Comprehensive Notes on Big Data Concepts and Applications Based on University Syllabus

Big Data
Unit 1
What is Big Data Analytics
Big data analysis uses advanced analytical methods that can extract important business
insights from bulk datasets. Within these datasets lies both structured (organized) and
unstructured (unorganized) data. Its applications cover different industries such as
healthcare, education, insurance, AI, retail, and manufacturing. By analyzing this data,
organizations get better insight on what is good and what is bad, so they can make the
necessary improvements, develop the production system, and increase profitability.
What is Big-Data Analytics?
Big data analytics is all about crunching massive amounts of information to uncover hidden
trends, patterns, and relationships. It’s like sifting through a giant mountain of data to find
the gold nuggets of insight.
Here’s a breakdown of what it involves:
• Collecting Data: Such data is coming from various sources such as social media, web
traffic, sensors and customer reviews.
• Cleaning the Data: Imagine having to assess a pile of rocks that included some gold
pieces in it. You would have to clean the dirt and the debris first. When data is being
cleaned, mistakes must be fixed, duplicates must be removed and the data must be
formatted properly.
• Analyzing the Data: It is here that the wizardry takes place. Data analysts employ
powerful tools and techniques to discover patterns and trends. It is the same thing as
looking for a specific pattern in all those rocks that you sorted through.
The multi-industrial utilization of big data analytics spans from healthcare to finance to
retail. Through their data, companies can make better decisions, become more efficient, and
get a competitive advantage.
How does big data analytics work?
Big Data Analytics is a powerful tool which helps to find the potential of large and complex
datasets. To get better understanding, let’s break it down into key steps:
• Data Collection: Data is the core of Big Data Analytics. It is the gathering of data from
different sources such as the customers’ comments, surveys, sensors, social media,
and so on. The primary aim of data collection is to compile as much accurate data as
possible. The more data, the more insights.
• Data Cleaning (Data Preprocessing): The next step is to process this information. It
often requires some cleaning. This entails the replacement of missing data, the
correction of inaccuracies, and the removal of duplicates. It is like sifting through a

treasure trove, separating the rocks and debris and leaving only the valuable gems
behind.
• Data Processing: After that we will be working on the data processing. This process
contains such important stages as writing, structuring, and formatting of data in a
way it will be usable for the analysis. It is like a chef who is gathering the ingredients
before cooking. Data processing turns the data into a format suited for analytics tools
to process.
• Data Analysis: Data analysis is being done by means of statistical, mathematical, and
machine learning methods to get out the most important findings from the
processed data. For example, it can uncover customer preferences, market trends, or
patterns in healthcare data.
• Data Visualization: Data analysis usually is presented in visual form, for illustration –
charts, graphs and interactive dashboards. The visualizations provided a way to
simplify the large amounts of data and allowed for decision makers to quickly detect
patterns and trends.
• Data Storage and Management: The stored and managed analyzed data is of utmost
importance. It is like digital scrapbooking. May be you would want to go back to
those lessons in the long run, therefore, how you store them has great importance.
Moreover, data protection and adherence to regulations are the key issues to be
addressed during this crucial stage.
• Continuous Learning and Improvement: Big data analytics is a continuous process of
collecting, cleaning, and analyzing data to uncover hidden insights. It helps
businesses make better decisions and gain a competitive edge.
Types of Big Data Analytics
Big Data Analytics comes in many different types, each serving a different purpose:
1. Descriptive Analytics: This type helps us understand past events. In social media, it
shows performance metrics, like the number of likes on a post.
2. Diagnostic Analytics: In Diagnostic analytics delves deeper to uncover the reasons
behind past events. In healthcare, it identifies the causes of high patient re-
admissions.
3. Predictive Analytics: Predictive analytics forecasts future events based on past data.
Weather forecasting, for example, predicts tomorrow’s weather by analyzing
historical patterns.
4. Prescriptive Analytics: However, this category not only predicts results but also offers
recommendations for action to achieve the best results. In e-commerce, it may
suggest the best price for a product to achieve the highest possible profit.
5. Real-time Analytics: The key function of real-time analytics is data processing in real
time. It swiftly allows traders to make decisions based on real-time market events.

