Data Mining

What is Data Mining?

 

Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden
patterns, finding predictive information that experts may miss because it lies outside their expectations.
Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides.

What Can Data Mining Do?

Although data mining is still in its infancy, companies in a wide range of industries - including retail, finance, heath care, manufacturing transportation, and aerospace - are already using data mining tools and techniques to take advantage of historical data. By using pattern recognition technologies and statistical and mathematical techniques to sift through warehoused information, data mining helps analysts recognize significant facts, relationships, trends, patterns, exceptions and anomalies that might otherwise go unnoticed.
For businesses, data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty. Specific uses of data mining include:

• Market segmentation - Identify the common characteristics of customers who buy the same products from your company.
• Customer churn - Predict which customers are likely to leave your company and go to a competitor.
• Fraud detection - Identify which transactions are most likely to be fraudulent.
• Direct marketing - Identify which prospects should be included in a mailing list to obtain the highest response rate.
• Interactive marketing - Predict what each individual accessing a Web site is most likely interested in seeing.
• Market basket analysis - Understand what products or services are commonly purchased together; e.g., beer and diapers.
• Trend analysis - Reveal the difference between a typical customer this month and last.

Data mining technology can generate new business opportunities by:

Automated prediction of trends and behaviors: Data mining automates the process of finding predictive information in a large database. Questions that traditionally required extensive hands-on analysis can now be directly answered from the data. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
Automated discovery of previously unknown patterns: Data mining tools sweep through databases and identify previously hidden patterns. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Using massively parallel computers, companies dig through volumes of data to discover patterns about their customers and products. For example, grocery chains have found that when men go to a supermarket to buy diapers, they sometimes walk out with a six-pack of beer as well. Using that information, it's possible to lay out a store so that these items are closer.
AT&T, A.C. Nielson, and American Express are among the growing ranks of companies implementing data mining techniques for sales and marketing. These systems are crunching through terabytes of point-of-sale data to aid analysts in understanding consumer behavior and promotional strategies. Why? To gain a competitive advantage and increase profitability!
Similarly, financial analysts are plowing through vast sets of financial records, data feeds, and other information sources in order to make investment decisions. Health-care organizations are examining medical records to understand trends of the past so they can reduce costs in the future.

The Evolution of Data Mining

Data mining is a natural development of the increased use of computerized databases to store data and provide answers to business analysts.

Evolutionary Step	Business Question	Enabling Technology
Data Collection (1960s)	"What was my total revenue in the last five years?"	computers, tapes, disks
Data Access (1980s)	"What were unit sales in New England last March?"	faster and cheaper computers with more storage, relational databases
Data Warehousing and Decision Support	"What were unit sales in New England last March? Drill down to Boston."	faster and cheaper computers with more storage, On-line analytical processing (OLAP), multidimensional databases, data warehouses
Data Mining	"What's likely to happen to Boston unit sales next month? Why?"	faster and cheaper computers with more storage, advanced computer algorithms

Traditional query and report tools have been used to describe and extract what is in a database. The user forms a hypothesis about a relationship and verifies it or discounts it with a series of queries against the data. For example, an analyst might hypothesize that people with low income and high debt are bad credit risks and query the database to verify or disprove this assumption. Data mining can be used to generate an hypothesis. For example, an analyst might use a neural net to discover a pattern that analysts did not think to try - for example, that people over 30 years old with low incomes and high debt but who own their own homes and have children are good credit risks.

