INF3703 - Chapter 15 Databases For Business Intelligence

Database Principles 3rd Ed., Coronel, Morris, Crockett, Blewett
©2020 Cengage EMEA
Database Principles
Fundamentals of Design,
Implementation, and Management
Carlos Coronel, Steven Morris,
Keeley Crockett, Craig Blewett
CHAPTER 15
DATABASES FOR BUSINESS
INTELLIGENCE

©2020 Cengage EMEA
In this chapter, you will learn:
• How business intelligence provides a
comprehensive business decision support
framework
• About business intelligence architecture, its
evolution and reporting styles
• The Data Warehouse Life cycle
• How to prepare data for the data warehouse
using the Extraction, Transformation and
Loading Process.

©2020 Cengage EMEA
In this chapter, you will learn
(continued):
• How to develop star and snowflake schemas
• About data analytics, data mining, and
predictive analysis
• About the characteristics and capabilities of
online analytical processing (OLAP)
• How SQL analytic functions are used to
support data analytics
• About data visualisation and how it supports
business intelligence

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The Need for Data Analysis
• Managers must be able to track daily
transactions to evaluate how the business is
performing
• By tapping into operational database,
management can develop strategies to meet
organisational goals
• Data analysis can provide information about
short-term tactical evaluations and strategies

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Business Intelligence
• Business intelligence (BI) is a term that describes a
comprehensive, cohesive, and integrated set of tools and
processes used to capture, collect, integrate, store, and
analyse data with the purpose of generating and
presenting information to support business decision
making.
• Intelligence is based on learning and understanding the
facts about the business environment.

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Business Intelligence (continued)

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• Implementing BI in an organisation involves
capturing not only internal and external
business data, but also the metadata, or
knowledge about the data.
• In practice, BI is a complex proposition that
requires a deep understanding and alignment
of the business processes, business data, and
information needs of users at all levels in an
organisation

©2020 Cengage EMEA
BI provides a framework for:
1. Collecting and storing operational data
2. Aggregating the operational data into decision support data
3. Analysing decision support data to generate information
4. Presenting such information to the end user to support business
decisions
5. Making business decisions, which in turn generate more data that
are collected, stored, and so on (restarting the process)
6. Monitoring results to evaluate outcomes of the business
decisions, which again provides more data to be collected, stored,
and so on
7. Predicting future behaviours and outcomes with a high degree of
accuracy

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Business Intelligence Architecture

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(continued)

©2020 Cengage EMEA
(continued)
• BI tools focus on the strategic and tactical use of
information.
• Therefore, BI uses an arrangement of best management
practices to manage data as a corporate asset.
• Master data management (MDM) is a collection of
concepts, techniques, and processes for the proper
identification, definition, and management of data
elements within an organisation.
• MDM’s main goal is to provide a comprehensive and
consistent definition of all data within an organisation.
• MDM ensures that all company resources (people,
procedures, and IT systems) that work with data have
uniform and consistent views of the company’s data.

©2020 Cengage EMEA
(continued)
• Governance is a method or process of
government.
• BI provides a method for controlling and
monitoring business health and for consistent
decision making.
• Having such governance creates accountability
for business decisions.
• In the present age of business flux,
accountability is increasingly important.

©2020 Cengage EMEA
(continued)
• Key performance indicators (KPIs) are quantifiable numeric or
scale-based measurements that assess the company’s effectiveness
or success in reaching its strategic and operational goals.
• Some examples of KPIs are:
• General. Year-to-year measurements of profit by line of business,
same-store sales, product turnovers, product recalls, sales by
promotion, and sales by employee
• Finance. Earnings per share, profit margin, revenue per employee,
percentage of sales to account receivables, and assets to sales
• Human resources. Applicants to job openings, employee turnover,
and employee longevity
• Education. Graduation rates, number of incoming freshmen,
student retention rates, publication rates, and teaching evaluation
scores

©2020 Cengage EMEA
(continued)
• A modern BI system provides three distinctive reporting
styles:
– Advanced reporting. A BI system presents insightful information
about the organisation in a variety of presentation formats.
– Monitoring and alerting. After a decision has been made, the BI
system offers ways to monitor the decision’s outcome. The BI
system provides the end user with ways to define metrics and
other key performance indicators to evaluate different aspects
of an organisation.
– Advanced data analytics. A BI system provides tools to help the
end user discover relationships, patterns, and trends hidden
within the organisation’s data. These tools are used to create
two types of data analysis: explanatory and predictive.