6. Spatial Analytics: Spatial analytics is about the location data. In urban management,
it optimizes traffic flow from the data unde the sensors and cameras to minimize the
traffic jam.
7. Text Analytics: Text analytics delves into the unstructured data of text. In the hotel
business, it can use the guest reviews to enhance services and guest satisfaction.
These types of analytics serve different purposes, making data understandable and
actionable. Whether it’s for business, healthcare, or everyday life, Big Data
Analytics provides a range of tools to turn data into valuable insights, supporting better
decision-making.
Big Data Analytics Technologies and Tools
Big Data Analytics relies on various technologies and tools that might sound complex, let’s
simplify them:
• Hadoop: Imagine Hadoop as an enormous digital warehouse. It’s used by companies
like Amazon to store tons of data efficiently. For instance, when Amazon suggests
products you might like, it’s because Hadoop helps manage your shopping history.
• Spark: Think of Spark as the super-fast data chef. Netflix uses it to quickly analyze
what you watch and recommend your next binge-worthy show.
• NoSQL Databases: NoSQL databases, like MongoDB, are like digital filing cabinets
that Airbnb uses to store your booking details and user data. These databases are
famous because of their quick and flexible, so the platform can provide you with the
right information when you need it.
• Tableau: Tableau is like an artist that turns data into beautiful pictures. The World
Bank uses it to create interactive charts and graphs that help people understand
complex economic data.
• Python and R: Python and R are like magic tools for data scientists. They use these
languages to solve tricky problems. For example, Kaggle uses them to predict things
like house prices based on past data.
• Machine Learning Frameworks (e.g., TensorFlow): In Machine learning frameworks
are the tools who make predictions. Airbnb uses TensorFlow to predict which
properties are most likely to be booked in certain areas. It helps hosts make smart
decisions about pricing and availability.
These tools and technologies are the building blocks of Big Data Analytics and helps
organizations gather, process, understand, and visualize data, making it easier for them to
make decisions based on information.
Benefits of Big Data Analytics
Big Data Analytics offers a host of real-world advantages, and let’s understand with
examples:

1. Informed Decisions: Imagine a store like Walmart. Big Data Analytics helps them
make smart choices about what products to stock. This not only reduces waste but
also keeps customers happy and profits high.
2. Enhanced Customer Experiences: Think about Amazon. Big Data Analytics is what
makes those product suggestions so accurate. It’s like having a personal shopper who
knows your taste and helps you find what you want.
3. Fraud Detection: Credit card companies, like MasterCard, use Big Data Analytics to
catch and stop fraudulent transactions. It’s like having a guardian that watches over
your money and keeps it safe.
4. Optimized Logistics: FedEx, for example, uses Big Data Analytics to deliver your
packages faster and with less impact on the environment. It’s like taking the fastest
route to your destination while also being kind to the planet.
Challenges of Big data analytics
While Big Data Analytics offers incredible benefits, it also comes with its set of challenges:
• Data Overload: Consider Twitter, where approximately 6,000 tweets are posted
every second. The challenge is sifting through this avalanche of data to find valuable
insights.
• Data Quality: If the input data is inaccurate or incomplete, the insights generated by
Big Data Analytics can be flawed. For example, incorrect sensor readings could lead
to wrong conclusions in weather forecasting.
• Privacy Concerns: With the vast amount of personal data used, like in Facebook’s ad
targeting, there’s a fine line between providing personalized experiences and
infringing on privacy.
• Security Risks: With cyber threats increasing, safeguarding sensitive data becomes
crucial. For instance, banks use Big Data Analytics to detect fraudulent activities, but
they must also protect this information from breaches.
• Costs: Implementing and maintaining Big Data Analytics systems can be expensive.
Airlines like Delta use analytics to optimize flight schedules, but they need to ensure
that the benefits outweigh the costs.
Overcoming these challenges is essential to fully harness the power of Big Data Analytics.
Businesses and organizations must tread carefully, ensuring they make the most of the
insights while addressing these obstacles effectively.
Usage of Big Data Analytics
Big Data Analytics has a significant impact in various sectors:
• Healthcare: It aids in precise diagnoses and disease prediction, elevating patient
care.

