MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
Objective: The objective of a lesson covering the Business Intelligence Component Framework, BI
Processes, and BI Roles and Responsibilities is to provide students or learners with a comprehensive
understanding of the key elements and functions of Business Intelligence (BI) within an organization.
The lesson aims to familiarize participants with the various components involved in BI implementation,
the processes used to achieve BI objectives, and the roles and responsibilities of individuals involved
in BI initiatives.
1. BI Component Framework
Business Intelligence (BI) component framework can be divided into three major layers:
• Business Layer
• Administration and Operation Layer
• Implementation Layer
1.1 Business Layer
This layer consists of the four components depicted in Figure 1.1
BUSINESS BUSINESS VALUE
REQUIREMENTS
BUSINESS
LAYER
PROGRAM
MANAGEMENT DEVELOPMENT
T
Figure 1.1 The Business Layer
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MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
1.1.1 Business Requirements: The requirements are a product of three steps of a business process
namely, Business drivers, Business goals and Business strategies:
• Business drivers: Business drivers are the key factors or influences that motivate
organizations to pursue specific business initiatives or strategic goals. These drivers can
vary depending on the company's industry, market conditions, and overall objectives. Few
examples of business drivers are: changing workforce, changing labour laws, changing
economy, changing technology, etc.
• Business goals: Business goals are specific and measurable objectives that organizations
set to align their efforts with the key business drivers. These goals represent the desired
outcomes that the company aims to achieve by focusing on the critical factors that
influence its success. Here are some examples of business goals corresponding to various
business drivers.
Business Driver: Market Demand
Business Goal: Increase market share by 15% within the next year by launching new
product variations based on customer feedback.
Business Driver: Cost Efficiency
Business Goal: Reduce operational expenses by 10% in the upcoming quarter through
process optimization and resource consolidation.
Business Driver: Technological Advancements
Business Goal: Implement an AI-powered chatbot on the website to improve customer
support response times and enhance user experience.
These examples demonstrate how specific business goals are directly linked to the business
drivers, reflecting the organization's strategic focus and priorities to drive growth and success.
• Business Strategies: Business strategies are the well-thought-out and planned approaches that
organizations employ to achieve their long-term objectives and fulfill their mission. These
strategies outline the actions, decisions, and resource allocations necessary to gain a
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MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
competitive advantage and succeed in the marketplace. Few examples of business strategies
are: outsourcing, global
1.1.2 Business Values: It refers to the measurable benefits and advantages that a business gains
from its BI initiatives and data-driven decision-making processes. It is the positive impact or
outcomes that BI brings to the organization in terms of increased efficiency, improved
decision-making, cost savings, revenue growth, and overall competitiveness.
Business value can be measured in terms of ROI (Return on Investment) , ROA ( Return on Assets)
, TCO (Total Cost of Ownership) , TVO (Total value of Ownership) etc.
• Return on Investment (ROI): Return on Investment (ROI) is a financial metric used to
evaluate the profitability or efficiency of an investment. It measures the return or gain on an
investment relative to its cost. ROI is expressed as a percentage and provides insights into how
effectively an investment has performed over a specific period.
For example, let's consider a business that invests $10,000 in a marketing campaign and, as a
result, generates $15,000 in additional revenue directly attributable to the campaign. The net
profit from the investment would be $15,000 (Revenue) - $10,000 (Cost) = $5,000.
Now, we can calculate the ROI:
ROI = ($5,000 / $10,000) x 100 = 50%
In this example, the ROI for the marketing campaign is 50%. It means that for every dollar
invested in the campaign, the business gained an additional 50 cents in profit.
ROI is a crucial metric for businesses and investors as it helps them assess the success of various
investments and compare the relative performance of different opportunities. A positive ROI
indicates that an investment has generated more returns than its cost, making it profitable.
Conversely, a negative ROI suggests that the investment has resulted in a net loss.
It's important to note that ROI should be interpreted in the context of the investment's timeframe
and the industry or market norms. Short-term and long-term ROI can vary significantly, and
some investments, such as those in research and development or brand building, may take time
to yield substantial returns. Therefore, it's essential to consider the appropriate time horizon
when evaluating ROI.
Page 3
MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
• Return on Assets (ROA): It is a financial ratio that measures a company's profitability and
efficiency in generating earnings from its total assets. It is a key indicator of how effectively a
company utilizes its assets to generate profit.
