UNIT-II

BUSINESS ANALYSIS

ByM.Dhilsath Fathima

Topics covered• Reporting and Query tools and Applications• Online Analytical Processing (OLAP)• Multidimensional Data Model• OLAP Guidelines• Cognos Impromptu• Multidimensional versus Multirelational OLAP • Categories of OLAP Tools.

BUSINESS ANALYSIS– It is the practice of identifying business needs,

capturing, analyzing and documenting requirements and supporting the communication and delivery of requirements with relevant stakeholders to define and implement an acceptable solution.

– The person who carries out this task is called a business analyst or BA.

– Major Task of business analyst Data analysis Decision Making

Applications of Business analysis

BUSINESS ANALYST-RESPONSIBILITIES• Collect, manipulate, analyze data and making

Decision.• They prepare reports, which may be in the

form of visualizations such as graphs, charts detailing the significant results they deduced.

• For example, data analysts might perform basic statistics such as variations and averages. They also might predict yields or create and interpret histograms.

Reporting And Query Tools And Application Tools / DECISION

SUPPORT TOOLSTool categories:• Reporting Tool • Managed Query• Executive Information System• OLAP • Data Mining

Reporting Tool• Rich, interactive display – Wide variety of tables, charts, graphs and other

visual BI tools can be configured and linked to source data to generate interactive data visualizations

• Share reports via a web browser – Interactive reports can be quickly shared through a web browser or any mobile device.

• Unify disparate data sources – Use data from multiple sources in a single report, including data from Excel, text/CSV files, any database (SQL Server, Oracle, MySQL), and Google platforms

• Automatic and manual data refresh – Reports can be refreshed manually or automatically at pre-defined intervals

• Fast query response – Query response is in seconds, even when dealing with huge amounts of data or working off commodity hardware

Types of reporting tools• Production Reporting Tools Companies

generate regular operational reports or support high volume batch jobs, such as calculating and printing pay checks.

• Report Writers (Desktop tools for end users) Crystal Reports / Actuate Reporting System /Excel.

Crystal Report

Jasper Report Business Intelligence

JMagallanus

Seal Report-For MS.Net(C#)

Ex:Fast Report

Various forms of Reporting

Charts-Bar chart ,Pie chartHistogramsTableGraphTextTree

Example for Business Intelligence Software

Report ServerCrystal reportMicrosoft Power BIRapid MinerPaloIBM Watson analytics.SAP LumiraJasper Report Business IntelligenceJmagallanusSeal Report

Managed Query Tools• Business Objects is the preferred tool for

creating and editing queries for all authorized users of the Warehouse data collections.

• joining tables, create Views, apply triggers and efficient nested querying.

• Import data from various formats such as delimited files, Excel spreadsheets, and fixed width files.

• Export data in various formats such as delimited files, Excel spreadsheets, text, HTML, XML.

Example

• DBComparer• EMS SQL Manager Lite for SQL Server• Firebird • SQuirreL SQL Client is a JAVA-based database

administration tool for JDBC compliant databases.

• SQLite Database Browser

OLAP Tools• Provide an intuitive way to view corporate

data.• Provide navigation through the hierarchies and

dimensions with the single click.• Aggregate data along common business

subjects or dimensions.• Users can perform OLAP operations such as

drill down, Roll up, Slice,Dice, Pivot.

OLAP CUBE• An OLAP Cube is a data structure that allows

fast analysis of data.• It consists of numeric facts called measures

which are categorized by dimensions.• Some popular OLAP server software programs

include: – Oracle Express Server.–Hyperion Solutions Essbase

Total annual salesof TV in U.S.A.Date

Produ

ct

Cou

ntrysum

sum TV

VCRPC

1Qtr 2Qtr 3Qtr 4QtrU.S.A

Canada

Mexico

sum

Example

Types of OLAP Operations

• Roll Up (Drill up)• Drill Down(Roll down)• Slice• Dice• Pivot

Roll Up (Drill up)• Roll-up performs aggregation on a data cube

by climbing up hierarchy or by dimension reduction

Roll Up (Drill up) (Cont..)

• Roll-up is performed by climbing up a concept hierarchy for the

dimension location.

• Initially the concept hierarchy was "street < city < province <

country".

• On rolling up, the data is aggregated by ascending the location

hierarchy from the level of city to the level of country.

• When roll-up is performed, one or more dimensions from the

data cube are removed.

Drill Down(Roll down)• Drill-down is the reverse operation of roll-up. It is performed

by either of the following ways: By stepping down a concept hierarchy for a dimension By introducing a new dimension.

