Data Mining is at the heart of analytics efforts across a variety of industries and disciplines.
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.
It shows how organizations can use predictive analytics and data mining to reveal new insights from data
In an overloaded market where competition is tight, the answers are often within your consumer data. Multimedia and telecommunications 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.
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.
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.
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.
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.
Large customer databases hold hidden insights that can help you improve customer 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.
|Industries / Fields where you applied Analytics / Data Mining|
|Customer Relation Management/ consumer analytics|
|Health care/ HR|
|Direct Marketing/ Fundraising|
|Telecom / Cable|
|Travel / Hospitality|
|Web usage mining|
|Search / Web content mining|
|Investment / Stocks|
|Entertainment/ Music/ TV/Movies|
|Security / Anti-terrorism|
|Social Policy/Survey analysis|
|Junk email / Anti-spam|
Descriptive Modeling :
|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).|
|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.|
|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.|
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