• Retail: Amazon’s use of Big Data Analytics offers personalized product
recommendations based on your shopping history, creating a more tailored and
enjoyable shopping experience.
• Finance: Credit card companies such as Visa rely on Big Data Analytics to swiftly
identify and prevent fraudulent transactions, ensuring the safety of your financial
assets.
• Transportation: Companies like Uber use Big Data Analytics to optimize drivers’
routes and predict demand, reducing wait times and improving overall transportation
experiences.
• Agriculture: Farmers make informed decisions, boosting crop yields while conserving
resources.
• Manufacturing: Companies like General Electric (GE) use Big Data Analytics to predict
machinery maintenance needs, reducing downtime and enhancing operational
efficiency.
Best practise for Big data analysis
Big data analysis involves processing and examining large, diverse datasets to uncover
hidden patterns, correlations, and other insights. Here are some best practices for effective
big data analysis:
1. Define Clear Objectives:
o Establish the goals of your analysis and the questions you need to answer.
Clear objectives help in focusing your efforts and resources effectively.
2. Data Collection and Preparation:
o Data Quality: Ensure the data is clean, accurate, and complete. Remove
duplicates, handle missing values, and correct errors.
o Data Integration: Combine data from various sources to create a
comprehensive dataset.
o Data Transformation: Format the data appropriately for analysis, which may
involve normalization, aggregation, or encoding.
3. Choose the Right Tools and Technologies:
o Utilize big data tools and frameworks like Hadoop, Spark, and NoSQL
databases (e.g., MongoDB, Cassandra) that are designed to handle large-scale
data.
o Select data processing languages like Python, R, or Scala, depending on the
analysis needs and available expertise.
4. Scalability and Performance:

o Design your system to scale efficiently with the growth of data.
o Optimize data storage and retrieval for performance, using techniques like
partitioning, indexing, and caching.
5. Data Security and Privacy:
o Implement robust security measures to protect sensitive data.
o Ensure compliance with relevant data protection regulations (e.g., GDPR,
HIPAA).
6. Exploratory Data Analysis (EDA):
o Perform EDA to understand the dataset's main characteristics and identify
patterns or anomalies.
o Use visualization tools like Tableau, Power BI, or libraries like Matplotlib and
Seaborn in Python for better insights.
7. Advanced Analytics and Machine Learning:
o Apply statistical methods and machine learning algorithms to uncover deeper
insights.
o Use tools like TensorFlow, PyTorch, or Scikit-Learn for building and deploying
models.
8. Iterative Process:
o Treat big data analysis as an iterative process. Continuously refine your
methods and models based on feedback and new data.
9. Collaboration and Communication:
o Foster collaboration among data scientists, analysts, domain experts, and
stakeholders.
o Communicate findings effectively using reports, dashboards, and
visualizations.
10. Continuous Monitoring and Maintenance:
o Regularly monitor the performance of your data systems and models.
o Update and maintain your data pipelines, ensuring they adapt to changes in
data sources and business requirements.

Big Data Characteristics
Big Data contains a large amount of data that is not being processed by traditional data
storage or the processing unit. It is used by many multinational companies to process the
data and business of many organizations. The data flow would exceed 150 exabytes per day
before replication.
There are five v's of Big Data that explains the characteristics.
5 V's of Big Data
o Volume
o Veracity
o Variety
o Value
o Velocity
Volume
The name Big Data itself is related to an enormous size. Big Data is a vast 'volumes' of data
generated from many sources daily, such as business processes, machines, social media
platforms, networks, human interactions, and many more.
Facebook can generate approximately a billion messages, 4.5 billion times that the "Like"
button is recorded, and more than 350 million new posts are uploaded each day. Big data
technologies can handle large amounts of data.