How Data Mining Works

 

How is data mining able to tell you important things that you didn't know or what is going to happen next? That technique that is used to perform these feats is called modeling. Modeling is simply the act of building a model (a set of examples or a mathematical relationship) based on data from situations where the answer is known and then applying the model to other situations where the answers aren't known. Modeling techniques have been around for centuries, of course, but it is only recently that data storage and communication capabilities required to collect and store huge amounts of data, and the computational power to automate modeling techniques to work directly on the data, have been available.
As a simple example of building a model, consider the director of marketing for a telecommunications company. He would like to focus his marketing and sales efforts on segments of the population most likely to become big users of long distance services. He knows a lot about his customers, but it is impossible to discern the common characteristics of his best customers because there are so many variables. From his existing database of customers, which contains information such as age, sex, credit history, income, zip code, occupation, etc., he can use data mining tools, such as neural networks, to identify the characteristics of those customers who make lots of long distance calls. For instance, he might learn that his best customers are unmarried females between the age of 34 and 42 who make in excess of $60,000 per year. This, then, is his model for high value customers, and he would budget his marketing efforts to accordingly.

Data Mining Technologies

The analytical techniques used in data mining are often well-known mathematical algorithms and techniques. What is new is the application of those techniques to general business problems made possible by the increased availability of data and inexpensive storage and processing power. Also, the use of graphical interfaces has led to tools becoming available that business experts can easily use.
Some of the tools used for data mining are:
Artificial neural networks - Non-linear predictive models that learn through training and resemble biological neural networks in structure.
Decision trees - Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset.
Rule induction - The extraction of useful if-then rules from data based on statistical significance.
Genetic algorithms - Optimization techniques based on the concepts of genetic combination, mutation, and natural selection.
Nearest neighbor - A classification technique that classifies each record based on the records most similar to it in an historical database.

Real-World Examples

Details about who calls whom, how long they are on the phone, and whether a line is used for fax as well as voice can be invaluable in targeting sales of services and equipment to specific customers. But these tidbits are buried in masses of numbers in the database. By delving into its extensive customer-call database to manage its communications network, a regional telephone company identified new types of unmet customer needs. Using its data mining system, it discovered how to pinpoint prospects for additional services by measuring daily household usage for selected periods. For example, households that make many lengthy calls between 3 p.m. and 6 p.m. are likely to include teenagers who are prime candidates for their own phones and lines. When the company used target marketing that emphasized convenience and value for adults - "Is the phone always tied up?" - hidden demand surfaced. Extensive telephone use between 9 a.m. and 5 p.m. characterized by patterns related to voice, fax, and modem usage suggests a customer has business activity. Target marketing offering those customers "business communications capabilities for small budgets" resulted in sales of additional lines, functions, and equipment.
The ability to accurately gauge customer response to changes in business rules is a powerful competitive advantage. A bank searching for new ways to increase revenues from its credit card operations tested a nonintuitive possibility: Would credit card usage and interest earned increase significantly if the bank halved its minimum required payment? With hundreds of gigabytes of data representing two years of average credit card balances, payment amounts, payment timeliness, credit limit usage, and other key parameters, the bank used a powerful data mining system to model the impact of the proposed policy change on specific customer categories, such as customers consistently near or at their credit limits who make timely minimum or small payments. The bank discovered that cutting minimum payment requirements for small, targeted customer categories could increase average balances and extend indebtedness periods, generating more than $25 million in additional interest earned,
Merck-Medco Managed Care is a mail-order business which sells drugs to the country's largest health care providers: Blue Cross and Blue Shield state organizations, large HMOs, U.S. corporations, state governments, etc. Merck-Medco is mining its one terabyte data warehouse to uncover hidden links between illnesses and known drug treatments, and spot trends that help pinpoint which drugs are the most effective for what types of patients. The results are more effective treatments that are also less costly. Merck-Medco's data mining project has helped customers save an average of 10-15% on prescription costs.

The Future of Data Mining

In the short-term, the results of data mining will be in profitable, if mundane, business related areas. Micro-marketing campaigns will explore new niches. Advertising will target potential customers with new precision.
In the medium term, data mining may be as common and easy to use as e-mail. We may use these tools to find the best airfare to New York, root out a phone number of a long-lost classmate, or find the best prices on lawn mowers.
The long-term prospects are truly exciting. Imagine intelligent agents turned loose on medical research data or on sub-atomic particle data. Computers may reveal new treatments for diseases or new insights into the nature of the universe. There are potential dangers, though, as discussed below.