BI – Business Intelligence

This is a online course is to gain fundamental understanding of Business Intelligence. The aim is to learn about deploying and managing business intelligence systems. The tutorials will help you learn about Business Intelligence Introduction, ,industry scenario, Fundamentals of Business Intelligence, Project requirement, Extraction transformation load, Frame work for BI and Target System.

Business Intelligence means a set of methods and techniques that handles large amount of data which are used by organizations for the decision making process. It helps to leverage the technologies that focuses on counts, statistics and business objectives to improve the business performance. BI is often a stated goal with actual practice.

Why is business intelligence used?

Companies deal with lot of data in their day to day transactions. The process of turning all these data into actionable insights is termed as Business intelligence. The following are the advantages of Business Intelligence

• They can have greater insight into their organization activities
• They will be able to find out new opportunities
• BI will help them to make correction to the existing processes
• Identify the recent trends and take competitive advantage over others in the market
• They will be able to identify the top selling products based on few factors
• BI helps to track the competitors and compare information about their customers, products and costs.

Course Objectives

At the end of this course you will be able to

• Describe the process of decision making
• List out the steps in decision making
• Tools used in decision making
• Data warehouse concepts, design and data integration
• Know the functions of neural networks
• Understand what is meant by groupware systems

Pre Requisites for taking this course

The participants in this course should have some knowledge on the use of business data rules and patterns. Other than that they should have a passion to learn with a good internet connection.

Target audience for this course

This course is meant for business professionals who would like to gain knowledge and understanding of the advantages of business intelligence and knowledge management systems.

Course Description

Section 1:Business Intelligence Introduction

• 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 analysis

This 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.

FAQ’s General Questions

• What background do I need to undertake this course ?

You need not have any prior experience in BI to take up this course. You should have some basic knowledge in computer and internet to learn this course.

• We are planning to implement BI in our organization. Can this course help me ?

This course will definitely help you to implement the BI solution in your organization. It will let you understand the actions to be taken and the resources needed for proper BI implementation.

Testimonials

Adriana

This course offers a high level introduction to the concepts of BI program in an excellent way. Participants can gain more knowledge from this course and improve a BI program to maximize their business opportunities.

Kissova

Thanks to this course. It made me learn about BI in a simple way. The course is well organized, thoughtful and clear. The examples are interesting, relevant and easy to understand. This is probably the most outstanding online course for Business Intelligence both for beginners as well as for professionals.

Where do our learners come from?

Professionals from around the world have benefited from eduCBA’s BI – Business Intelligence Courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.

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