1. CISB594 – Business Intelligence
Business Analytics and Data Visualization
Part I

2. Reference
Materials used in this presentation are extracted mainly from the following texts, unless stated otherwise.

3. Objectives At the end of this lecture, you should be able to:
Describe business analytics (BA) and its importance to organizations
List and briefly describe the major BA methods and tools
Describe how online analytical processing (OLAP), data visualization, and multidimensionality can improve decision making
Describe advanced analysis methods
CISB594 – Business Intelligence

4. Introduction
Many organizations have amassed vast amounts of data that employers can use to unlock valuable relationship to enable organization to compete and perform successfully
Using analytical tools, organizations enable decision analysis through access to all relevant data and information

5. The Business Analytics (BA) : An Overview
The Essentials of BA
Analytics : The science of analysis.
Business analytics (BA)
Provides the analysis procedures to BI, tracking data and analyzing them for competitive advantage.
Broad category of applications and techniques for gathering, storing and analyzing to help users make better business and strategic decisions
Allows for automating the thinking for decision making

6. The Business Analytics (BA) : An Overview
Many tools can be used – the results will be presented in a form of reports, predictions, alerts or graphical presentations
More advanced applications of BA includes financial modeling, budgeting, resource allocation and competitive intelligence

7. The Business Analytics (BA) : An Overview
Example :
An analytic application used for credit card scoring for a loan application
Calculate a credit worthiness score
Automatically accept or deny the loan application
Select the loan limit
Select which credit card product/deal to suit the applicant

8. The tools and techniques of BA
Three major categories of BA analytic tools and techniques
Information and knowledge discovery
Decision Support and Intelligent System
Visualization
Illustrated in the following diagram

9. The Business Analytics (BA) Field: An Overview

10. The Business Analytics (BA) Field: An Overview
Vendors classify BA tools in several different ways.
MicroStrategy’s classification of BA tools:
Enterprise reporting - used to generate highly formatted static reports meant for broad distribution to many people
Cube analysis – used to provide simple OLAP multidimensional slice and dice analytical capabilities to business managers
Ad hoc querying and analysis – used to allow power users to query a database for answers
Statistical analysis and data mining - statistical, mathematical and data mining tools are used to perform predictive analysis and to determine cause-and-effect correlations
Report delivery and alerting – report distribution engines to send full reports/alerts to internal/external users, based on subscriptions and schedules or threshold events

11. The Business Analytics (BA) Field: An Overview
Vendors classify BA tools in several different ways.
SAP’s classification of strategic enterprise management
Three levels of support
Operational – SAP R/3 mainly supports transaction processing on the operational level
Managerial – middle managers can use SAP/R3 to access all reports, arranged by functional areas
Strategic - SAP SEM (Strategic Enterprise Management)

12. The Business Analytics (BA) Field: An Overview
Major Capabilities of BA Tools
Drill-down
The investigation of information in detail (e.g., finding not only total sales but also sales by region, by product, or by salesperson).
Ad Hoc Analysis
Analysis made at any time, and with any desired factors and relationships
Slicing and dicing
Rearranging data so that they can be viewed from different perspectives
Exception Report
Using reports to highlight deviation larger than threshold

13. Online Analytical Processing (OLAP)
Variety of activities usually performed by users in online system – usually involving generating and answering queries, requesting ad-hoc reports, conducting statistical analysis and building visual presentation
An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on. The result is generated in seconds

14. Online Analytical Processing (OLAP)
OLAP versus OLTP
OLTP concentrates on processing repetitive transactions in large quantities and conducting simple manipulations
OLAP involves examining many data items complex relationships
OLAP may analyze relationships and look for patterns, trends, and exceptions
OLAP is a direct decision support method

15. Online Analytical Processing (OLAP)
Types of OLAP
Multidimensional OLAP (MOLAP)
OLAP implemented via a specialized multidimensional database (or data store).
It summarizes transactions into multidimensional views ahead of time
Data are organized into cube structure that users can rotate; particularly suitable for financial summaries

16. Online Analytical Processing (OLAP)
Types of OLAP
Relational OLAP (ROLAP)
The implementation of an OLAP on top of an existing relational database
Extracts data from relational database
Tends to be used on data that has a large number of attributes, where it cannot be easily placed into a cube structure. Example, customer data as oppose to financial data.
Web OLAP – accessible from Web Browser
Desktop OLAP – low priced, simple OLAP, performs local analysis from database

17. Online Analytical Processing (OLAP)
Four types of processing that are performed by analysts in an organization:
Categorical analysis – static analysis based on historical data
Exegetical analysis – also based on historical data, and it adds the capability of drill-down analysis (ability to query further down into data to determine the detail data that were used to obtain the derived value)
Contemplative analysis – allows user to change a single value to determine its impact
Formulaic analysis - allows changes to multiple variables
OLAP tools are designed to support all of the above activities