Variety
Big Data can be structured, unstructured, and semi-structured that are being collected from
different sources. Data will only be collected from databases and sheets in the past, But
these days the data will comes in array forms, that are PDFs, Emails, audios, SM posts,
photos, videos, etc.
The data is categorized as below:
a. Structured data: In Structured schema, along with all the required columns. It is in a
tabular form. Structured Data is stored in the relational database management system.
b. Semi-structured: In Semi-structured, the schema is not appropriately defined,
e.g., JSON, XML, CSV, TSV, and email. OLTP (Online Transaction Processing) systems are
built to work with semi-structured data. It is stored in relations, i.e., tables.
c. Unstructured Data: All the unstructured files, log files, audio files, and image files
are included in the unstructured data. Some organizations have much data available, but
they did not know how to derive the value of data since the data is raw.
d. Quasi-structured Data:The data format contains textual data with inconsistent data
formats that are formatted with effort and time with some tools.
Example: Web server logs, i.e., the log file is created and maintained by some server that
contains a list of activities.
Veracity

Veracity means how much the data is reliable. It has many ways to filter or translate the
data. Veracity is the process of being able to handle and manage data efficiently. Big Data is
also essential in business development.
For example, Facebook posts with hashtags.
Value
Value is an essential characteristic of big data. It is not the data that we process or store. It
is valuable and reliable data that we store, process, and also analyze.
Velocity
Velocity plays an important role compared to others. Velocity creates the speed by which
the data is created in real-time. It contains the linking of incoming data sets speeds, rate of
change, and activity bursts. The primary aspect of Big Data is to provide demanding data
rapidly.
Big data velocity deals with the speed at the data flows from sources like application logs,
business processes, networks, and social media sites, sensors, mobile devices, etc.

Validating and promoting the value of big data
Validating and promoting the value of big data involves demonstrating how big data can
provide meaningful insights, drive business decisions, and create competitive advantages.
Here are key strategies for validating and promoting the value of big data:
1. Define Clear Objectives and Use Cases
• Business Goals: Align big data initiatives with specific business objectives to ensure
they address real needs and problems.
• Use Cases: Identify and prioritize high-impact use cases that can showcase the
benefits of big data, such as customer insights, operational efficiency, predictive
maintenance, and fraud detection.
2. Quantify the Benefits
• ROI Calculation: Measure the return on investment (ROI) by comparing the costs of
big data initiatives with the benefits gained, such as increased revenue, cost savings,
and improved customer satisfaction.
• KPIs: Establish key performance indicators (KPIs) to track the impact of big data
projects, such as reduced downtime, higher sales, or enhanced customer retention.
3. Leverage Advanced Analytics
• Predictive Analytics: Use machine learning and predictive analytics to forecast trends
and behaviors, enabling proactive decision-making.
• Real-Time Analytics: Implement real-time data processing to respond promptly to
emerging opportunities and threats.
4. Showcase Success Stories
• Case Studies: Develop detailed case studies that highlight successful big data
projects, emphasizing the problems solved, methods used, and results achieved.
• Testimonials: Collect testimonials from stakeholders and customers who have
benefited from big data initiatives.
5. Invest in Quality and Governance
• Data Quality: Ensure high data quality by implementing robust data cleaning,
validation, and enrichment processes.
• Data Governance: Establish strong data governance frameworks to maintain data
integrity, security, and compliance.
6. Build Scalable and Flexible Infrastructure
• Scalability: Invest in scalable big data technologies and infrastructure that can grow
with your data needs, such as cloud computing, Hadoop, and Apache Spark.

