Regression: A common technique used to make predictions based on data. 

Uses for data mining

Data mining has a wealth of applications. It's commonly used to acquire customers, increase revenue, improve cross-selling and upselling, increase customer loyalty, detect fraud, and improving operational performance and efficiency. Here are some industries where data mining is routinely used.

  • Banking: The banking industry relies on data mining to detect fraud, assess market and investment trends, and manage regulatory and compliance issues.
  • Education: Educators use data mining to make predictions about student performance and develop strategies for intervening when students don't achieve the desired level.
  • Manufacturing: Data mining plays an important role in detecting problems and ensuring quality on the operations floor as well as anticipating the need for equipment maintenance and forecasting customer demand. 
  • Retail: This business sector is highly invested in data mining to uncover customer insights that help businesses improve sales, better target marketing campaigns, and forecast future sales trends. 

In fact, we're surrounded by real-world applications for data mining.

Amazon, for example, has an enormous amount of data about its users and what they buy, and the retailer mines that to power its recommendation engine, which provides highly targeted purchase suggestions whenever you are on the site.

GettyImages 865878278Similarly, Groupon processes its enormous volume of data to continuously realign its marketing activities with customer preferences, detecting and acting on customer trends in real time. 

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