The formula for calculating Return on Assets (ROA) is as follows:
ROA = (Net Income / Average Total Assets) x 100
Where:
Net Income refers to the company's total earnings after deducting all expenses, taxes, and
interest.
Average Total Assets is the average value of the company's total assets over a specific period,
usually calculated by adding the total assets at the beginning and end of the period and dividing
by two.
The result is then multiplied by 100 to express ROA as a percentage.
For example, let's consider a company with a net income of $500,000 at the end of the year.
At the beginning of the year, the company had total assets of $5,000,000, and at the end of the
year, it had total assets of $5,500,000. To calculate the ROA:
ROA = ($500,000 / (($5,000,000 + $5,500,000) / 2)) x 100 ROA = ($500,000 / $5,250,000) x
100 ROA = 9.52%
In this example, the Return on Assets (ROA) for the company is 9.52%. It means that for every
dollar of assets the company holds, it generates approximately 9.52 cents of net income.
ROA is a critical metric for investors, creditors, and managers to assess a company's financial
performance and efficiency. A higher ROA indicates that the company is effectively utilizing
its assets to generate profits, which is generally considered favorable. A lower ROA may
suggest that the company's asset utilization is less efficient and may warrant further
investigation. It's essential to compare ROA with industry benchmarks and the company's
historical performance to gain a more meaningful understanding of its financial health and
efficiency. Additionally, it is crucial to analyze ROA in conjunction with other financial ratios
and factors to get a comprehensive view of a company's overall performance.
• Total Cost of Ownership (TCO): It is a comprehensive financial estimate that takes into
account all direct and indirect costs associated with owning, operating, and maintaining an asset
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MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
or investment over its entire lifecycle. TCO is a valuable tool for businesses and organizations
to evaluate the true cost of acquiring and using a product, service, or asset, beyond the initial
purchase price.
In the context of business, TCO analysis helps decision-makers make informed choices by
considering the long-term expenses related to an investment. This approach is particularly
useful for comparing different options, such as software solutions, equipment purchases, or
outsourcing decisions, as it provides a more accurate assessment of the financial implications.
Total Cost of Ownership provides a more holistic view of the financial impact of an investment
over its entire lifecycle, allowing businesses to make more informed choices and avoid
unexpected cost surprises down the line.
• Total Value of Ownership: Total value of ownership (TVO) or total value of opportunity, is
a methodology of measuring and analyzing the business value of IT investments. Gartner Group
designed this methodology in 2003. TVO differs from total cost of ownership (TCO) in that
TVO considers the benefits of alternative investments. It is a comparative measurement that
evaluates the TCO and any additional benefits, such as the mobility of laptops when compared
to desktop computers.
1.1.3 Program Management: This component of the business layer ensures that people, projects,
and priorities work in a manner in which individual processes are compatible with each other
so as to ensure seamless integration and smooth functioning of the entire program. It should
attend to each of the following:
o Business priorities
o Mission and goals
o Strategies and risks
o Multiple projects
o Dependencies
o Cost and value
o Business rules
o Infrastructure
2 Development: The process of development consists of
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MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
• database/data-warehouse development (consisting of ETL, data profiling, data cleansing and
database tools),
• data integration system development (consists of data integration tools and data quality tools)
• business analytics development (about processes and various technologies used).
2.1 Administration and Operation Layer:
This Layer consists of four components:
(1) BI Architecture: Following are the components of BI Architecture
• Data:
o should follow design standards
o Must have a logically apt data model
o Metadata should be of high standard
• Integration:
o Performed according to business semantics and rules
o During integration, certain processing standards have to be followed.
o Data must be consistent.
Page 6
MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
• Information:
o Information derived from the data that has been integrated should be usable and findable
as per the requirements.
• Technology:
o Technology used for deriving Information must be accessible
o Also, it should have a good user – interface.
o Should support analysis, decision making, data and storage management.
• Organization:
o Consists of the different roles and responsibilities, like management, development,
support and usage roles.
(2) BI and DW (data warehouse) Operations: Data warehouse administration requires the usage of various
tools to monitor the performance and usage of the warehouse and perform administrative tasks on it.
Some of these tools are:
• Backup and restore
• Security
• Configuration Management
• Database Management
(3) Data Resource Administration: It involves data governance and metadata management.
