This tutorial covers the topics of introduction to business intelligence with examples of BI scenarios and touches upon ETL(Extract, Transform and Load) operations using SSIS on SQL 2005 & 2008 and using DTS on SQL 2000. It contains introductions to crystal reports and SSRS. It compares Data warehouse and OLAP Cube. This tutorial concludes with topics on Data Mining and Dashboards.

1. SQL Business Intelligence
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2. What is Business Intelligence
 Business intelligence (BI) is a broad category of application programs and
technologies for gathering, storing, analysing, and providing access to
data from various data sources, thus providing enterprise users with reliable
and timely information and analysis for improved decision making.
 BI is an umbrella term that refers to an assortment of software applications
for analysing an organization’s raw data for intelligent decision making for
business success.
 BI as a discipline includes a number of related activities, including decision
support, data mining, online analytical processing (OLAP), querying and
reporting, statistical analysis and forecasting.

3. Business Intelligence for Success
 Business intelligence equips enterprises to:
 gain business advantage from data
 anticipate enhanced turnaround time on data collection
 come up with fresh ideas for novel business initiatives
 foresee accurate picture of customer needs and demands
 perform more targeted marketing campaigns
 gain enhanced knowledge that will help it advance its brand into the top slot in
terms of market share
 cut down its client churn
 reduce its overheads and also diminish delays in supply chain

4. Business Intelligence Scenario
 Every day, a dairy company collects from 2,000 stores information which is
uploaded to servers at its headquarters at the end of the day. This
information is used by the chain’s main office to instantly analyse key
operational measures such as the number of dairy products sold, profits,
trends, and so forth.
 Next day, by early morning the company’s managers receive performance
data. Next, they verify current revenue, time required to perform each job,
and other performance measures. With BI, franchisees with multiple
locations can have consolidated views, as can the company’s regional
managers.
 This scenario clearly explains how implementation of Business intelligence
can be very fruitful for an organization.

5. BI and Business Success
 BI can catalyse a business’s success in terms of:
 Revenues
 Distinguish the products and services that drive revenues.
 Rank customers and locations based on profitability.
 Customer relationship management
 Categorize low-value customers and work toward improving their value.
 Find customer relationship issues early and take timely action to resolve them.
 Sales and marketing
 Aim at high-value customers to minimize marketing risk.
 Rank the success of product promotions based on product and market
segment.
 Find what is in the sales pipeline

6. ETL operations using SSIS on SQL 2005 & 2008
 SQL Server Integration Services (SSIS) is a tool that we use to perform ETL
operations; i.e. extract, transform and load data.
 At a high level, SSIS provides the ability to:
 retrieve data from just about any source
 perform various transformations on the data; e.g. convert from one type to
another, convert to uppercase or lowercase, perform calculations, etc.
 load data into just about any source
 define a workflow
 The first version of SSIS was released with SQL Server 2005.

7. Creating SSIS Packages with SQL Server
Management Studio (SSMS)
 SQL Server Management Studio (SSMS) provides Import and Export Wizard
tasks which you can use to copy data from one data source to another.
 You can choose from a variety of source and destination data source
types, select tables to copy or specify your own query to extract data, and
save your work as an SSIS package.
 You can run the generated SSIS package as is, schedule it to run at a later
time, or make any necessary changes to it to fit your needs.
 Using the Import and Export Wizard is a good starting point for learning
about SSIS packages.

8. ETL operations using DTS on SQL 2000
 Data Transformation Services, or DTS, is a set of objects and utilities to allow the
automation of extract, transform and load operations to or from a database.
 The objects are DTS packages and their components, and the utilities are called
DTS tools.
 SQL Server 2000 expanded DTS functionality in several ways.
 Many new types of tasks were made, including the ability to FTP files, move
databases or database components, and add messages into Microsoft
Message Queue.
 DTS packages can be saved as a Visual Basic file in SQL Server 2000, and this
can be expanded to save into any COM-compliant language. Packages were
also integrated into Windows 2000 security, DTS tools were made more userfriendly, and tasks can accept input and output parameters.

9. SQL Server Reporting Services (SSRS)
 SQL Server Reporting Services provides a full range of ready-to-use tools
and services to help you create, deploy, and manage reports for your
organization, as well as programming features that enable you to extend
and customize your reporting functionality.
 Reporting Services is a server-based reporting platform that provides
comprehensive reporting functionality for a variety of data sources.
 Reporting Services includes a complete set of tools for you to create,
manage, and deliver reports, and APIs that enable developers to integrate
or extend data and report processing in custom applications.
 Reporting Services tools work within the Microsoft Visual Studio environment
and are fully integrated with SQL Server tools and components.

10. SQL Server Reporting Services (SSRS)
contd.,
 With Reporting Services, you can create interactive, tabular, graphical, or freeform reports from relational, multidimensional, or XML-based data sources.
 Reports can include rich data visualization, including charts, maps, and
sparklines.
 The reports that you create can be viewed over a Web-based connection or as
part of a Microsoft Windows application or SharePoint site.
 You can also create data alerts on reports published to a SharePoint site and
receive email messages when report data changes.
 In SQL Server 2012, Reporting Services introduces Power View, an interactive
data exploration, visualization, and presentation experience for the Reporting
Services Add-in for Microsoft SharePoint Server 2010 Enterprise Edition.

