Chapter 1 Why

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Chapter 1Why What is Data Mining?

• Note Included in this Slide Set is both Chapter
  1 material and additional material from the
  instructor.

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Data Mining is a subset of Business Intelligence
(BI)
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Topics to Discuss in Session 1

• What is Data Mining (DM)?
• Who uses DM?
• Why DM?
• Where DM?
• When DM?
• How DM?
• Why study DM?

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Data Mining Definition Goal
What, Who

• Definition
• DM is the exploration and analysis of large
  quantities of data in order to discover
  meaningful patterns and rules.
• Goal
• To allow an enterprise to IMPROVE its ______
  through better understanding of its ______ .
• Potential for Competitive Advantage.

Synonyms include corporation, firm, non-profit
organization, government agency
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Foundations of Data Mining

• Data mining is the process of using raw data to
  infer important business relationships.
• Despite a consensus on the value of data mining,
  a great deal of confusion exists about what it
  is.
• Data Mining is a collection of powerful
  techniques intended for analyzing large amounts
  of data.
• There is no single data mining approach, but
  rather a set of techniques that can be used stand
  alone or in combination with each other.

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Data Mining Why now?
Why, Where, When

• So much data are being produced!
• Data are being warehoused
• Computing power is more affordable
• Competitive pressures are enormous
• Data Mining software is available

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Customer Relationship Management (CRM)
How
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Customer Relationship Management (CRM)
How
In order to form a learning relationship with its
customers, an enterprise (firm) must be able to

• Notice what its customers are doing
• Remember what it and its customers have done
  over time
• Learn from what it has remembered
• Act On what it has learned to make customers
  more profitable

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Based on Transaction Data
How
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Based on Transaction Data
How
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Identifying and Remembering Relationships is the
Key!
How
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Group Exercise 1

• Time Box 15 minutes
• Teams of 4 or less
• Discuss DM situations among yourselves and pick
  one to report to the class
• What to report (verbally 5 minute max)
• Describe the DM situation
• How does it help the enterprise?
• Presentationsanother 15 to 30 minutes

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Why Study Data Mining? Open discussion to
identify these
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Topics to Discuss in Session 2

• Data Mining History
• Data Warehouse
• Data Mart

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Data Mining History

• The approach has roots in practice dating back
  over 40 years.
• In the early 1960s, data mining was called
  statistical analysis, and the pioneers were
  statistical software companies such as SAS and
  SPSS.
• By the late 1980s, the traditional techniques had
  been augmented by new methods such as fuzzy
  logic, heuristics and neural networks.

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Definitions of a Data Warehouse
A subject-oriented, integrated, time-variant and
non-volatile collection of data in support of
management's decision making process
1.
- W.H. Inmon
A copy of transaction data, specifically
structured for query and analysis
2.
- Ralph Kimball
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Data Warehouse

• For organizational learning to take place, data
  from many sources must be gathered together and
  organized in a consistent and useful way hence,
  Data Warehousing (DW)
• DW allows an organization (enterprise) to
  remember what it has noticed about its data
• Data Mining techniques make use of the data in a
  DW

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Data Warehouse
Enterprise Database
Customers
Orders
Transactions
Vendors
Products
Etc

• Data Miners
• Farmers they know
• Explorers - unpredictable

Copied, organized summarized
(Prospectors)
Data Warehouse
Data Mining
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Data Warehouse

• A data warehouse is a copy of transaction data
  specifically structured for querying, analysis
  and reporting hence, data mining.
• Note that the data warehouse contains a copy of
  the transactions which are not updated or changed
  later by the transaction system.
• Also note that this data is specially structured,
  and may have been transformed when it was copied
  into the data warehouse.

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Data Mart

• A Data Mart is a smaller, more focused Data
  Warehouse a mini-warehouse.
• A Data Mart typically reflects the business rules
  of a specific business unit within an enterprise.

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Data Warehouse to Data Mart
Decision Support Information
Data Warehouse
Decision Support Information
Decision Support Information
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Data Warehouse Mart

• Set of Tables 2 or more dimensions
• Designed for Aggregation

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Group Exercise 2

• Time Box 15 minutes
• Teams of 4 or less
• Discuss Data Warehouse to Data Mart situations
  among yourselves and pick one to report to the
  class
• What to report (verbally 5 minute max)
• Describe the DW to Data Mart situation
• How does it help the enterprises business
  unit?
• Presentationsanother 15 to 30 minutes

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Topics to Discuss in Session 3

• Data Mining Flavors
• Data Mining Examples
• Data Mining Tasks
• Data Minings Biggest Challenge
• What does all of this mean?

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Data Mining Flavors

• Directed Attempts to explain or categorize some
  particular target field such as income or
  response.
• Undirected Attempts to find patterns or
  similarities among groups of records without the
  use of a particular target field or collection of
  predefined classes.

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Data Mining Examples in Enterprises
For Illustration Purposes Only

• US Government
• FBI track down criminals (Local Police also)
• Treasury Dept suspicious international funds
  transfer
• Phone companies
• Supermarkets Superstores (Vons, Albertsons,
  Wal-Mart, Costco)
• Mail-Order, On-Line Order (L.L. Bean, Victorias
  Secret, Lands End, Amazon!)
• Financial Institutions (BofA, Wells Fargo,
  Charles Schwab)
• Insurance Companies (USAA, Allstate, State Farm)
• Tons of others

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Data Mining Tasks

• Classification example Fr, So, Jr, Sr, ND
• Estimation example household income
• Prediction example predict credit card balance
  transfer average amount
• Affinity Grouping Example people who buy X,
  often buy Y also with probability Z
• Clustering similar to classification but no
  predefined classes
• Description and Profiling behavior begets an
  explanation such as Men tend to prefer
  BurgerKing women prefer Wendys.

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Data Minings Biggest Challenge

• The largest challenge a data miner may face is
  the sheer volume of data in the data warehouse.
• It is quite important, then, that summary data
  also be available to get the analysis started.
• A major problem is that this sheer volume may
  mask the important relationships the data miner
  is interested in.
• The ability to overcome the volume and be able to
  interpret the data is quite important.

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What Does All of This Mean?

• On a regular basis, farmers and explorers
  utilize their data warehouses to give guidance
  for and/or answer a limitless variety of
  questions.
• Nothing is free, however, and the costs may be
  heavy.
• The value of a data warehouse and subsequent data
  mining is a result of the new and changed
  business processes it enables competitive
  advantage also.
• There are limitations, though - A Data Warehouse
  cannot correct problems with its data, although
  it may help to more clearly identify them.

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End of Chapter 1