Idsc 3001 Articles Topics 10-14

Front 1

How are predictive analytics moving into the business world?

Back 1

used in crime, counterterrorism, expanding to health care, insurance, etc

Front 2

Why do some object to predictive analytics?

Back 2

could eventually lead to denying people jobs, insurance because of genes, conditions, etc.

Front 3

What are the main ingredients of predictive analytics?

Back 3

business data, mathematical algorithms, and forecasting models

Front 4

Prediction by IDC--what will the predictive analytics market grow to by 2008?

Back 4

$8 billion

Front 5

What does a DBMS provide for a database and the organization using it?

Back 5

1) Data independence: the application
programs are protected from changes in
the hardware, operating system, data
storage devices, and so on.
2) Data sharing: all the applications
use one copy of the database.
3) Security: only authorized individuals,
terminals, and programs can perform
specific functions.
4) Data integrity: hardware and software
defects will not make the database
inconsistent.
5) Ease of use: the view of data provided
to the programmers and other users
is clear, straightforward, and easy to
use; the DBMS is packaged in such a
way that the system programmers find it
easy to install and maintain.
6) Performance: response time and
throughput requirements are met.

Front 6

What services does a DBMS provide?

Back 6

1) User view of data: describing the
way data appear to the user of the
DBMS, a view that is usually quite different
from the way data are stored in the
computer (the DBMS maps from one to
the other).
2) Data language: allowing the user to
retrieve, update, insert, and delete data
fi-om the database.
3) Transaction management: providing
execution control over the database,
determining the level of concurrent access,
the recovery options, and specifying
the "atomicity" of database operations.

Front 7

Why should platform standardization be used in DBMS strategy?

Back 7

-minimize administration, infrastructure cost
-minimize complexity
-improve operational efficiency
-improve data security- fewer platforms leads to strong policies, control, authorization, etc.

Front 8

What is a recommended approach to DBMS standardization?

Back 8

-Choosing the right DBMS platform to meet your application requirements
-Using a selected DBMS platform for all new deployments.
-Consolidating existing databases on a standard DBMS platform.
-Making careful decisions when dealing with exceptions.

Front 9

What is data mining?

Back 9

the process of analyzing data from
different perspectives and summarizing it into useful information - information that can be used to increase
revenue, cuts costs, or both.

-the process of finding correlations or patterns among
dozens of fields in large relational databases

Front 10

What is information?

Back 10

Patterns and relationships found among data

Front 11

What types of relationships are sought by data mining software?

Back 11

-classes- ie restaurant when customers visit and what they order
-clusters- mkt segments, consumer affinities
-associations- ie beer diaper example
-sequential patterns- anticipate behaviors and trends

Front 12

5 major elements of data mining

Back 12

Extract, transform, and load transaction data onto the data warehouse system.
Store and manage the data in a multidimensional database system.
Provide data access to business analysts and information technology professionals.
Analyze the data by application software.
Present the data in a useful format, such as a graph or table

Front 13

Levels of analysis available in DM

Back 13

Artificial neural networks: Non-linear predictive models that learn through training and resemble
biological neural networks in structure.
Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation,
and natural selection in a design based on the concepts of natural evolution.
Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for
the classification of a dataset. Specific decision tree methods include Classification and Regression Trees
(CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree
techniques used for classification of a dataset. They provide a set of rules that you can apply to a new
(unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by
creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART
typically requires less data preparation than CHAID.
Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of
the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the
k-nearest neighbor technique.
Rule induction: The extraction of useful if-then rules from data based on statistical significance.
Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.

Front 14

Four drivers to determine value of projects to business

Back 14

1. Minimize cost
2. Increase revenue overall
3. Strategic for company to gain advantage/stick with competition
4. Legal/regulatory/security- sensitive data shouldn't be stored outside the system

Front 15

Expense reduction in projects, four parameters?

Back 15

1. Customer service expense
2. Customer acquisition and retention
3. Back office efficiency gains
4. Other expense

Front 16

Projects are constrained by what three factors?

Back 16

Time, cost and scope

Front 17

Project management lifecycle

Back 17

Initiating
Planning
Executing
Monitoring and controlling
Closing