1

Chapter 5Business Intelligence: Data

Warehousing, Data Acquisition, Data Mining, Business Analytics,

and Visualization

2

How to leverage all the data that organizations collect and

store?

Answer

• Data warehousing

• Data acquisition (access)

• Data mining

• Online analytical processing (OLAP) or Business Analytics

• Data Visualization

3

Data, Information, Knowledge

• Data– Items that are the most elementary descriptions

of things, events, activities, and transactions– May be internal or external

• Information– Organized data that convey meaning and value

• Knowledge– Processed data or information that conveys

understanding, experience, accumulated learning and expertise applicable to a problem or activity

4

What kinds of Data Issues organizations deal with?

• Multiple sources• Wide time frame• Data reduction (aggregation)• Various levels of detail• Various amounts of data• Varying degrees of accuracy• Provide random access to database• Security and private databases• End user interface (i.e., ability to interface two or

more databases at a time)

5

6

Preparing data for Warehousing

• Cleanse data– When populating warehouse– Data quality action plan– Best practices for data quality– Measure results

• Data integrity issues– Uniformity– Version– Completeness check– Conformity check– Genealogy or drill-down

7

Preparing data for Warehousing

• Data Integration• Access needed to multiple sources

– Often enterprise-wide – Disparate and heterogeneous databases– XML becoming language standard

• Web (external data source)– Intelligent agents– Document management systems– Content management systems

• External Commercial databases– Sell access to specialized databases

8

Database Models

• Hierarchical– Top down, like inverted tree– Fields have only one “parent”, each “parent” can have multiple

“children”– Fast

• Network – Relationships created through linked lists, using pointers– “Children” can have multiple “parents”– Greater flexibility, substantial overhead

• Relational– Flat, two-dimensional tables with multiple access queries– Examines relations between multiple tables– Flexible, quick, and extendable with data independence

• Object oriented– Data analyzed at conceptual level– Inheritance, abstraction, encapsulation

9

Database Models

• Multimedia Based– Multiple data formats

• JPEG, GIF, bitmap, PNG, sound, video, virtual reality

– Requires specific hardware for full feature availability

• Document Based– Document storage and management

• Intelligent– Intelligent agents and ANN

• Inference engines

10

What is Data Warehousing?

• Is the process of taking internal and/or external data, cleansing it and storing it in a data warehouse where it can be accessed by various decision makers in the decision support process.

• A data mart is a part of a data warehouse containing a subject area data.

• Data warehousing solves the data acquisition or access problem.

• The end users perform ad hoc query, reporting, analysis and visualization on the data warehouse or on one or more data marts.

11

12

Business Intelligence and Analytics

• Business intelligence– Acquisition of data and information for

use in decision-making activities

• Business analytics– Models and solution methods

• Data mining– Applying models and methods to data to

identify patterns and trends

13

What is Multidimensionality?

• It is the ability to present in one screen or table several dimensions, e.g., Sales by region, by city, by product, by time period, by salesperson (5 dimensions), and it can be easily changed for different presentations of dimensions.

• Data organized according to business standards, not analysts – for slicing and dicing them to gain new insights.

14

What is OLAP?

• A database-oriented DSS which uses data warehouse and a set of tools usually with multidimensional capabilities to aid in reporting, querying and data analysis.

• Activities performed by end users in OLAP systems– Specific, open-ended query generation

• SQL– Requesting Ad hoc reports– Conducting Statistical and other (e.g. data mining) analyses– Building DSS applications

• Modeling and visualization capabilities• OLAP tools fall into four product groups:

– Multidimensional spreadsheets– Multidimensional query & report writing tools for standard

RDMS (e.g., Business Objects)– Fully multidimensional DBMS– Visual information access systems

15

What is Data Mining?

• A term used to describe knowledge discovery in databases

• Requires accessibility from a user’s workstation to data that may reside in different locations (e.g., corporate warehouse, servers)

• Statistical, mathematical, artificial intelligence, and machine-learning techniques are used to identify new patterns in data for knowledge discovery

• Automatic, uses intelligent search, and fast

16

Data Mining (contd.)

• Data mining application classes of problems– Classification– Clustering– Association– Sequencing– Regression– Forecasting– Others

• Data mining application areas– Marketing– Banking– Insurance– Health care– Law enforcement– Government and defense– Others

17

What is an Intelligent Database?

• Provides the user with an easy access to data

• Allows to do complex operations without much user input – the user specifies what she is looking for, and an intelligent agent can execute the request

• Business Objects is an example of an intelligent database

• “Objects” are created by IS professionals to represent elements in databases such as customers, products or locations. Users click on the objects and business objects automatically generates and executes the appropriate SQL queries

18

Data Visualization

• Technologies supporting visualization and interpretation– Digital images (maps), GIS, GUI, tables,

multidimensionality, graphs, VR (virtual reality), 3D, animation

– Identify relationships and trends

• Data manipulation allows real time look at performance data

19

Analytic systems

• Real-time queries and analysis• Real-time decision-making• Real-time data warehouses updated

daily or more frequently– Updates may be made while queries are

active– Not all data updated continuously

• Deployment of business analytic applications

20

GIS

• Computerized system for managing and manipulating data with digitized maps– Geographically oriented– Geographic spreadsheet for models– Software allows web access to maps– Used for modeling and simulations

21

22

Web Analytics/Intelligence

• Web analytics– Application of business analytics to Web

sites

• Web intelligence– Application of business intelligence

techniques to Web sites