• Flexibility: Use flexible data architectures that can handle various data types and
sources, ensuring adaptability to changing business requirements.
7. Foster a Data-Driven Culture
• Training and Education: Provide training and resources to employees to enhance
their data literacy and encourage data-driven decision-making.
• Collaboration: Promote collaboration between data scientists, IT professionals, and
business stakeholders to maximize the value of big data insights.
8. Communicate Value Effectively
• Visualization Tools: Use data visualization tools like Tableau, Power BI, or D3.js to
create compelling visual representations of data insights that are easy to understand
and act upon.
• Regular Reporting: Develop regular reports and dashboards to keep stakeholders
informed about the progress and impact of big data projects.
9. Leverage External Expertise
• Consulting Services: Engage with big data consulting firms or experts to gain insights
and best practices for maximizing the value of your data.
• Partnerships: Form partnerships with technology providers and research institutions
to stay updated with the latest advancements and innovations in big data.
10. Continuous Improvement
• Feedback Loop: Establish a feedback loop to continuously learn from past projects
and improve future big data initiatives.
• Innovation: Stay abreast of emerging technologies and methodologies to keep your
big data strategy innovative and effective.
Applications & Uses of Big Data
Evolving technology has extended the potential of using big data in every type of industry.
Organizations of all sizes in various industries are using big data insights to make good
strategic and operational decisions. Here are the top 5 domains where big data is used:
Banking, Financial Services, and Insurance (BFSI)
BFSI is one of the most data-intensive domains in the world economy. Financial institutions
have huge amounts of customer data, such as information on customer profile data
collected for KYC, deposits and withdrawals at ATMs, online payments, and more. Big data
technologies enable financial institutions to easily access data and eliminate redundancy and
overlapping.

The BFSI industry uses big data to efficiently use these rich data sets and become more
customer-centric and profitable. Banking and finance institutions leverage big data
technologies data sets to maximize customer understanding and gain a competitive
advantage. Traders also use this technology for sentiment measurement and high-frequency
trading.
Big Data use cases in the BFSI industry
• Improved levels of customer insight
• Customer engagement
• Fraud detection and prevention
• Market trading analysis
• Risk management
• New data-driven products and services
Retail
The retail industry collects a huge amount of data through RFID, customer loyalty programs,
and more. Big data analytics enable businesses to uncover patterns and trends in a large
volume of data to improve pricing, supply chain movement, personalized shopping
experiences, and enhance customer loyalty. Businesses also use retail analytics data to
forecast trends and make strategic decisions. This helps increase their competitiveness in the
market to a great extent.
Big Data use cases in the Retail industry
• Personalized customer experience
• Predicting demands
• Dynamic pricing
• Customer journey analytics
• Fraud detection and prevention
Healthcare
Healthcare institutions gather a large amount of data in the form of patient details,
physician’s prescriptions, medical imaging, lab reports, insurance, and other administrative
data. Using big data, the vast amount of data can be stored systematically and easily
accessed when needed.
Many healthcare institutions are using electronic health records (EHR) to gain a deeper
understanding of patient disease patterns. Using big data, healthcare practitioners can

access a wide range of data and make informed decisions related to the patient’s health,
hospital performance, and more.
Big Data use cases in the Healthcare industry
• Improved patient predictions
• Real-time alerts
• Electronic Health Records (EHRs)
• Better patient engagement
• Fraud prevention and detection
• Smoother hospital administration
Education
In the education sector, a lot of data is collected in the form of names of students enrolled in
a program/course, enrollment year, course details, student ID, marks obtained in each
subject, and more. Using big data, educators can store this information efficiently and
identify patterns and trends to spot opportunities for positive change in the performance of
both the students and the educational institutions.
Big data analytics help educators reveal trends in students’ behavior and their preferences to
create customized programs. It also gives a base to evaluate the state of the entire education
system.
Big Data use cases in the Education industry
• Create customized programs
• Improve student’s results
• Reduce dropouts
• Identify leaner’s strengths
• Data-driven decision making
Manufacturing
Regardless of what type of data a business has, it plays an important role when it comes to
outperforming the competition. In manufacturing, data is gathered from machines, devices,
and operators at every stage of production. Big data help manufacturers store this data
efficiently. The use of big data also allows firms to identify new ways to save costs and
improve product quality. Using big data analytics, companies can find patterns to solve
existing problems and improve the overall process.

