BI Introduction

Business data is growing big every day in terms of volume, velocity and variety. Business intelligence is used to organize such data in an organization and turn them into an useful information to the business. This chapter gives a brief introduction to the Business Intelligence, history of business intelligence, what are the technologies used and areas of application of business intelligence

Multidimensional DB

A multidimensional database is a type of database that is used in data warehouse and OLAP applications. In this chapter you will learn what is multidimensional database, OLAP applications introduction, Multidimensional analysis, Operational databases, OLTP and OLAP characteristics and examples.

Section 2: Fundamentals of Business Intelligence

DBMS platform

This section lets you understand what is database management system, types of DBMS and advantages of DBMS

Non technical Infrastructure

Under this chapter you will get topics like what is non technical infrastructure for BI applications, need for non technical infrastructure and the enterprise architecture.

Change Management

This chapter deals with developing change management for Business Intelligence, Frequency of changes in BI and why it is needed in an organization.

Planning deliverables

This topic explains the Business Intelligence Project roles and responsibilities and some of the deliverables that are created as a part of BI project such as Project charter and project plan.

Section 3: Project requirement

Project requirement, Data analysis application

The topics covered under this section are listed below

Approaching requirements for a business intelligence project

Tactical Business intelligence requirements – Identifying tactical business questions, analyzing and specifying tactical business requirements

Strategic business intelligence requirements – Identifying strategic business questions, dimensional models, analyzing and specifying tactical business requirements

Business analytical processes – Cluster business questions, decision tree flows, asymmetrical dimensions, business work flow processes

Data acquisition requirement analysis – Information architecture patterns and layers, ETL, ELT, data staging, data sources, data quality testing, modelling time variant data for integration.

Metadata in Business Intelligence

Here you will learn what is metadata, metadata in BI, benefits of metadata and how to implement a metadata management strategy in BI.

Section 4: Extraction, Transformation, Load (ETL)

ETL involves the following tasks

Extracting data from sources of the data warehouse

Transforming the data and

Loading the data into a data warehouse

This chapter involves the ETL process, which includes Extract, Clean, Transform and Loading.

It also involves the following topic

ETL and its application in data warehouses

Managing ETL process – Designing the ETL process, Staging process

ETL concepts – Data migration, data management, data cleansing, data synchronization, data consolidation.

ETL tool implementation – Data transformation tool, reasons for using the tool, benefits of using the ETL tool

ETL tools info portal – difference between business intelligence tools and data warehousing solutions

ETL tool evaluation and comparison process

Well known ETL tools – Includes some of the best known commercial ETL tools

ETL framework layer description – Schedule layers, Process control layers, Error management layer, Reconciliation layer, Status and reporting layer, Data tracking layer, Mapping layer

Designing ETL data reconciliation routines

ETL application development for data warehouse projects – includes steps in creating an ETL application

Metadata – What is Metadata, types of metadata – Business metadata and technical metadata, its structure, role of metadata in ETL, metadata repository development steps

ETL case studies and examples. Toyota company example is given here

Section 5: Framework for BI

This chapter includes the following section

Introduction and the need for a framework

Business intelligence conceptual framework – Source system, ETL system, DW system, DA system

Difference between the frameworks

Components of the framework – Data warehouse, Business analytics, Performance and strategy and User interface.

Business strategy and metrics

People, processes and technology in the BI framework

Program management

Metadata and services repositories

How the framework is used in BI and analytics

Strategic imperative of BI

This section explains that the BI is a strategic imperative and your infrastructure changes to handle it. You will also know the hurdles of BI, changing your infrastructure, what can be done today and what can be changed in the future to make your BI successful.

Section 6: Targit System

Targit System

Targit is the business intelligence software and this chapter explains its dashboards, analytics, reporting and data discovery platform in detail.

Data warehouse and ETL

This section contains Data warehouse concepts, design, data integration, ETL overview and process of ETL.

Facebook data space management with open source tools

BI tools used in Facebook data space management is explained in detail in this section

Agile Development Process

This chapter contains need for agile business intelligence, Agile best practices of BI, five factors in agile BI, agile BI development methodology.

Challenges on dash board

This chapter includes the four BI implementation challenges and the ways to overcome them.

Semantic Technologies

This section explains what is semantic web technologies, how it is applied to business intelligence, BI semantic model, semantic technologies for BI and its tools.

BI Algorithm By Example

Here you will learn the rise of the algorithms in BI, types of algorithms for BI and the examples of each algorithm.

Benefits of BI

This section explains the benefits of business intelligence and few companies using business intelligence software is given for your reference.

What is Information Governance

It includes what is information governance and step by step guide to make information governance,

Other BI Applications

The applications of BI and its advantages are mentioned in this chapter.