Drill Down(Roll down)(Cond..)

• Drill-down is performed by stepping down a concept hierarchy

for the dimension time.

• Initially the concept hierarchy was "day < month < quarter <

year."

• On drilling down, the time dimension is descended from the

level of quarter to the level of month.

• When drill-down is performed, one or more dimensions from

the data cube are added.

• It navigates the data from less detailed data to highly detailed

data.

Slice• The slice operation selects one particular dimension

from a given cube and provides a new sub-cube. Consider the following diagram that shows how slice works.

Slice(Cont..)• Here Slice is performed for the

dimension "time" using the criterion time = "Q1".

• It will form a new sub-cube by selecting one or more dimensions.

Dice• Dice selects two or more dimensions from a given cube and

provides a new sub-cube. Consider the following diagram that shows the dice operation.

Dice(Cont..)

• The dice operation on the cube based on the following selection criteria involves three dimensions.

• (location = "Toronto" or "Vancouver")• (time = "Q1" or "Q2")• (item =" Mobile" or "Modem")

Pivot• The pivot operation is also known as rotation. It

rotates the data axes in view in order to provide an alternative presentation of data. Consider the following diagram that shows the pivot operation.

• In this the item and location axes in 2-D slice are rotated.

Example query-RollUp

OLAP vs Data Mining• Both data mining and OLAP are two of the common Business

Intelligence (BI) technologies. Business intelligence refers to computer-based methods for identifying and extracting useful information from business data.

• In large data warehouse environments, many different types of analysis can occur. Can enrich data warehouse with advance analytics using OLAP (On-Line Analytic Processing) and data mining.

OLAP Data Mining

For data analysis For Decision Making(Future Prediction)

Provides summary data and generates rich calculations

Data mining discovers hidden patterns in data. Data mining operates at a detail

level instead of a summary level.

Ex: How do sales of mutual funds in North America for this quarter compare with

sales a year ago?

Who is likely to buy a mutual fund in the next six months?

DATA MINING

• Data mining is the field of computer science which, deals with extracting interesting patterns from large sets of data. It combines many methods from artificial intelligence, neural network, machine learning, statistics and database management.

• Data mining is also known as Knowledge Discovery in data (KDD).

• Data mining usually deals with following four tasks: association ,clustering, classification, regression.

Functions of Data Mining

• Association is looking for relationships between variables.

• Clustering is identifying similar groups from unstructured data.

• Classification is learning rules that can be applied to new data,ie.Classification models predict categorical class labels for any application.

• Regression is finding functions with minimal error to model data.

Data Mining Tools

• Provide insights into corporate data that are not easily discerned with managed query or OLAP tools.

• Use a variety of statistical and Artificial Intelligence algorithms to analyze the correlation of variables in data.

• To investigate Interesting patterns and relationship.• Example:

IBM’s Intelligent Miner DataMind Corp.’s DataMind

Output –Data Mining Tool

Output –Data Mining Tool(Intelligent Miner Data)

Executive Information System Tools

• It is a type of management information system and decision support

system that facilitates and supports senior executives to perform data

analysis and decision-making needs.

• It provides easy access to internal and external information relevant

to organizational goals.

• It is an integrated tool to perform querying, reporting, OLAP analysis, Data

mining functions.

• EIS Apps highlight exceptions to business activity or rules by using color-

coded graphics. To Build customized, graphical decision support Tasks. .

Example(Pegasus-EIS Tool)

Example(Pegasus-EIS Tool)

Example(Pegasus-EIS Tool)

Dash board in vehicle

EIS Tool (Dash Board)• Digital dashboards allow managers, Executives to monitor the

contribution of the various departments in their organization.• showing a graphical presentation of the current status (snapshot)

and historical trends of an organization’s.Benefits of using digital dashboards include: Visual presentation of performance measures Ability to identify and correct negative trends Measure efficiencies/inefficiencies Ability to generate detailed reports showing new trends Ability to make more informed decisions based on

collected business intelligence Align strategies and organizational goals Saves time compared to running multiple reports Quick identification of data outliers and correlations

* Reference: http://www.arborsoft.com/essbase/wht_ppr/coddTOC.html* Reference: http://www.arborsoft.com/essbase/wht_ppr/coddTOC.html

What Is OLAP?

