Download Databases and Data Warehouses: Building Business Intelligence

Week 4, Lecture 11
Chapter 3- Databases and Data Warehouses: Building Business
Intelligence

In the first two chapters, we briefly touched on a couple of important terms–business
intelligence and analytics. We define them as follows:
1. Business intelligence (BI)–collective information–about customers, competitors,
business partners–that gives you the ability to make effective, important, and often
strategic business decisions.
2. Analytics–the science of fact-based decision making. Analytics is a growing field
of study, research, and career opportunities that focuses on integrated use of
technology tools and statistical techniques to create real-time, high-quality, factbased business intelligence in support of decision making.
3. So, BI a resource/component of the overall framework or field of analytics.

To create BI you need data and information and is extracts the meaning of information
 To obtain BI you need first to gather and organize you data and
information.
 Then, you need to have the right IT tools to define and analyse
relationships within the collected information.
 People (knowledge workers) use IT tools (databases, database
management systems (DBMSs), data warehouse and data-mining tools) to
create BI from information. This is what analytics is all about. The
technology by itself won’t do it for you, but they help you build and use
BI.

This chapter is about the technologies of databases, DBMSs, data warehouses and data
mining tools, which all have a place in an organisation as tools for working with
information.

When working with these IT tools, you’ll be performing the two types of information
processing:
1. Online transaction processing (OLTP) is the:
1. Gathering of input information;
2. Processing that information; and
3. Updating existing information to reflect the gathered and processed
information.
o Databases and DBMS are the IT tools that directly support OLTP.
 Databases that support OLTP are referred to as operational databases.
These databases hold information that forms the basis for BI.
Page 1 of 5
o You can query operational databases to gather basic BI (e.g. how many products
individually sold over JD500 last month? How much money was spent on radio
advertising last month? Who are delinquent in paying their bill?). While the
results of these queries may be helpful, you really need to combine products and
adverting information (with several other types of information including
customer demographics) to perform online analytical processing.
2. Online analytical processing (OLAP) is the:
 Manipulation of information to support decision making.

Through the creation and use of data warehouses, a company can know all the products a
customer has purchases, determine cross-selling opportunities, and what marketing
campaigns a given customer is likely to respond to.

Data warehouse is:
o A special form of database.
o Contains information gathered from several operational databases
o For the purpose of supporting decision-making tasks.

When you build a data warehouse and use data-mining tools to manipulate the data
warehouse’s information, you only goal is to create BI for the purpose of analytics.

Data warehouses support only OLAP; they do not support OLTP.
o As in the figure, you can perform in-depth queries to gather business intelligence
from a data WH than you can with a single database.
o For example, “what new advertising strategy needed to reach customers who can
afford a high-priced product?” is a query that requires information from multiple
databases.
Page 2 of 5
The Relational Database Model

For organizing and storing basic and transaction-oriented information (that is eventually
used in analytics to create business intelligence), businesses today use databases.

There are four primary models for creating a database. The object-oriented database
model is the newest. For more about this model see the Extended Learning Module G.
But, we will focus from the four models on the most popular database model: the
relational database model.

A database is a collection of information that you organize and access according to the
logical structure of that information. Relational database is a database that uses a series
of logically related two-dimensional tables of files in the relational model to store
information. Relational database is actually composed of two parts (See Figure 3.2):
1. The information itself.
2. The logical structure of that information.
Collections of Information [Page 68]
 Using the relational database model, you organize and access information according to its
logical structure (which table [ORDER or CUSTOMER]) not its physical position.
o You don’t really need to know where a specific row in the ORDER table appears.
You only need to know a piece of information to retrieve the row (e.g. order’s
number e.g. 100001)
Page 3 of 5
Created with Logical Structures [Page 68]
 In the relation database model the data dictionary contains the logical structure of the
information in a database (e.g. Order Number is a number of 6-digit field).
 When you create a database, you first create the data dictionary.
 RDBs are quite different from other ways of organizing information (e.g. Excel) where
you need sometimes to take into consideration the row and column numbers of
information.
 In RDBs you only need to know the field name of the column and its logical row (not the
physical row).
With Logical Ties within the Information [Page 70]

In relational databases you need to create ties or relationships in the information that
show how the files relate to each other.

Before creating the relationships, you must specify the primary key of each table.

Primary key is a field (or group of fields in some cases) that uniquely describes each
record.

Primary key cannot be blank (which is again different from working with
spreadsheets)

Foreign key is a primary key of one file that appears in another table.

Foreign keys are so important. Without them you cannot create logical ties among the
tables. Relationships allow us to create BI because they enable us to create logical ties
within many types of information. See Figure 3.3.
Page 4 of 5
With Built-In Integrity Constraints [Page 71]

By defining logical structure of information in a relational database, you are also
developing integrity constraints.

Integrity constraints are rules that help ensure the quality of information.
o Example: by saying that customer_no is the primary key of table Customer and a
foreign key in the Order table you are actually saying:
 No two customers can have the same customer number.

A customer_no entered in Order table must have a matching customer_no
in Customer

You cannot create new order for a customer that doesn’t exist.
Page 5 of 5