18. Online Analytical Processing (OLAP)
OLAP Products Evaluation Rules: Codd’s 12 Rules for OLAP
Multidimensional conceptual view for formulating queries
Transparency to the user
Easy accessibility: batch and online access
Consistent reporting performance
Client/server architecture: the use of distributed resources
Generic dimensionality
7. Dynamic sparse matrix handling
8. Multiuser support rather than support for only a single user
9. Unrestricted cross- dimensional operations
10. Intuitive data manipulation
11. Flexible reporting
12. Unlimited dimensions and aggregation level

19. Reports and Queries
The oldest activities of OLAP and BI are using reports and queries.
Reports
Routine reports
Ad hoc (or on-demand) reports
Multilingual support
Scorecards and dashboards
Report delivery and alerting
Report distribution through any touch point
Self-subscription as well as administrator-based distribution
Delivery on-demand, on-schedule, or on-event
Automatic content personalization

20. Reports and Queries Ad hoc query
A query that cannot be determined prior to the moment the query is issued . User might need to place such a query after seeing a report

21. Multidimensionality Multidimensionality
Raw and summary data can be organized in different ways for analysis and presentation
The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)
Three factors are considered in multidimensional presentation
Dimensions – products, salespeople, business units
Measures - money, sales, sales volumes, head count
Time – daily, weekly, monthly, quarterly

22. How multidimensionality works
A manager wants to know the sales of a product (by unit or dollar) in a certain geographic area, by a specific salesperson, during a specific month. The answer to such question can be provided fast if the data is organized in multidimensional database or if query or related software products are designed for multidimensionality. This will allow users to navigate through the many dimensions and levels of data via tables or graphs and are able to make quick interpretations, such as uncovering significant deviations or important trends.

23. Multidimensionality Multidimensional database
A database in which the data are organized specifically to support easy and quick multidimensional analysis
Data cube
A two-dimensional, three-dimensional, or higher-dimensional object in which each dimension of the data represents a measure of interest
Provides an opportunity to retrieve decision support information in an efficient way.

24. Multidimensionality Cube
The term cube refers to a subset of highly interrelated data that is organized to allow users to combine any attributes (e.g., stores, products, customers, suppliers) with any metrics (e.g., sales, profit, units, age) to create various two-dimensional views, or slices, that can be displayed on a computer screen

25. Multidimensionality More on cube
Example : A database contains transaction information relating company sales of products (p) to a customer (c ) at different store (s) locations. The data cube formed from this database is a three dimensional representation, with each cell ( p, c, s). The cube can be used to retrieve information within the database about, for example which store should be given a certain product to sell in order to make greater profit.

26. Multidimensionality

27. Multidimensionality Limitations of dimensionality
The multidimensional database can take up significantly more computer storage room than a summarized relational database
Multidimensional products cost significantly more than standard relational products
Database loading consumes significant system resources and time, depending on data volume and the number of dimensions
Interfaces and maintenance are more complex in multidimensional databases than in relational databases

28. Advanced Business Analytics
While OLAP concentrates on reporting and queries, a more sophisticated way of analyzing data and information is needed
Users today will want to perform statistical and mathematical analysis such as hypothesis testing, multiple regression, churn prediction and customer scoring models. Such investigation cannot be done with basic OLAP and will require special tools, including data mining and predictive analysis – hence, advanced business analytics

29. Advanced Business Analytics
A major step in managerial decision making is forecasting or estimating the results of different alternative courses of actions
Two methods that can be used for advanced business analytics are
Data mining and predictive analysis
Data mining
Predictive analysis

30. Advanced Business Analytics
Data mining
Tools that would automatically extract hidden, predictive information from databases, search for pattern in large transaction database. OLAP can only answer questions you are certain to ask, whereas data mining answers questions you don’t necessarily know you should ask (to be discussed further in the next chapter)
Predictive analysis
Use of tools that help determine the probable future outcome for an event or the likelihood of a situation occurring. These tools also identify relationships and patterns

31. Data Visualization Data visualization
A graphical, animation, or video presentation of data and the results of data analysis
Visual technologies can condense 1000 numbers in one picture and make decision support applications more attractive and understandable
The ability to quickly identify important trends in corporate and market data can provide competitive advantage
Check their magnitude of trends by using predictive models that provide significant business advantages in applications that drive content, transactions, or processes

32. Data Visualization New directions in data visualization
Dashboards and scorecards
Visual analysis
Financial data visualization

33. Data Visualization

34. Data Visualization

35. Now ask if … You are now be able to:
Describe business analytics (BA) and its importance to organizations
List and briefly describe the major BA methods and tools
Describe how online analytical processing (OLAP), data visualization, and multidimensionality can improve decision making
Describe advanced analysis methods
CISB594 – Business Intelligence