Designing and Implementing BI Program

It includes the analysis, design and implementation of a business intelligence solution.

ETL

This section includes the meaning of ETL, ETL process, ETL tools information, ETL concepts and ETL functions – Extraction, transformation and loading.

Dimension and facts

This chapter contains the following topics

What is a dimension in data warehouse

Basics of dimensions

What are fact tables and dimension tables

What is a fact table in a data warehouse

Conceptual Model

This chapter contains an introduction to conceptual model, its problem statement and research issues.

Section 7: Metadata

This section includes the following topics

What is metadata

How it can be managed

Extracting metadata from legacy systems

Metadata management strategy

Metadata management tools

Metadata advantages and disadvantages

Section 8: Data Advantage

Data Advantage Group

Data advantage group is the tool which offers metadata management and data governance. This tool is explained in detail in this chapter

DBMS Meta Data Tips

DBMS Metadata can be used to extract DDL definitions from a database. This is explained with examples in this section.

For Building The Data warehouse(Extraction Team)

This section contains overview of extraction in data warehouses, extraction methods in data warehouses and data warehousing extraction examples.

Metadata Essentials For IT

This chapter will help you to understand the concepts and terminologies of metadata, relationship between metadata and cataloguing and examples of metadata at work in IT.

Section 9: Business Metadata

Here you will learn business terms, its definitions, business rules and policies, advantages of business metadata and examples of business metadata.

Section 10: Project Planning

This chapter contains the following sections under it

Things to consider in project planning

Managing the BI project

Defining the BI project

Aspects of project planning

Brief description of project planning activities

Section 11: Deployment Process

This section lets you learn the deployment process and strategic approach to successful deployment. It is explained with a business problem and a solution.

Break even analysisThis chapter contains the following sections

Overview of break even analysis

How it improves business intelligence

Calculating breakeven point

Formula for breakeven point

Limitations of breakeven point

Section 12: Multivariate analysis

Here you will learn what is multivariate analysis, methods of multivariate analysis and its example.

Section 13: System development

Graphs

This section tells you about the different types of charts and graphs available in BI and how to select the graph or chart which suits your need.

Why metadata is important

This section explains the importance of metadata in Business Intelligence

Project Risk Assessment Factors

This section includes the project risk assessment process in BI and explains why such assessment is necessary in a project life cycle.

Managing Project timing

This chapter will help you to learn the  time management techniques of a project in BI.

Prototyping benefits

This section explains how better prototyping can improve the business intelligence of an organization.

Section 14: Incremental Development

This chapter explains the incremental approach for BI projects, its advantages and examples.

What is cluster analysis

Under this section you will understand

What is cluster analysis

How to harness the power of BI using cluster analysis

Types of cluster analysis

Benefits of cluster analysis

Section 15: K means clustering method

It explains what is K-means clustering method and how it is computed in BI with examples.

What is the problem with PAM

This section explains the reasons why Pam is not used much when compared to K-means clustering method.

BIRCH (1996)

It is one of the clustering method used for very large databases. In this chapter you will learn

What is BIRCH and its history

Clustering feature and CF tree

BIRCH clustering algorithm

Case studies or examples

Density Reachable And Density Connected

Both are density based clustering methods and are explained in detail in this section with examples

DENCLUE Technical Issues

This is another density based clustering technique used for large multimedia databases. In this section you will learn about this in detail.

The Wave Cluster Algorithm

This is a multi resolution clustering approach explained in brief in this chapter with examples

Conceptual Clustering

The concepts in conceptual clustering, its learning and observation are given under this chapter with examples.

Section 16: Clustering in Quest

This is a grid based clustering algorithm that separates each dimension of the dataset as a grid. This algorithm is explained in detail under this section.

Why Constraints Based Cluster Analysis

It tells you what is constrained clustering and why it is used in data analysis.

What Is Outlier Discovery

It is a distance based approach used to eliminate the limitation of statistical methods in data analysis. This algorithm in given in brief under this heading.

Segmentation In Data Mining

This explains what is data mining, what is data segmentation, the difference between the both and how it is used in BI.

Bottle Neck Of GSP & Spade

This comes under the spade algorithm and is explained with pictures.

Why Deal with Sequential Data

This chapter explains why it is more important to deal with sequential data during the process of data analysis.

Algorithm Definition

This topic explains what is algorithm in BI with a simple definition and example.

Introduction To Regression Analysis

Here you will learn what is regression analysis, types of regression analysis, how it is used in business intelligence and examples.

Section 17: Regression model

Regression model

The regression analysis model in business intelligence is explained using an example in this section.

Market Basket Analysis Applications

This chapter will help you to learn what is market basket analysis, how it is used, applications of market basket analysis and how to apply market basket analysis rules to improve cross sales.

(Duration:- 6h 07m)