Big Data use cases in the Manufacturing industry
• Customize product design
• Predictive quality
• Anomaly detection
• Better management of supply chain
• Production forecasting
• Yield improvement
• Risk evaluation
Big data applications exhibit several key characteristics that enable them to
effectively manage and derive insights from large, diverse datasets. Here are the primary
characteristics of big data applications:
1. Scalability
• Horizontal Scaling: Ability to scale out by adding more nodes to a distributed system
to handle increased data volume and processing demands.
• Vertical Scaling: Ability to scale up by adding more resources (CPU, memory, storage)
to existing nodes.
2. Distributed Processing
• Parallel Computing: Use of distributed computing frameworks (e.g., Hadoop, Spark)
to process large datasets in parallel across multiple nodes.
• Fault Tolerance: Ability to continue processing data even when some nodes fail,
ensuring high availability and reliability.
3. Data Variety Handling
• Multi-Format Support: Capability to ingest and process structured, semi-structured,
and unstructured data from various sources (e.g., databases, logs, social media, IoT
devices).
• Schema Flexibility: Ability to handle evolving data schemas without requiring
extensive reconfiguration.
4. High Velocity Processing
• Real-Time Analytics: Processing and analyzing data in real-time or near-real-time to
support immediate decision-making (e.g., streaming data from sensors, financial
transactions).
• Batch Processing: Efficient handling of large volumes of data processed in batches at
scheduled intervals.

5. Data Storage and Management
• Distributed Storage: Use of distributed file systems (e.g., HDFS) and NoSQL
databases (e.g., Cassandra, MongoDB) to store large datasets across multiple nodes.
• Data Redundancy: Replication of data across different nodes to ensure data
availability and durability.
6. Advanced Analytics
• Machine Learning: Integration of machine learning algorithms to uncover patterns,
predict outcomes, and automate decision-making processes.
• Data Mining: Extraction of valuable information and patterns from large datasets
using statistical and computational techniques.
7. Data Security and Privacy
• Access Control: Implementation of robust access control mechanisms to protect
sensitive data.
• Encryption: Use of encryption techniques to secure data at rest and in transit.
• Compliance: Adherence to regulatory requirements and data protection laws (e.g.,
GDPR, HIPAA).
8. User-Friendly Interfaces
• Visualization Tools: Provision of intuitive data visualization tools (e.g., Tableau,
Power BI) to help users understand and interpret data insights.
• Interactive Dashboards: Creation of interactive dashboards that provide real-time
updates and facilitate data exploration.
9. Interoperability
• APIs and Integration: Support for APIs and integration with various data sources,
tools, and platforms to enable seamless data flow and interoperability.
• Data Exchange Standards: Adherence to data exchange standards and protocols
(e.g., JSON, XML, RESTful APIs).
10. Continuous Monitoring and Maintenance
• Performance Monitoring: Continuous monitoring of system performance and
resource utilization to ensure optimal operation.
• Automated Maintenance: Use of automated tools and scripts for system
maintenance tasks such as data backups, updates, and scaling.
11. Cost Efficiency
• Resource Optimization: Efficient use of computing resources to minimize costs while
maximizing performance.