• Online Analytical Processing - coined by EF Codd in 1994 and contracted by Arbor Software*

• Generally synonymous with earlier terms such as Decisions Support, Business Intelligence, Executive Information System

• OLAP = Multidimensional Database

48

Strengths of OLAP

• It is a powerful visualization paradigm

• It provides fast, interactive response times

• It is good for analyzing time series data

• It can be useful to find some clusters and outliers

• Many vendors offer OLAP tools

49

Use/Nature of OLAP Analysis• Performs Aggregation -- (total sales, percent-to-

total)• Performs Comparison -- Budget vs. Expenses• Performs Ranking -- Top 10 customers, quartile

analysis• Access to detailed and aggregate data• Complex criteria specification• Visualization

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MULTI-DIMENSIONAL DATA MODEL

51

From Tables and Spreadsheets to Data Cubes

• A data warehouse is based on a multidimensional data model which views data in the form of a data cube

• A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions

– Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)

– Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables

• In data warehousing literature, an n-D base cube is called a base cuboid. The topmost 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube.

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Conceptual Modeling of Data Warehouses

• Modeling data warehouses: dimensions & measures

– Star schema: A fact table in the middle connected to a set of dimension tables

– Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake

– Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy

schema or fact constellation

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Example of Star Schematime_keydayday_of_the_weekmonthquarteryear

time

location_keystreetcityprovince_or_streetcountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_salesMeasures

item_keyitem_namebrandtypesupplier_type

item

branch_keybranch_namebranch_type

branch

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Example of Snowflake Schematime_keydayday_of_the_weekmonthquarteryear

time

location_keystreetcity_key

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures

item_keyitem_namebrandtypesupplier_key

item

branch_keybranch_namebranch_type

branch

supplier_keysupplier_type

supplier

city_keycityprovince_or_streetcountry

city

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Example of Fact Constellation

time_keydayday_of_the_weekmonthquarteryear

time

location_keystreetcityprovince_or_streetcountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_salesMeasures

item_keyitem_namebrandtypesupplier_type

item

branch_keybranch_namebranch_type

branch

Shipping Fact Table

time_key

item_key

shipper_key

from_location

to_location

dollars_cost

units_shipped

shipper_keyshipper_namelocation_keyshipper_type

shipper

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A Concept Hierarchy: Dimension (location)

all

Europe North_America

MexicoCanadaSpainGermany

Vancouver

M. WindL. Chan

...

......

... ...

...

all

region

office

country

TorontoFrankfurtcity

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Specification of Hierarchies

• Schema hierarchyday < {month < quarter; week} < year

• Set_grouping hierarchy{1..10} < inexpensive

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Multidimensional Data

• Sales volume as a function of product, month, and region

Prod

uct

Region

Month

Dimensions: Product, Location, TimeHierarchical summarization paths

Industry Region Year

Category Country Quarter

Product City Month Week

Office Day

CLASSIFICATION OF

OLAP TOOLS/SERVER

Need of OLAP• OLAP (online analytical processing) is computer processing

that enables a user to easily and selectively extract and view data from different points of view.

• Ex: Execute Query, Analyze Data ,Comparative Analysis, Generate Report.• To facilitate these, OLAP data is stored in

a multidimensional database. • OLAP software can locate the intersection of dimensions

(all products sold in the Eastern region above a certain price during a certain time period) and display them.

Classification of OLAP TOOLS/SERVER

MOLAP SERVER ROLAP SERVERHOLAP SERVER

MOLAP SERVER(Multidimensional On-Line Analytical Processing Server)

MOLAP Architecture

Database Server

Meta Data Request

Processing

MOLAP Server

Load

Result

SQLFront End Tool

Result Set

InfoRequest

MOLAP SERVER• Uses MDDBMS to organize and navigate data.• Structure of a multidimensional database is generally

referred to as a cube.• Data Structure: Array• MOLAP cube structure allows for particularly fast,

flexible data-modeling and calculation• It incorporate advanced array-processing techniques

and algorithms for managing data and calculations. As a result, multidimensional databases can store data very efficiently and process calculations in a fraction of the time required of relational-based products.

Advantage-MOLAP• Provides maximum query performance,

because all the required data (a copy of the detail data and calculated aggregate data) are stored in the OLAP server itself and there is no need to refer to the underlying relational database

Drawback-MOLAP• However, MOLAP system implementations

have very little in common, because no multidimensional logical model standard has yet been set.

• The lack of a common standard is a problem being progressively solved. This means that MOLAP tools are becoming more and more successful after their limited implementation for many years.