©2020 Cengage EMEA
Business Intelligence Benefits
• Integrating architecture. Like any other IT project, BI has the potential of
becoming the integrating umbrella for a disparate mix of IT systems within
an organisation.
• Common user interface for data reporting and analysis. BI front ends can
provide up-to-the-minute consolidated information using a common
interface for all company users. IT departments no longer have to provide
multiple training options for diverse interfaces. End users benefit from
similar or common interfaces in different devices that use multiple clever
and insightful presentation formats.
• Common data repository fosters single version of company data. In the
past, multiple IT systems supported different aspects of an organisation’s
operations. Such systems collected and stored data in separate data
stores.
• Improved organisational performance. BI can provide competitive
advantages in many different areas, from customer support to
manufacturing processes.

Business Intelligence Evolution (1)

Business Intelligence Evolution (2)

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Business Intelligence Evolution

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Decision Support Systems
• Decision support is methodology (or series of
methodologies) designed to extract information from data
and to use such information as a basis for decision making
• Decision support system (DSS)
– Arrangement of computerised tools used to assist managerial
decision making within business
– Usually requires extensive data “massaging” to produce
information
– Used at all levels within organisation
– Often tailored to focus on specific business areas
– Provides ad hoc query tools to retrieve data and to display data in
different formats

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Decision Support Systems (continued)
• Composed of following four main components:
– Data store component
• Basically a DSS database
– Data extraction and data filtering component
• Used to extract and validate data taken from operational database and
external data sources
– End-user query tool
• Used to create queries that access database
– End-user presentation tool
• Used to organise and present data

©2020 Cengage EMEA
Operational Data vs. Decision Support
Data
• Operational Data
– Mostly stored in relational database
– Optimised to support transactions representing daily operations
• DSS Data
– Give tactical and strategic business meaning to operational data
– Differs from operational data in following three main areas:
• Timespan
• Granularity
• Dimensionality

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Operational Data vs Decision Support
Data
• DSS data differ from operational data in three
main areas:
– Time Span
– Granularity
– Dimensionality

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Data (cont.)

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Data (continued)

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DSS Database Requirements
• A specialised DBMS tailored to provide fast
answers to complex queries.
• Four main requirements:
– Database schema
– Data extraction and loading
– End-user analytical interface
– Database size

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(continued)
• Database schema
– Must support complex data representations
– Must contain aggregated and summarised data
– Queries must be able to extract multidimensional
time slices

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DSS Database Requirements (continued)

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(continued)
• Data extraction
– Should allow batch and scheduled data extraction
– Should support different data sources
• Flat files
• Hierarchical, network, and relational databases
• Multiple vendors
• Data filtering
– Must allow checking for inconsistent data

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(continued)
– Database size
• The DBMS must be capable of supporting very large
databases (VLDBs).
• To support a VLDB the DBMS might be required to use
advanced hardware, such as multiple disk arrays, and,
even more importantly, to support multiple-processor
technologies, such as a symmetric multiprocessor
(SMP) or a massively parallel processor (MPP).

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The Data Warehouse
• Integrated, subject-oriented, time-variant,
nonvolatile collection of data that provides
support for decision making
• Usually a read-only database optimised for
data analysis and query processing
• Requires time, money, and considerable
managerial effort to create

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The Data Warehouse (continued)

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Twelve Rules That Define a Data
Warehouse
1. Data warehouse and operational environments are separated
2. Data warehouse data are integrated
3. Data warehouse contains historical data over long time
horizon
4. Data warehouse data are snapshot data captured at given
point in time
5. Data warehouse data are subject oriented