• Data Governance: Data governance is a technique for controlling data quality, which is used to
assess, improve, manage and maintain information. It helps to define standards that are required
to maintain data quality. The distribution of roles for governance of data is as follows:
Data ownership
Data stewardship
Data custodianship
• Metadata Management: Metadata is data about data.
Consider CD/DVD of music. There is the date of recording, the name of the artist, the
genre of music, the songs in the album, copyright information, etc. All this information
constitutes the metadata for the CD/DVD of music. In the context of a camera, the data is
the photographic image. The metadata then is the date and time when the was taken. In
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MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
simple words, metadata is data about data. When used in the context of a data warehouse,
it is the data that defines the warehouse objects. Few examples of metadata are timestamp
at which the data was extracted, the data sources from where metadata has been extracted,
and the missing fields/columns that have been added by data cleaning or integration
processes. Metadata management involves tracking, assessment, and maintenance of
metadata.
Metadata can be divided into four groups:
o Business metadata
o Process metadata
o Technical metadata
o Application metadata
Application
Business Metadata Process Metadata Technical Metadata
Metadata
• Data definitions • Source/target • Data locations • Data access
• Metrics maps • Data formats history:
definitions • Transformation Technical names • Who is accessing?
• Subject models rules Data Data sizes Frequency of
• Data models cleansing rules • Data types access?
Extract audit trail Indexing • When accessed?
• Business rules
Transform audit • Data structures How accessed?
Data rules
trail Load audit etc. etc.
• Data trail
owners/stewards,
• Data quality audit
etc.
etc.
(4) Business Applications: The application of technology to produce value for the business refers to
the generation of information or intelligence from data assets like data warehouses/data marts.
Using BI tools, we can generate strategic, financial, customer, or risk intelligence. This
information can be obtained through various BI applications, such as DSS (decision support
system), EIS (executive information system), OLAP(On-line analytical processing), data mining
and discovery, etc.
Page 8
MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
2.2 Implementation Layer
The implementation layer of the BI component framework consists of technical components that are
required for data capture, transformation and cleaning, data into information, and finally delivering that
information to leverage business goals and produce value for the organization.
(1) Data Warehousing
o Data Sources
o Data Acquisition, Cleaning, and Integration
o Data Stores
(2) Information Services
o Information Delivery
o Business Analytics
It is the process which prepares the basic repository of data (called data warehouse) that becomes
the data source where we extract information from.
Date Warehouse: A data warehouse is a data store. It is structured on the dimensional model
schema, which is optimized for data retrieval rather than update.
Data warehousing must play the following five distinct roles:
• Intake
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MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
• Integration
• Distribution
• Delivery
• Access
Information Services is not only the process of producing information; rather, it involves
ensuring that the information produced is aligned with business requirements and can be
acted upon to produce value for the company. Information is delivered in the form of KPI’s,
reports, charts, dashboards or scorecards, etc., or in the form of analytics. Data mining is a
practice used to increase the body of knowledge. Applied analytics is generally used to
drive action and produce outcomes.
Business Intelligence Process
Business Intelligence (BI) is a process that involves gathering, analyzing, and presenting
data to help organizations make informed decisions. It empowers businesses with insights
into their operations, performance, and market trends, allowing them to improve
efficiency, optimize processes, and identify growth opportunities.
Page 10
MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
The BI process typically involves several key stages:
Data Collection:
The first step in the BI process is data collection. Relevant data is gathered from various
sources, which can include internal systems (such as databases, CRM systems, ERP
systems) and external sources (like market research reports, public data sets, social media
platforms, etc.). Data can be structured (e.g., in databases) or unstructured (e.g., in text
files, documents, social media posts).
Data Integration:
Once data is collected, it needs to be integrated and consolidated from different sources.
Data integration ensures that information is consistent and can be analyzed collectively.
This step may involve data cleaning, transformation, and normalization to ensure data
accuracy and consistency.
Data Warehousing:
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MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
In this step, the integrated data is stored in a data warehouse. A data warehouse is a
centralized repository that allows for efficient querying and analysis. It is designed to
support complex analytical queries and reporting while separating operational and
analytical systems.
Data Analysis:
With the data in the data warehouse, the BI process moves to the analysis phase. Different
analytical techniques and tools are applied to explore patterns, trends, correlations, and
insights within the data. Common data analysis techniques include data mining, statistical
analysis, data visualization, and more.
Reporting:
The insights gained from data analysis are presented in the form of reports and dashboards.
