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What it is & why it matters

Data mining

Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.

Why is data mining important?


So why is data mining important? You’ve seen the staggering numbers – the volume of data produced is doubling every two years. Unstructured data alone makes up 90 percent of the digital universe. But more information does not necessarily mean more knowledge.
Data mining allows you to:
• Sift through all the chaotic and repetitive noise in your data.
• Understand what is relevant and then make good use of that information to assess likely outcomes.
• Accelerate the pace of making informed decisions.
Learn more about data mining techniques in Data Mining From A to Z, a paper that shows how organizations can use predictive analytics and data mining to reveal new insights from data.

Who's using it?

Data mining is at the heart of analytics efforts across a variety of industries and disciplines.

Telecom, Media & Technology

In an overloaded market where competition is tight, the answers are often within your consumer data. Telecom, media and technology companies can use analytic models to make sense of mountains of customers data, helping them predict customer behavior and offer highly targeted and relevant campaigns.

Insurance

With analytic know-how, insurance companies can solve complex problems concerning fraud, compliance, risk management and customer attrition. Companies have used data mining techniques to price products more effectively across business lines and find new ways to offer competitive products to their existing customer base.

Education

With unified, data-driven views of student progress, educators can predict student performance before they set foot in the classroom – and develop intervention strategies to keep them on course. Data mining helps educators access student data, predict achievement levels and pinpoint students or groups of students in need of extra attention.

Manufacturing

Aligning supply plans with demand forecasts is essential, as is early detection of problems, quality assurance and investment in brand equity. Manufacturers can predict wear of production assets and anticipate maintenance, which can maximize uptime and keep the production line on schedule.

Banking

Automated algorithms help banks understand their customer base as well as the billions of transactions at the heart of the financial system. Data mining helps financial services companies get a better view of market risks, detect fraud faster, manage regulatory compliance obligations and get optimal returns on their marketing investments.

Retail

Large customer databases hold hidden customer insight that can help you improve relationships, optimize marketing campaigns and forecast sales. Through more accurate data models, retail companies can offer more targeted campaigns – and find the offer that makes the biggest impact on the customer.

How It Works

Data mining, as a composite discipline, represents a variety of methods or techniques used in different analytic capabilities that address a gamut of organizational needs, ask different types of questions and use varying levels of human input or rules to arrive at a decision.

Descriptive Modeling: It uncovers shared similarities or groupings in historical data to determine reasons behind success or failure, such as categorizing customers by product preferences or sentiment. Sample techniques include:

 

Clustering
Grouping similar records together.
Anomaly detection
Identifying multidimensional outliers.
Association rule learning
Detecting relationships between records.
Principal component analysis
Detecting relationships between variables.
Affinity grouping
Grouping people with common interests or similar goals (e.g., people who buy X often buy Y and possibly Z).

 

Predictive Modeling: This modeling goes deeper to classify events in the future or estimate unknown outcomes – for example, using credit scoring to determine an individual's likelihood of repaying a loan. Predictive modeling also helps uncover insights for things like customer churn, campaign response or credit defaults. Sample techniques include:

 

Regression
A measure of the strength of the relationship between one dependent variable and a series of independent variables.
Neural networks
Computer programs that detect patterns, make predictions and learn.
Decision trees
Tree-shaped diagrams in which each branch represents a probable occurrence.
Support vector machines
Supervised learning models with associated learning algorithms.


Prescriptive Modeling: With the growth in unstructured data from the web, comment fields, books, email, PDFs, audio and other text sources, the adoption of text mining as a related discipline to data mining has also grown significantly. You need the ability to successfully parse, filter and transform unstructured data in order to include it in predictive models for improved prediction accuracy.

In the end, you should not look at data mining as a separate, standalone entity because pre-processing (data preparation, data exploration) and post-processing (model validation, scoring, model performance monitoring) are equally essential. Prescriptive modelling looks at internal and external variables and constraints to recommend one or more courses of action – for example, determining the best marketing offer to send to each customer. Sample techniques include:

Predictive analytics plus rules
Developing if/then rules from patterns and predicting outcomes.
Marketing optimization
Simulating the most advantageous media mix in real time for the highest possible ROI.