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Twelve Rules That Define a Data
Warehouse (continued)
6. Data warehouse data are mainly read-only with periodic
batch updates from operational data
6. No online updates allowed
7. Data warehouse development life cycle differs from classical
systems development
8. Data warehouse contains data with several levels of detail:
current detail data, old detail data, lightly summarised data,
and highly summarised data
9. Data warehouse environment is characterised by read-only
transactions to very large data sets

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Twelve Rules that Define a Data
Warehouse (continued)
10. Data warehouse environment has system that traces data
sources, transformations, and storage
11. Data warehouse’s metadata are critical component of this
environment
12. Data warehouse contains chargeback mechanism for
resource usage that enforces optimal use of data by end
users

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Data marts
• Data mart
– Small, single-subject data warehouse subset
– Each is more manageable data set than data
warehouse
– Provides decision support to small group of
people
– Typically lower cost and lower implementation
time than data warehouse

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Designing and Implementing a
Data warehouse
• The Data Warehouse as an Active Decision
Support Framework
• A Company-Wide Effort That Requires User
Involvement
– Building the perfect data warehouse is not just a
matter of knowing how to create a star schema; it
requires managerial skills to deal with conflict
resolution, mediation and arbitration.

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Designing and Implementing a Data
warehouse (continued)
• The DW designer must:
– Involve end users in the process.
– Secure end users’ commitment from the
beginning.
– Create continuous end-user feedback.
– Manage end-user expectations.
– Establish procedures for conflict resolution

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• The data warehouse designer must satisfy:
• Data integration and loading criteria.
• Data analysis capabilities with acceptable query
performance.
• End-user data analysis needs.
• The foremost technical concern in implementing
a data warehouse is to provide end-user decision
support with advanced data analysis
capabilities—at the right moment, in the right
format, with the right data, and at the right cost.

©2020 Cengage EMEA
• Apply Database Design Procedures
• Each business process that is to be modelled within the
data warehouse must be described in detail in order too:
• Identify business measures. For example a sales business
measure may be the number of a particular product that
has been sold in a week.
• Identify the level of detail or granularity of the data.
– The general rule of thumb is design for one grain finer than what the
users require.
• Check all data sources to ensure that the level of data
required can actually be obtained from the existing
source systems.

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The Extraction, Transformation,
Loading Process
• The Extraction, Transformation, Loading process
(ETL) is critical to a successful data warehouse.
• It must ensure that the data that is loaded into
warehouse is high-quality, accurate, relevant,
useful, and accessible.
• The is the most time consuming phase in building
a warehouse as routines must be developed to
select the required fields from often many
sources of data.

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Loading Process (continued)
• Types of data that may be extracted include:
– Operational data. The main source of data into the warehouse.
This data can directly come from any DBMS or application
within the organisation.
– Historical Archived data. This type of data is useful to perform
predictive analytics Unique data extraction and transformation
routines are therefore required to load the data in the
warehouse during the first time load.
– Internal data. Data within the organisation such as budgets or
sales forecasts which may exist in spread sheets.
– External data. Important for comparing the business
performance to enable an organisation to be competitive.
Sources of external data include real time data feeds,
newspapers and reports (from the internet) and marketing data
that has been purchased.

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• Some common source data anomalies include:
– Name and address inconsistencies
– Multiple coding problems
– Different Country standards
– Missing Values
– Referential integrity

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• The final stage of the ETL process is known as Loading
where the data is moved into the data warehouse.
• Loading data can be a very can be time-consuming
process and is usually done in two stages.
– The first stage, known as the first time load takes place
only once and is used to initially load historical data into
the data warehouse the first time load is very time and
resource intensive.
– Once the data warehouse is live, it will need to be updated
or refreshed at regular intervals. A Load window needs to
be defined.

