Business Analytics 1 Module 1.pdf

Copyright © 2023 Jayanti Rajdevendra Pande. All rights reserved.
- Jayanti R Pande
DGICM College, Nagpur
Sales
RASHTRASANT TUKDOJI MAHARAJ NAGPUR UNIVERSITY
MBA
SEMESTER: 3
SPECIALIZATION
BUSINESS ANALYTICS (BA 1)
SUBJECT
DATA VISUALIZATION FOR MANAGERS
MODULE NO : 1
Creating Visual Analytics with
Interactive Data Visualization
software Desktop

Q1. What is Data Visualization? What is process of Data Visualization?
DATA VISUALIZATION is the graphical representation of information and data. By using visual elements like charts, graphs, and
maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.
PROCESS OF DATA VISUALIZATION
1. Integrate Different Data Sets: Gather data from various sources and integrate them into a unified dataset. This step involves
cleaning, transforming, and combining data to create a coherent dataset for analysis.
2. Analyse: Perform data analysis on the integrated dataset. This step includes exploring the data, identifying patterns,
relationships, and trends, and using statistical or machine learning techniques to gain insights.
3. Visualize: Create visual representations of the analysed data. Use charts, graphs, and other visualization tools to present the
patterns and insights discovered during the analysis phase. Visualization makes it easier for stakeholders to understand complex
data and draw meaningful conclusions.
Integrate Different Data
Sets
Analyse Visualize

Q2. What are the shortcomings of Traditional Information Analysis.
TRADITIONAL INFORMATION ANALYSIS refers to the conventional methods and tools used by organizations to process, interpret,
and visualize their data. In the past, businesses relied on basic software like spreadsheets and limited Business Intelligence (BI)
tools to gain insights from their data. However, these traditional approaches come with several shortcomings that hinder effective
decision-making in today's fast-paced, data-driven business environment.
SHORTCOMINGS OF TRADITIONAL INFORMATION ANALYSIS
1. Limited Data Exploration: Traditional tools don’t provide in-depth exploration of data, hindering comprehensive decision-
making. For reliable decision-making, access to 100% of organizational data is vital, both internal and external. When these
tools can’t dive into data details and key metrics across the organization, timely decision-making becomes challenging.
2. Complex User Interface: Traditional tools are difficult to learn and navigate, requiring expertise for effective use. Special effort
and training are needed to develop visualizations, making them less user-friendly.
3. Delayed Reporting: Real-time insights are compromised due to slow processing, leading to delayed problem identification.
Traditional BI dashboards often have data latency, showing historical data rather than real-time status. Business users need
actionable insights based on the latest information, making minimization of data latency crucial.
4. Data Size Restrictions: Tools have limitations on data volume, preventing a complete understanding of business situations. For
instance, tools like Excel have constraints on the number of rows they can handle. In the era of big data, these limitations
hinder obtaining a comprehensive understanding of real-world occurrences.

Q3. Compare Traditional Information Analysis with modern information analysis?
Modern Information Analysis outperforms Traditional Information Analysis by leveraging advanced technologies, diverse data sources,
real-time processing, user-friendly interfaces, and robust security measures. These advancements empower organizations to gain deeper
insights, make faster decisions, and stay competitive in today's data-driven landscape.
1 Data Sources:
Traditional Information Analysis: Relies primarily on structured data from internal sources like databases and spreadsheets.
Modern Information Analysis: Utilizes a wide variety of data, including structured and unstructured data from internal and external
sources such as social media, IoT devices, and online platforms.
2 Data Volume and Variety:
Traditional Information Analysis: Deals with limited data volumes and focuses on structured data formats.
Modern Information Analysis: Handles massive volumes of both structured and unstructured data, offering insights from diverse sources
like text, images, and videos.
3 Analytical Tools:
Traditional Information Analysis: Relies on basic tools like spreadsheets and limited Business Intelligence (BI) software.
Modern Information Analysis: Utilizes advanced analytics tools, machine learning algorithms, and Artificial Intelligence (AI) to uncover
complex patterns and trends in data.
4 Data Governance and Security:
Traditional Information Analysis: Generally has basic data security measures and may lack comprehensive governance protocols.
Modern Information Analysis: Focuses heavily on data security, compliance, and governance, ensuring the privacy and integrity of data
throughout its lifecycle.
5 Decision-Making Agility:
Traditional Information Analysis: Decision-making is often sluggish due to delayed insights and limited data exploration.
Modern Information Analysis: Enables fast decision-making with real-time insights, allowing organizations to respond swiftly to market
changes and emerging opportunities.