These reports can be generated regularly or on-demand to provide a comprehensive view
of the organization's performance and critical metrics. Interactive dashboards allow users
to explore data, filter information, and drill down into specific details.
Data Visualization:
Data visualization is an essential part of the BI process. It involves presenting data in
graphical formats, such as charts, graphs, and maps. Visual representations make complex
information more accessible and understandable, helping users quickly grasp trends and
patterns.
Business Decision Making:
Armed with the insights and reports generated by the BI process, business leaders and
decision-makers can make informed decisions to address challenges, capitalize on
opportunities, and improve overall business performance.
Continuous Monitoring and Improvement:
Page 12
MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
BI is an ongoing process, not a one-time event. Continuous monitoring of key performance
indicators (KPIs) and data is crucial to track progress and make necessary adjustments to
business strategies. Feedback from users helps improve the BI system and make it more
responsive to evolving business needs.
Business Intelligence tools and technologies play a significant role in enabling these stages
of the BI process, making it easier for organizations to turn data into actionable insights
and drive better business outcomes.
BI Roles and Responsibilities:
In a Business Intelligence (BI) team, various roles and responsibilities are assigned to
individuals with different skill sets and expertise to ensure the effective implementation
and operation of BI initiatives. The specific roles and responsibilities may vary depending
on the organization's size, complexity, and BI strategy. Here are some common BI roles
and their key responsibilities:
BI Manager / BI Director / BI Lead:
• Responsible for overseeing the entire BI team and BI initiatives.
• Develops and executes the BI strategy aligned with the organization's goals.
• Collaborates with business stakeholders to understand their requirements.
• Manages BI projects, resources, and budgets.
• Ensures data governance and compliance with data security policies.
BI Analyst / Business Analyst:
• Gathers and documents business requirements for BI solutions.
• Analyzes data and identifies trends, patterns, and insights to support decision-making.
• Creates and maintains reports, dashboards, and visualizations.
• Collaborates with IT and data teams to ensure data availability and accuracy.
• Provides ad-hoc data analysis to address specific business questions.
Page 13
MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
Data Engineer / ETL Developer:
• Designs, develops, and maintains data integration processes (ETL) to populate the
data warehouse or data mart.
• Ensures data from various sources is cleansed, transformed, and loaded into the BI
environment efficiently.
• Monitors and optimizes ETL workflows for performance and data quality.
• Collaborates with database administrators and data architects for data modeling.
Data Architect:
• Designs the overall data architecture for the BI solution, including data models and
database structures.
• Defines data standards, naming conventions, and data classification for consistency
and accuracy.
• Collaborates with business analysts and developers to ensure data models meet
business requirements.
• Ensures scalability and performance of the data infrastructure.
BI Developer / Report Developer:
• Develops and maintains BI reports, dashboards, and visualizations based on
business requirements.
• Implements data visualizations and interactive features to enhance user experience.
• Performs data validation and testing of BI solutions.
• Collaborates with business analysts and end-users to refine and improve reports.
Data Scientist:
• Applies advanced analytics and statistical techniques to analyze data and uncover
hidden insights.
• Builds predictive and prescriptive models to support data-driven decision-making.
• Conducts data mining and machine learning experiments.
• Collaborates with the BI team to integrate advanced analytics into BI solutions.
Page 14
MAHARISHI MARKANDESHWAR (DEEMED TO BE UNIVERSITY),
MULLANA (AMBALA)
Programme: MCA
Course: MCA-101: Business Intelligence & its applications
Data Quality Manager:
• Monitors and maintains data quality within the BI environment.
• Defines data quality metrics and establishes data quality rules.
• Implements data profiling and data cleansing processes.
• Collaborates with data stakeholders to resolve data quality issues.
BI Administrator:
• Manages the BI infrastructure, including servers, databases, and software
applications.
• Performs installation, configuration, and maintenance of BI tools.
• Monitors system performance and ensures uptime and availability.
• Manages user access and security permissions.
Business User / Stakeholder:
• Engages with the BI team to communicate business requirements and use BI
solutions effectively.
• Provides feedback on the usability and usefulness of BI reports and dashboards.
• Applies BI insights to make data-driven decisions for their respective areas.
In some organizations, individuals may hold multiple roles, especially in smaller BI teams.
Moreover, as BI is an iterative process, collaboration among team members and effective
communication with business stakeholders are essential for successful BI implementations
and deriving value from data analysis.
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