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Star Schemas
• Data modeling technique used to map multidimensional
decision support data into relational database
• Creates near equivalent of multidimensional database schema
from existing relational database
• Yield an easily implemented model for multidimensional data
analysis, while still preserving relational structures on which
operational database is built
• Has four components: facts, dimensions, attributes, and
attribute hierarchies

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Facts
• Numeric measurements (values) that represent specific
business aspect or activity
– Normally stored in fact table that is center of star schema
• Fact table contains facts that are linked through their
dimensions
• Metrics are facts computed or derived at run time

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Dimensions

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Attributes
• Used to search, filter, or classify facts
• Dimensions provide descriptive characteristics about the
facts through their attributes

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Attributes (continued)

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Attribute Hierarchies
• Provides top-down data organisation
• Provides capability to perform drill-down and roll-up
searches in a data warehouse

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Attribute Hierarchies (continued)

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Star Schema Representation
• Each dimension record is related to thousands
of fact records
• Facilitates data retrieval functions

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Star Schema Representation (continued)

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Star Schema Performance-Improving
Techniques
• Four techniques to optimise data warehouse
design are:
– Normalising dimensional tables
– Maintaining multiple fact tables to represent
different aggregation levels
– Denormalising fact tables
– Partitioning and replicating tables
• Achieve semantic simplicity and facilitate end-
user navigation through the dimensions

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Techniques (continued)

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• Denormalising fact tables improves data
access performance and save data storage
space
• Partitioning splits table into subsets of rows or
columns
• Replication makes copy of table and places it
in different location

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Data Analytics
• Data analytics is used at all levels within the BI
framework, including queries and reporting,
monitoring and alerting, and data
visualisation.
– Explanatory analytics focuses on discovering and
explaining data characteristics and relationships
based on existing data.
– Predictive analytics focuses on predicting future
data outcomes with a high degree of accuracy.

©2020 Cengage EMEA
Data Mining
• Data mining tools do the following:
– Analyse data
– Uncover problems or opportunities hidden in data
relationships
– Form computer models based on their findings
– Use models to predict business behaviour
• Require minimal end-user intervention

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Data Mining (continued)

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Data Mining Phases
1. Data preparation
2. Data analysis and classification
3. Knowledge acquisition
4. Prognosis.

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Predictive Analytics
• Data mining focuses on answering the “how” and
“what” of past data, while predictive analytics focuses
on creating actionable models to predict future
behaviours and events.
• Predictive analytics employs mathematical and
statistical algorithms, neural networks, artificial
intelligence, and other advanced modelling tools to
create actionable predictive models based on available
data.
• The algorithms used to build the predictive model are
specific to certain types of problems and work with
certain types of data.

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Online Analytical Processing
• Advanced data analysis environment that supports
decision making, business modeling, and operations
research
• OLAP systems share four main characteristics:
– Use multidimensional data analysis techniques
– Provide advanced database support
– Provide easy-to-use end-user interfaces
– Support client/server architecture

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Multidimensional Data Analysis
Techniques
• Data are processed and viewed as part of a
multidimensional structure
• Particularly attractive to business decision
makers

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• Augmented by following functions:
– Advanced data presentation functions
– Advanced data aggregation, consolidation and
classification functions
– Advanced computational functions
– Advanced data modeling functions

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Advanced Database Support
• Advanced data access features include:
– Access to many different kinds of DBMSs, flat files, and internal
and external data sources
– Access to aggregated data warehouse data as well as to detail
data found in operational databases
– Advanced data navigation
– Rapid and consistent query response times
– Ability to map end-user requests to appropriate data source and
then to proper data access language (usually SQL)
– Support for very large databases

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Easy-to-Use End-User Interface
• Many of interface features are “borrowed”
from previous generations of data analysis
tools that are already familiar to end users
– Makes OLAP easily accepted and readily used

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OLAP Architecture (continued)
• Designed to use both operational and data
warehouse data
• Defined as an “advanced data analysis
environment that supports decision making,
business modeling, and an operation’s
research activities”
• In most implementations, data warehouse and
OLAP are interrelated and complementary
environments

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Relational OLAP
• Provides OLAP functionality by using relational databases
and familiar relational query tools to store and analyse
multidimensional data
• Adds following extensions to traditional RDBMS:
– Multidimensional data schema support within RDBMS
– Data access language and query performance optimised for

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Multidimensional OLAP
• Extends OLAP functionality to
multidimensional database management
systems (MDBMSs)
– MDBMS end users visualise stored data as a 3D
cube-a data cube
– Data cubes can grow to n number of dimensions,
becoming hypercubes
– To speed access, data cubes are held in memory in
a cube cache