Q4. Give Advantages of Modern information analysis.
MODERN INFORMATION ANALYSIS, powered by advanced technologies and cloud solutions, streamlines data processing and
enhances accessibility. It centralizes data management, ensuring rapid processing, and automated cleaning, transforming data
into actionable insights for informed decision-making.
ADVANTAGES OF MODERN INFORMATION ANALYSIS
1. Centralized Data Management: Modern information analysis allows you to collect, combine, and work with all your data in one
place. This eliminates the need to pull data from different systems, ensuring quick access to the required information. There's
no time wasted on data formatting or worrying about working with outdated information.
2. High-Speed Data Processing: Cloud-based data analytics solutions can handle vast amounts of data swiftly. These systems can
store and process millions of gigabytes, enabling complex queries within seconds. Furthermore, they automatically scale as
you add more data, ensuring you receive rapid responses without the need to switch to different systems for varied queries.
3. Automated Data Cleaning and Transformation: Modern analytics solutions offer automated tools to import, clean, and
transform data reliably. Raw data from sources isn't always in optimal condition. These tools help connect to the necessary
data sources, ensuring data integrity. They also aid in shaping the data correctly, making it easier to combine information
from different systems. This capability allows for a comprehensive understanding of interactions with customers or suppliers
and facilitates the analysis of the true impact of relationship changes.
4. Actionable Insights for Informed Decisions: A key advantage lies in providing actionable insights. Modern data analytics
solutions offer advanced visualization tools that surpass traditional reports and charts. They enable interactive and intuitive
ways to visualize data. Regardless of the volume of data being processed, these solutions ensure fast and responsive
reporting. This feature empowers timely decision-making based on accurate, easy-to-understand insights.

Q5. Why businesses should use Visual analysis or Business intelligence?
Visual analysis and Business Intelligence (BI) tools play pivotal roles in shaping modern business strategies. BI processes are integral
as they organize data, making it accessible and analyzable for decision-makers. Beyond enhancing decision-making, these tools offer
multifaceted advantages to businesses.
BENEFITS OF USING BI SYSTEMS
1. Comprehensive Data Insight: BI infrastructure collects and analyzes vast data, providing organizations with a clear and tailored view.
This insight empowers stakeholders to make data-driven decisions. The systems are highly customizable, intuitive, and self-service,
ensuring user-friendly experiences.
2. Enhanced Decision-Making: BI platforms process substantial data from multiple sources, facilitating in-depth analysis. Intuitive
dashboards and reports simplify complex data, enabling non-technical users to draw insights and tell compelling data-driven stories
without extensive coding knowledge.
4. Real-time Data-Driven Decisions: BI provides real-time data access, eliminating delays associated with traditional reporting methods.
With up-to-date information, leaders can make timely, informed decisions, steering the organization proactively.
5. Enhanced Customer Experience: BI directly influences customer satisfaction by pulling data from various sources, including customer
support interactions. Analyzing this data offers valuable insights, allowing businesses to refine their services and enhance overall
customer experience.
6. Increased Employee Satisfaction: BI systems empower employees with easy access to data analysis tools, reducing reliance on IT
support. This accessibility and scalability in data analysis enhance job satisfaction and productivity across departments.
7. Reliable and Centralized Data: BI systems ensure data integrity by centralizing information from diverse sources. This organized data
enables departments to access accurate, trusted information, eliminating silos and promoting seamless collaboration.
8. Competitive Advantage: BI equips businesses to stay competitive by providing insights into market trends, customer behaviors, and
industry changes. Anticipating market shifts and customer needs becomes possible, giving organizations a strategic edge.

Q6. Give an introduction to the data visualization software ecosystem.
Introduction to the Data Visualization Software Ecosystem
In the realm of Analytics and Business Intelligence (ABI), data visualization software serves as the cornerstone, enabling
organizations to interpret, explore, and communicate data-driven insights effectively. This ecosystem comprises a diverse range
of platforms, each offering unique capabilities tailored to meet the varied needs of businesses across sectors.
3 Magic Quadrant Visionaries: Visionaries, represented by
ThoughtSpot, Sisense, Oracle, SAP, TIBCO Software, SAS,
IBM, Yellowfin, and Tellius, present a robust vision for
modern ABI platforms. They excel in specialized areas,
showcasing high functionality and innovative thinking.
However, their ability to execute ABI solutions at scale and
consistently sometimes falls short. Despite this, their
innovative approaches mark them as influential figures in
the data visualization landscape.
4 Magic Quadrant Niche Players: Niche Players, such as
Amazon Web Services, Alibaba Cloud, Zoho, Pyramid
Analytics, MicroStrategy, and Incorta, carve a niche by
catering to specific sectors or use cases. These vendors offer
tailored solutions, often centered around particular cloud
stacks. While they excel in specific domains, questions arise
concerning their ability to compete with market leaders in
terms of innovation and overall performance.
1 Magic Quadrant Leaders: Leaders in the data visualization
software arena, such as Microsoft, Salesforce (Tableau), and
Qlik, possess advanced ABI capabilities. They exhibit a
profound understanding of essential ABI functionalities and
are committed to ensuring client success. These leaders not
only offer proven value but also demonstrate flexibility,
allowing incremental purchases and enterprise scalability.
Their platforms empower users to analyze, visualize, and
collaborate seamlessly, making them pivotal players in the
industry.
2 Magic Quadrant Challengers: Challengers, including Google
and Domo, are well-positioned to thrive in the ABI market.
They consistently deliver value to businesses with specific use
cases. However, challenges arise in terms of coordination
across their product portfolios and lag behind leaders in areas
like sales, marketing, industry-specific content, and innovation.
Despite these hurdles, they remain formidable contenders in
the competitive landscape.

