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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, fact-based 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.

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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.

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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)

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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.

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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.