• Cloud Computing: Leveraging cloud-based services (e.g., AWS, Google Cloud, Azure)
to provide scalable and cost-effective big data solutions.
12. Complex Event Processing
• Event Correlation: Ability to process and analyze complex sequences of events to
detect patterns, anomalies, or trends in real-time.
• Rule-Based Systems: Use of rule-based systems to trigger actions based on specific
conditions or events detected in the data stream.
The perception and quantification of the value of big data involve understanding its potential
benefits and measuring its tangible impact. This process ensures that investments in big data
technologies and projects are justified and align with business goals.
Perception of Value
1. Strategic Alignment:
o Big data initiatives should support the organization's strategic goals, such as
enhancing customer experience, driving innovation, or improving operational
efficiency.
2. Stakeholder Engagement:
o Involve key stakeholders early to understand their needs and demonstrate
how big data can address specific challenges and opportunities.
3. Success Stories and Case Studies:
o Highlight successful big data projects within the organization or industry to
showcase potential benefits and build confidence in new initiatives.
4. Data-Driven Culture:
o Promote a culture where decisions are based on data insights rather than
intuition, emphasizing the importance and value of data-driven decision-
making.
5. Clear Communication and Visualization:
o Use data visualization tools to present insights in an understandable and
compelling way, making it easier for stakeholders to grasp the value of big
data.
Quantification of Value
1. Return on Investment (ROI):
o Increased Revenue: Measure the additional revenue generated through
improved customer insights, targeted marketing, or new product offerings.

o Cost Savings: Calculate the savings achieved through operational efficiencies,
reduced waste, or optimized resource utilization.
o Implementation Costs: Account for the costs of data collection, storage,
processing, and analysis.
2. Key Performance Indicators (KPIs):
o Define and monitor KPIs that reflect the impact of big data projects, such as
customer satisfaction, operational efficiency, or sales growth.
3. Time Savings:
o Quantify the time saved in decision-making and operations through
automation and real-time analytics, translating this into cost savings and
productivity gains.
4. Risk Reduction:
o Measure the reduction in risks achieved through predictive analytics, such as
fraud detection, preventive maintenance, and supply chain optimization.
5. Customer Metrics:
o Customer Retention: Track improvements in customer retention rates from
personalized and targeted interactions.
o Customer Acquisition: Measure the effectiveness of data-driven marketing
campaigns in acquiring new customers.
6. Operational Efficiency:
o Process Optimization: Assess improvements in process efficiency, such as
reduced downtime, streamlined workflows, and better resource
management.
o Supply Chain Efficiency: Measure enhancements in supply chain operations,
including inventory management, logistics, and supplier performance.
7. Innovation and Competitive Advantage:
o Evaluate the role of big data in driving innovation, such as developing new
products, services, or business models.
o Measure the competitive advantage gained through data-driven strategies
and insights.
8. Compliance and Risk Management:
o Quantify the benefits of improved compliance with regulations and reduced
risks associated with data breaches or non-compliance.
Example Calculation of ROI

Scenario: A retail company implementing a big data solution to optimize inventory
management.
1. Revenue Increase:
o Improved demand forecasting leads to a $2 million annual increase in sales.
2. Cost Savings:
o Reduces excess inventory holding costs by $500,000 annually.
o Decreases losses from perishable goods by $200,000 annually.
3. Implementation Costs:
o Initial setup and integration costs: $1 million.
o Annual operating costs (maintenance, personnel, software licenses):
$300,000.
4. ROI Calculation:
o Total Benefits: $2 million (revenue increase) + $500,000 (cost savings) +
$200,000 (loss reduction) = $2.7 million.
o Total Costs: $1 million (setup) + $300,000 (annual operating costs) = $1.3
million.
o ROI = (Total Benefits - Total Costs) / Total Costs = ($2.7 million - $1.3 million) /
$1.3 million ≈ 107.7%.
By systematically perceiving and quantifying the value of big data, organizations can make
informed decisions about their data strategies and investments, clearly demonstrating the
tangible benefits and returns.
What is big data?
Big data refers to extremely large data sets, either structured or not, that professionals
analyze to discover trends, patterns, or behaviors. It's unique in that it has what
professionals describe as the three Vs—volume, velocity, and variety—in such large
amounts that traditional data management systems struggle to store or analyze the data
successfully. Therefore, scalable architecture must be available to manage, store, and
analyze big data sets.