Example

Organization tool :• Microsoft (Analysis Services) • Oracle (Hyperion)

ROLAP Server(Relational On-Line Analytical Processing Server)

ROLAP Server-ARCHITECTURE

Database Server

Meta Data Request

Processing

ROLAP Server

Resultset

SQLFront End Tool

Result Set

InfoRequest

ROLAP Server• Data Structure: Table• Provides multidimensional analysis of data,

stored in a Relational database(RDBMS) ,i.e. directly access data stored in relational databases.

• ROLAP access a RDBMS by using SQL (structured query language), which is the standard language that is used to define and manipulate data in an RDBMS.

• Subsequent process are :accepts requests from clients, translates them into SQL statements, and passes them on to the RDBMS.

• ROLAP products provide GUIs to perform data analysis(End-User/Executives).

Advantage of ROLAP

• Ability to view the data in near real-time(Can Access Transactional Data).

• Since ROLAP does not make another copy of data as in case of MOLAP, it has less storage requirements. This is very advantageous for large datasets which are queried infrequently such as historical data.

Drawback of ROLAP

• Compared to MOLAP the query response time and Processing time is also typically slower because everything is stored on relational database and not locally on the OLAP server.

Example-ROLAP Tools

Vendors Tools• Information advantage (Axsys)• Microstrategy (Dss agent/ Dss server)• Platinum/Prodea software (Beacon)• Sybase (High gate project)

Managed Query Environment/HOLAP

(Hybrid On-Line Analytical Processing Server)

HOLAP/MQE/Hybrid architecture

RDBMS

Database Server

MOLAP Server

Resultset

SQL

Front End Tool

Result Set

InfoRequest

Load

Result set

SQL Query

OR

Managed Query Environment/HOLAP

• HOLAP(Hybrid OLAP) a combination of both ROLAP and MOLAP can provide multidimensional analysis simultaneously of data stored in a multidimensional database and in a relational database(RDBMS).

Advantage of HOLAP

• HOLAP balances the disk space requirement, as it only stores the aggregate data on the OLAP server and the detail data remains in the relational database. So no duplicate copy of the detail data is maintained on server.

Drawback of HOLAP

• Query performance (response time) degrades if it has to drill through the detail data from relational data store, in this case HOLAP performs very much like ROLAP.

Comparison of OLAP Server’sMOLAP ROLAP HOLAP

ADVANTAGE

Provides maximum query performance, because all the required data (a copy of the detail data and calculated aggregate data) are stored in the OLAP server itself and there is no need to refer to the underlying relational database

•Ability to view the data in near real-time.•Since ROLAP does not make another copy of data as in case of MOLAP, it has less storage requirements. This is very advantageous for large datasets which are queried infrequently such as historical data.

•HOLAP balances the disk space requirement, as it only stores the aggregate data on the OLAP server and the detail data remains in the relational database.•So no duplicate copy of the detail data is maintained.

Comparison of OLAP Server’sMOLAP ROLAP HOLAP

DISADVANTAGE

•However, MOLAP system implementations have very little in common, because no multidimensional logical model standard has yet been set. •MOLAP stores a copy of the relational data at OLAP server and so requires additional investment for storage

Compared to MOLAP or HOLAP the query response is generally slower because everything is stored on relational database and not locally on the OLAP server.

Query performance (response time) degrades if it has to drill through the detail data from relational data store, in this case HOLAP performs very much like ROLAP.

Other less popular kinds of OLAP technology • MOLAP-Mobile OLAP is merely refers to OLAP functionalities on

a wireless or mobile device. This enables users to access and work on OLAP data and applications remotely thorough the use of their mobile devices.

• Desktop OLAP/“DOLAP,” is based on the idea that user can download a section of an OLAP model from another source, and work with that dataset locally, on their desktop.

• WOLAP signifies a Web browser – based OLAP technology. And it suggests a technology that is Web-based only, without any kind of option for a local install or local client to access data.

• The aim of Spatial OLAP (SOLAP) is to integrate the capabilities of both Geographic Information Systems (GIS) and OLAP into a unified solution, thus facilitating the management of both spatial and non-spatial data.

OLAP GUIDELINES

• Dr. E.F. Codd, the “father” of the relational model, has formulated a list of guide lines and requirements as the basis for selecting OLAP systems/Server.

GUIDELINES• Multidimensional conceptual view

A tool should provide users with a multidimensional model that corresponds to the business problems and is spontaneously analytical and easy to use.

• AccessibilityThe OLAP system should be able to access data from all heterogeneous enterprise data source required for the analysis.

• Unrestricted cross-dimensional operationsThe OLAP system must be able to recognize dimensional hierarchies and automatically perform associated roll-up-calculations within and across dimensions.