11. Crystal reporting using .NET framework
 Crystal Reports for Visual Studio 2005 ships with deployment projects that
enable you to deploy your application to target machines.
 Deployment components include report files, Crystal Reports redistribution
files and the .NET Framework 2.0 redistribution package.
 Deployment in Visual Studio 2005 is based on Windows Installer technology.
 The setup project builds installer files with .exe and .msi extensions. Those
files can be distributed for installation on computers that do not have
Crystal Reports or Visual Studio 2005 installed. It is recommended to
distribute project files using the .msi Windows Installer technology.

12. Deployment Overview
 To deploy an application, start with a project and add the following
components:
 Project Output:
 Selected files from the project necessary to deploy the application to client machines.
 Report Files
 Non-embedded reports to be distributed to client machines.
 .NET Framework 2.0
 .NET components that are necessary for an application to run.
 Crystal Reports for .NET Framework 2.0 Windows Installer
 Installer technology that sets up Crystal Reports runtime files on target machines without the
need to separately add selected merge modules to the project output.
 Merge Modules
 Components that enable clients to view applications that use Crystal Reports.

13. SQL Server Analysis Services (SSAS)
 Microsoft SQL Server Analysis Services, SSAS, is an Online Analytical
Processing, OLAP, data mining and reporting tool in Microsoft SQL Server.
 SSAS is used as a tool by organizations to analyze and make sense of
information possibly spread out across multiple databases, or in disparate
tables.
 Analysis Services includes a group of OLAP and data mining capabilities.
 This feature rich tool set offers insight into your data that you know exists,
but just don't have an easy way of accessing. By writing queries and reports
you can get the sense of certain things that you are looking for, but by
utilizing Analysis Services you have the ease of maneuvering through your
data with much less effort. This gives you insight to areas that are often
overlooked or not even thought about.

14. Pivot Tables
 You can use the PIVOT and UNPIVOT relational operators to change a
table-valued expression into another table.
 PIVOT rotates a table-valued expression by turning the unique values from
one column in the expression into multiple columns in the output, and
performs aggregations where they are required on any remaining column
values that are wanted in the final output.
 UNPIVOT performs the opposite operation to PIVOT by rotating columns of a
table-valued expression into column values.
 When PIVOT and UNPIVOT are used against databases that are upgraded
to SQL Server 2005 or later, the compatibility level of the database must be
set to 90 or higher.

15. OLAP Cubes
 An OLAP cube is an array of data understood in terms of its 0 or more
dimensions.
 A cube can be considered a generalization of a three-dimensional
spreadsheet. For example, a company might wish to summarize financial
data by product, by time-period, and by city to compare actual and
budget expenses.
 OLAP data is typically stored in a star schema or snowflake schema in a
relational data warehouse or in a special-purpose data management
system. Measures are derived from the records in the fact table and
dimensions are derived from the dimension tables.

16. Data Warehouse vs. OLAP Cube?
Data Warehouse
 A data warehouse is a database
with a design that makes analyzing
data easier† (often with data from
many sources). It is usually
composed of fact tables and
dimension tables, and often
aggregate tables.
 a data warehouse is a place to
store data in an easily analyzable
format
 they compliment each other in that
a data warehouse makes it easy to
analyze data using OLAP
OLAP Cube
 OLAP is a set of operations that
one can do on a data set, for
example pivoting, slicing, dicing,
drilling. For example, one can do
OLAP operations with MS Excel
PivotTables.
 OLAP is a method to analyze
data.
 OLAP can make analyzing a data
warehouse faster.

17. Data Mining
 Generally, data mining (sometimes called data or knowledge discovery) is
the process of analyzing data from different perspectives and summarizing
it into useful information - information that can be used to increase
revenue, cuts costs, or both.
 Data mining is primarily used today by companies with a strong consumer
focus - retail, financial, communication, and marketing organizations.
 With data mining, a retailer could use point-of-sale records of customer
purchases to send targeted promotions based on an individual's purchase
history.
 Data mining software analyzes relationships and patterns in stored
transaction data based on open-ended user queries.

18. Data Warehousing
 A data warehouse is a relational database that is designed for query and
analysis rather than for transaction processing.
 In addition to a relational database, a data warehouse environment
includes an extraction, transportation, transformation, and loading (ETL)
solution, an online analytical processing (OLAP) engine, client analysis tools,
and other applications that manage the process of gathering data and
delivering it to business users.
 the characteristics of a data warehouse are:
 Subject Oriented
 Integrated
 Nonvolatile
 Time Variant

19. Dashboards
 Dashboards often provide at-a-glance views of KPIs (key performance
indicators) relevant to a particular objective or business process (e.g. sales,
marketing, human resources, or production).
 Dashboards give signs about a business letting the user know something is
wrong or something is right. Dashboards typically are limited to show
summaries, key trends, comparisons, and exceptions.
 There are four Key elements to a good dashboard:
 Simple, communicates easily
 Minimum distractions...it could cause confusion
 Supports organized business with meaning and useful data
 Applies human visual perception to visual presentation of information

20. Thank You!
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