Multidimensional OLAP (continued)

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Materialised Views
• A dynamic table that contains not only the
SQL query command to generate rows, but
also stores actual rows
• Created the first time query is run and
summary rows are stored in table
• Automatically update when base tables are
updated

Materialised Views (continued)

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Data Visualisation
• Data visualisation is the process of abstracting
data to provide a visual data representation
that enhances the user’s ability to
comprehend the meaning of the data.
• The goal of data visualisation is to allow the
user quickly and efficiently to see the data’s
big picture by identifying trends, patterns and
relationships

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Data Visualisation (continued)
• Data visualisation encodes the data into visually
rich formats (mostly graphical) that provide
• at-a-glance insight into overall trends, patterns
and possible relationships.
• Data visualisation techniques range from simple
to very complex including:
– pie charts, line graphs, bar charts, bubble charts,
bubble maps, donut charts, scatter plots, Gantt charts,
heat maps, histograms, time series plots, steps charts,
waterfall charts, and many more.

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The Science of Data Visualisation
• The science of data visualisation relates to
how our brains process visual data.

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• Data visualisation can be studied as a group of
visual communication techniques used to
explore and discover data insights by applying:
– Pattern recognition: Visually identifying trends,
distribution and relationships
– Spatial awareness: Use of size and orientation to
compare and relate data
– Aesthetics: Use of shapes and colours to highlight
and contrast data composition and relationships.

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Understanding the Data
• In general, there are two types of data:
• Qualitative: Describes qualities of the data. This type of
data can be subdivided into two subtypes:
– Nominal: This is data that can be counted but not ordered or
aggregated. Examples: Gender (male or female);
– Ordinal: This is data that can be counted and ordered but not
aggregated. Examples: Rate your teacher (excellent, good, fair,
poor)
• Quantitative: Describes numeric facts or measures of the
data. This type of data can be counted, ordered and
aggregated.
– ‘interval and ratio’ data.
– Examples of include age, GPA, number of accidents, etc.

©2020 Cengage EMEA
Summary
• Business intelligence (BI) is a term for a comprehensive,
cohesive, and integrated set of applications used to capture,
collect, integrate, store, and analyse data with the purpose of
generating and presenting information to support business
decision making.
• Decision support is methodology designed to extract
information from data and to use such information as basis for
decision making
• Operational data are not best suited for decision support
• Requirements for a DSS DBMS are divided into four main
categories: database schema, data extraction and loading, end-
user analytical interface, and database size requirements

©2020 Cengage EMEA
Summary (continued)
• Data warehouse is integrated, subject-
oriented, time-variant, nonvolatile database
that provides support for decision making
• OLAP is advanced data analysis environment
that supports decision making, business
modeling, and operations research
• ROLAP provides OLAP functionality by using
relational databases and familiar relational
query tools to store and analyse

©2020 Cengage EMEA
Summary (continued)
• Star schema is data-modeling technique used
to map multidimensional decision support
data into relational database
• Four techniques generally used to optimise
data warehouse design: normalising
dimensional tables, maintaining multiple fact
tables representing different aggregation
levels, denormalising fact tables, and
partitioning and replicating tables

©2020 Cengage EMEA
Summary (continued)
• Data analytics is a subset of BI functionality that
provides advanced data analysis tools to extract
knowledge from business data.
– Data analytics can be divided into explanatory and
predictive analytics. Explanatory analytics focuses on
discovering and explaining data characteristics and
relationships. Predictive analytics focuses on creating
models to predict future outcomes or events based on the
existing data.
• Data mining automates the analysis of operational data
with the intention of finding previously unknown data
characteristics, relationships, dependencies and/or
trends.

©2020 Cengage EMEA
Summary (continued)
• The data mining process has four phases: data
preparation, data analysis and classification,
knowledge acquisition and prognosis.
• SQL has been enhanced with extensions that
support OLAP type processing and data
generation.
• Data visualisation provides visual representations
of data that enhance the user’s ability to
comprehend the meaning of the data.

INF3703 - Chapter 15 Databases For Business Intelligence

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