In this dynamic ecosystem, businesses navigate through a plethora of choices, aiming to select the most suitable data
visualization software that aligns with their unique requirements. The landscape continues to evolve, with each player
striving to innovate, ensuring that organizations have the tools they need to transform data into actionable insights.

Q7. Give a detailed overview of the start page of Tableau.
TABLEAU is a user-friendly data visualization and business intelligence tool that connects to diverse data sources. Its intuitive
interface allows for interactive dashboards and in-depth data exploration. Offering advanced analytics, mobile compatibility, and
seamless integration with other applications, Tableau empowers businesses to gain actionable insights quickly and
collaboratively. With a supportive user community and robust training resources, Tableau facilitates efficient data-driven
decision-making.
Tableau's Start Page serves as the gateway to its powerful data visualization environment, acting as a control centre for users. Its
key components:
1 Connect Pane:
File Connections: Located on the top-left side, it provides options for connecting to various file types such as Microsoft Excel, Microsoft
Access (Windows only), statistical files, and text files.
Server Connections: Available in Tableau Desktop Professional, it offers connections to Tableau Server and other server-based data
sources.
Saved Data Sources: At the bottom, users can save frequently used data source connections for easy access in the future.
2 Open Pane:
Recent Workbooks: Displays the most recently opened workbooks, allowing users to easily access and continue their work. It cycles
through the last nine workbooks and also allows pinning frequently used workbooks.
Sample Workbooks: Found at the bottom, these are pre-created workbooks provided by Tableau for learning purposes, serving as
valuable resources for users new to the software.

START PAGE OF TABLEAU
3 Discover Pane:
Training Section: Offers access to online learning resources, tutorials, and guides provided by Tableau Software. This section assists
users in enhancing their skills and knowledge.
Viz of the Week: Showcases exemplary visualizations, providing inspiration and best practices for users to create impactful dashboards.
Resources: Contains additional helpful resources such as articles, case studies, and community forums, fostering a collaborative
learning environment among Tableau users.
By utilizing the Start Page's intuitive layout and features, users can seamlessly connect to data, access recent work, learn from samples,
and explore valuable educational resources, enhancing their proficiency in Tableau's data visualization and analysis capabilities.

Q8. How Visual Analytics improved Decision making?
1. Enhancing Data Comprehension: Transforming complex data into intuitive visual representations, facilitating quicker
understanding of patterns, trends, and outliers within datasets.
2. Real-Time Insights: Providing access to real-time data visualizations, enabling businesses to respond promptly to
market changes, customer behaviors, and emerging trends.
3. Interactive Exploration: Offering interactive features like drill-down capabilities, allowing decision-makers to explore
specific data points and conduct in-depth analyses, leading to comprehensive insights.
4. Predictive Analytics: Integrating machine learning algorithms, visual analytics predicts future trends and behaviors
based on historical data, enabling proactive decision-making and strategic planning.
5. Improved Risk Management: Identifying potential risks through visual patterns and trends, allowing organizations to
implement risk mitigation strategies promptly and make informed decisions to safeguard the business.

Q9. Write about Business Case for Visual Analysis
Business Case for Visual Analysis : Visual analysis is a powerful tool that can be used to gain insights from complex data sets. It is
especially useful in business analytics, where businesses need to be able to quickly and easily understand large amounts of data in
order to make informed decisions.
There are many benefits to using visual analysis in business analytics. For example, visual analysis can help businesses to:
• Identify trends and patterns: Visual analysis can help businesses to identify trends and patterns in their data that would
otherwise be difficult to see. For example, a business might use visual analysis to identify which products are selling well,
which customers are most likely to churn, or which marketing campaigns are most effective.
• Make better decisions: Visual analysis can help businesses to make better decisions by providing them with a clear and concise
understanding of their data. For example, a business might use visual analysis to decide which new products to launch, which
markets to expand into, or how to allocate their resources most effectively.
• Communicate insights more effectively: Visual analysis can help businesses to communicate their insights to others more
effectively than traditional text-based reports. For example, a business might use visual analysis to create dashboards or
presentations that can be used to share insights with stakeholders, customers, or the public.
Examples of Visual Analysis in Business Analytics
• A retail company might use visual analysis to identify which products are selling well and which are not. This information can
be used to make decisions about which products to stock, which products to promote, and which products to discontinue.
• A financial services company might use visual analysis to identify patterns in customer behavior. This information can be used
to develop targeted marketing campaigns, to identify potential fraud, and to improve customer service.
• A healthcare organization might use visual analysis to identify trends in patient health data. This information can be used to
develop new treatments, to improve patient care, and to reduce costs.

Copyright © 2023 Jayanti Rajdevendra Pande.
All rights reserved.
This content may be printed for personal use only. It may not be copied, distributed, or used for any other
purpose without the express written permission of the copyright owner.
This content is protected by copyright law. Any unauthorized use of the content may violate copyright laws
and other applicable laws.
For any further queries contact on email: jayantipande17@gmail.com

Business Analytics 1 Module 1.pdf

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Business Analytics 1 Module 1.pdf