1.1. Evolution-and-Scope-of-Business-Analytics

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Evolution Scope of Business Analytics
Business analytics has come a long way, from
simple data analysis to sophisticated predictive
modeling and AI-powered insights. This
presentation explores the historical evolution of
business analytics, its current scope, and the
emerging trends shaping its future.
by Jitendra Tomar
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From Data to Insights The History of Business
Analytics
Early forms of business analytics emerged in the
19th century with the development of statistical
methods. Businesses began using data to track
sales and inventory.
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The introduction of computers in the mid-20th
century revolutionized data analysis, enabling
businesses to process and analyze vast quantities
of data.
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The rise of the internet and e-commerce in the
late 20th century led to an explosion of data,
driving the need for more sophisticated analytics
tools.
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Data Collection and Storage The Foundation of
Analytics
Businesses collect data from a wide range of
sources, including customer transactions, website
interactions, and social media activity.
Data warehousing and cloud computing have enabled
businesses to store and manage massive datasets,
facilitating comprehensive analysis.
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Descriptive Analytics Turning Data into
Meaningful Information
Sales Analysis
Customer Segmentation
Market Research
Descriptive analytics helps businesses understand
past performance, identify trends, and gain
insights from historical data.
Descriptive analytics helps businesses understand
market trends, identify competitors, and evaluate
market opportunities.
Businesses can use descriptive analytics to group
customers based on shared characteristics,
enabling targeted marketing campaigns.
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Predictive Analytics Forecasting Future Trends
and Behaviors
Predictive Modeling
Using historical data to build models that
predict future outcomes, like sales forecasting,
customer churn prediction, and fraud detection.
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Machine Learning Algorithms
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Leveraging algorithms to learn from data and make
predictions, enabling businesses to anticipate
future trends and behaviors.
Data Mining Techniques
Discovering patterns and insights from large
datasets, helping businesses identify
opportunities and mitigate risks.
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Prescriptive Analytics Optimizing Decision-Making
Optimization Algorithms
Algorithms designed to find the best solution to
a problem, helping businesses make optimal
decisions in areas like inventory management and
pricing.
Simulation Modeling
Creating virtual representations of real-world
systems to test different scenarios and make
informed decisions.
Decision Support Systems
Tools that provide businesses with data-driven
recommendations to support decision-making in
areas like marketing, finance, and operations.
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Big Data and Business Analytics Unlocking the
Power of Large Datasets
100
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Petabytes
V's
The vast amount of data generated by modern
businesses, social media, and online activity.
The defining characteristics of big data volume,
velocity, variety, veracity, and value.
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Artificial Intelligence and Machine Learning in
Business Analytics
Automation
Predictive Power
AI and ML automate tasks like data analysis,
reporting, and customer service, freeing up human
resources for more strategic work.
AI and ML algorithms can analyze vast datasets to
make accurate predictions, driving more effective
marketing campaigns and personalized customer
experiences.
Insights and Recommendations
AI-powered tools can identify patterns and trends
in data, providing businesses with valuable
insights and actionable recommendations.
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Ethics and Privacy in Business Analytics
Transparency
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Businesses must be transparent about how they
collect, use, and share customer data.
Fairness
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Analytics models and algorithms should be fair
and unbiased, avoiding discrimination against
individuals or groups.
Privacy Protection
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Businesses have a responsibility to protect
customer data from unauthorized access and misuse.
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The Future of Business Analytics Emerging Trends
and Innovations
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Thats all folks

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