GUIDELINES(CONT..)• Consistent reporting performance

As the number of dimensions and the size of the database increase, users should not recognize any significant degradation in performance.

• Intuitive data manipulationConsolidation path reorientation (pivoting), drill-down and roll-up, and other manipulations should be accomplished via direct point-and click, drag and drop actions on the cells of the cube.

• Multiuser supportThe OLAP system must be able to support a work group of users working concurrently on a specific model.

GUIDELINES(CONT..)• Transparency

The OLAP system’s technology, the underlying database and computing architecture (client/server, gateways, etc.) and the heterogeneity of input data sources should be transparent to users to maintain their productivity and proficiency with familiar front-end environments and tools (e.g., MS Windows , MS Excel).

GUIDELINES(CONT..)• Client/server architecture

The OLAP system has to conform to client/server architectural principles for maximum price and performance, flexibility, adaptively and interoperability.

• Flexible reportingThe ability to arrange rows, columns and cells in a fashion that facilitates analysis by spontaneous visual presentation of analytical reports must exist.

GUIDELINES(CONT..)• Comprehensive database management tools

These tools should functions as an integrated centralized tool and allow for database management for the distributed enterprise.

• The ability to drill down to detail (source record) level

This means that the tools should allow for a smooth transition from the multidimensional (pre aggregated) database to the detail record level of the source relations data bases.

COGNOS IMPROMPTU

Cognos Impromptu• Impromptu is an interactive database reporting

tool from IBM- Cognos Corporation.• Provides Flexible data warehousing and

database reporting solution.• Cognos Impromptu is an intuitive, user-friendly

system that enables non-technical personnel (Power User) to quickly and easily design and distribute business intelligence reports

• Easy-to-use graphical user interface.

Cognos Impromptu(Cont..)

Cognos Impromptu(Cont..)• In terms of scalability, support single user

reporting on personal data, or thousand of users reporting on data from large warehouse.

• When using the Impromptu tool, no data is written or changed in the database. It is only capable of reading the data and generating report.

• Extensive reporting capabilities allow users to create one-time and recurring reports that support your exact information requirements and dynamic business needs.

Output -Cognos Impromptu

Output -Cognos Impromptu

Cognos Impromptu-Catalog• Catalog contains metadata which is used retrieved by warehouse

database. • A catalog is a set of instructions containing information about the

data items to be retrieved and the database columns in a user friendly way.

• A catalog acts as an interface between the End-user and the data base thereby hiding the complexities of the database.

• A catalog contains Folders, Calculations, Conditions(Filters) and prompts.

• Catalog does not contain any data,It just contains the table structures and definitions(Like Meta data).

Cognos Impromptu-CatalogA catalog contains: Folders—meaningful groups of information representing

columns from one or more tables Columns—individual data elements that can appear in one

or more folders Calculations—expressions used to compute required

values from existing data. Conditions—used to filter information so that only a

certain type of information is displayed Prompts—pre-defined selection criteria prompts that users

can include in reports they create Other components, such as metadata, a logical database name, join information, and user classes

Cognos Impromptu-Catalog

• There are two different types of catalogs available with Cognos :

Personal Catalog: Only the creator can make use of it.

Shared Catalog: A catalog is kept in a common server, where users can access it to create reports using it.

The following table shows Sybase DDL statements that create a table named ACCOUNTS using the login BIADMIN, together with the equivalent mapping in Impromptu.

Impromptu's main features• Flexible report creation: frame-based report builder with

features such as prompts, pick lists, filters, and grouping, sorting and formatting capabilities. Provides powerful data summary and calculation features.

• Linked reports: a report author can easily create a system of linked reports to explore the data and move from summary to detail. Enables queries and reports that are quickly and easily designed and distributed.

• Supports the creation of customized reports ranging from simple lists to series of interactive, linked reports with drill-down capabilities.

Impromptu's main features(Cont..)

• Powerful summaries and calculations. • Supports the creation of one-time and

recurring reports.• Advanced reporting options let users build a

wide variety of reports: grouped lists, crosstabs, charts and more.

• Provides a variety of output formats including PDF and formatted Excel spreadsheets.

Benefits of Cognos Impromptu• Reduces the resources and time historically

required to generate comprehensive reports.• Effectively and efficiently supports information

requirements for your dynamic business needs.

• Enables non-technical personnel to generate professional, graphically-enhanced reports.

• Improves efficiency with automated report generation and electronic distribution.