What is big data storage?
Big data storage is a scalable architecture that allows businesses to collect, manage, and
analyze immense sets of data in real-time. The design of big data storage solutions is
specifically tailored to address the speed, volume, and complexity of the data sets. Some
examples of big data storage options are:
• Data lakes are centralized storage solutions that process and secure data in its native
format without size limitations. They can enable different forms of smart analytics,
such as machine learning and visualizations.
• Data warehouses aggregate data sets from different sources into a single storage
unit for robust analysis, supporting data mining, artificial intelligence (AI), and more.
Unlike a data lake, data warehouses have a three-tier structure for storing data.
• Data pipelines gather raw data and transport it into repositories, such as lakes or
warehouses.
Data lakes, warehouses, and pipelines exist within several different storage options,
including:
• Cloud-based storage system is where a business outsources the storage of its data to
a vendor that operates a cloud storage system.
• Colocation storage is the process of a business renting space to store its servers
rather than having it on-site.
• On-premise storage is where a business manages its network and servers on-site.
This can include hardware, such as servers, that houses the data at an organization’s
premises.
Read more: Data Lake vs. Data Warehouse: What’s the Difference?
What is big data storage used for?
The primary purpose of big data storage is to successfully store immense amounts of data
for future analysis and use. Big data is crucial for businesses and organizations, from health
care research to retailers and security, to make more efficient, informed, and effective
decisions. Without big data storage, businesses wouldn’t have the time, money, or
technology to store and manage big data sets successfully.
Because big data is valuable for processing and understanding patterns and trends, it needs
correct storage. Big data storage makes applying big data to business decisions possible.
How does big data storage work?
Big data storage employs a system of commodity servers and high-capacity disks capable of
analyzing the data sets. For example, in a cloud storage scenario, the big data sets exist in a
server hosted in an off-site location that can be accessed through the internet. Virtual
machines provide the space for the data to live safely, and it’s possible to quickly create
more virtual machines when the amount of data grows past the servers’ current capacity.

Who uses big data storage?
Professionals across multiple industries use big data storage to store, manage, and analyze
their data. These industries include health care, finance, government, education, and retail.
These industries benefit from big data storage because it provides a unique opportunity to
analyze data at a large scale, offering insights that wouldn’t be possible otherwise, such as
predictions for the future and customer behavior analysis.
Pros and cons of using big data storage
The pros and cons of big data storage typically relate to the volume of data being
handled. Here are some advantages of using big data storage:
• Data-driven. The large-scale data analysis allows businesses to become data-driven
using concrete data to help make decisions and better inform strategic planning.
• Make safe and informed decisions. Big data storage keeps data safe and lets
professionals apply analytical tools to the data sets, resulting in more informed
decision-making, better customer service, more flexibility in strategic planning, and
increased efficiency in operations.
• Flexible. Cloud-based storage is flexible and allows businesses to scale their needed
servers up or down without up-front investment.
On the other hand, here are some factors to consider when using big data storage:
• Costly. It’s expensive for a business to purchase the necessary space to store big data
sets, and the cost will only increase as more data becomes available. For example, if
a business opts to manage its servers on-site, it may face the risk of needing to
purchase more systems and the staff to run them.
How to get started in big data storage
If you’re interested in pursuing a career that involves big data storage, the first step is to
research a category known as “big data jobs.” These jobs all include working with big data,
and you might find one that matches your specific skills and interests. Big data jobs often
involve managing and creating solutions for data storage and thinking up strategies to use
data to drive profits.
For example, a big data and AI engineer is responsible for designing, building, and
maintaining big data storage and other architecture to make big data sets available for
analysis. They create architectural solutions for storing big data, and they often work with
other professionals, such as data scientists. To become a big data and AI engineer, you’ll
need a four-year degree in math, computer science, or information technology. Some big
data engineers also gain certification to become more competitive. The average annual
salary for a big data engineer in the US is $126,110

Comprehensive Notes on Big Data Concepts and Applications Based on University Syllabus

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