Business Analytics Importance of Business Analytics Evolution of Business Analytics History of Business Analytics Classification of Business Analytics Application of Business Analytics

1. Dept. of MBA, Sanjivani COE, Kopargaon
402 :Current Trends in Management
Unit No 4
BUSINESS ANALYTICS
Presented By:
Dr. S.P. Ghodake
(Asst. Prof. Department of MBA)
1
Sanjivani College of Engineering,
Kopargaon
www.sanjivanimba.org.in

2. Dept. of MBA, Sanjivani COE, Kopargaon
Content
• Business Analytics
• Importance of Business Analytics
• Evolution of Business Analytics
• History of Business Analytics
• Classification of Business Analytics
• Application of Business Analytics

3. Dept. of MBA, Sanjivani COE, Kopargaon
Business Analytics
• Business analytics is the
process of transforming data
into insights to improve
business decisions. Data
management, data visualization,
predictive modeling, data
mining, forecasting simulation,
and optimization are some of
the tools used to create insights
from data.

4. Dept. of MBA, Sanjivani COE, Kopargaon
Business Analytics
• Business analytics refers to the practice of using data and statistical methods to gain insights
and make informed decisions in a business context.
• It involves collecting, organizing, analyzing, and interpreting data to identify patterns, trends,
and relationships that can provide valuable insights for business strategy, operations, and
decision-making.
• Business analytics combines various techniques from data mining, statistical analysis,
predictive modeling, and data visualization to uncover meaningful information from large and
complex data sets.
• It helps organizations understand their past performance, predict future outcomes, optimize
processes, and make data-driven decisions to improve efficiency, profitability, and
competitive advantage.

5. Dept. of MBA, Sanjivani COE, Kopargaon
Key components of business analytics
• Data collection and integration: Gathering relevant data from various sources, such as
databases, spreadsheets, and external data providers, and integrating it into a single repository.
• Data exploration and cleaning: Exploring the data to understand its structure, identifying
missing or erroneous data, and cleaning and transforming the data to ensure its quality and
consistency.
• Data analysis: Applying statistical techniques, data mining algorithms, and machine learning
models to analyze the data and uncover patterns, correlations, and insights.
• Data visualization: Presenting the analyzed data in a visual and interactive format, such as
charts, graphs, and dashboards, to communicate the findings effectively and aid decision-
making.
• Predictive modeling: Building mathematical and statistical models that can predict future
outcomes based on historical data and variables.
• Decision optimization: Using analytical models and algorithms to optimize business
processes, allocate resources efficiently, and make data-driven decisions.

6. Dept. of MBA, Sanjivani COE, Kopargaon
Importance of Business Analytics
• Business Analytics may be defined as refining past or present business data using
modern technologies. They are used to build sophisticated models for driving future
growth. A general Business Analytics process may include Data Collection, Data
Mining, Sequence Identification, Text Mining, Forecasting, Predictive Analytics,
Optimization, and Data Visualization.
• Every business today produces a considerable amount of data in a specific way.
Business Analytics now are leveraging the benefits of statistical methods and
technologies to analyze their past data. This is used to uncover new insights to help
them make a strategic decision for the future.
• Business Intelligence, a subset of the Business Analytics field, plays an essential
role in utilizing various tools and techniques such as machine learning and artificial
intelligence technologies to predict and implement insights into daily operations.

7. Dept. of MBA, Sanjivani COE, Kopargaon
• Business analytics can transform raw data into more valuable inputs to leverage this
information in decision making.
• With Business Analytics tools, we can have a more profound understanding of
primary and secondary data emerging from their activities. This helps businesses
refine their procedures further and be more productive.
• To stay competitive, companies need to be ahead of their peers and have all the
latest toolsets to assist their decision making in improving efficiency as well as
generating more profits.
• Thus, Business Analytics brings together fields of business management, and
computing to get actionable insights. These values and inputs are then used to
remodel business procedures to generate more efficiency and build a productive
system.

8. Dept. of MBA, Sanjivani COE, Kopargaon
The Benefits of Business Analytics
• Business Analytics brings actionable insights for businesses. However, here
are the main benefits of Business Analytics:
1. Improve operational efficiency through their daily activities.
2. Assist businesses to understand their customers more precisely.
3. Business uses data visualization to offer projections for future
outcomes.
4. These insights help in decision making and planning for the future.
5. Business analytics measures performance and drives growth.
6. Discover hidden trends, generate leads, and scale business in the right
direction

9. Dept. of MBA, Sanjivani COE, Kopargaon
Evolution of Business Analytics
• Technologies have been used as a measure to improve business efficiency
since the beginning. Automation has played a considerable role in
managing and performing multiple tasks for large organizations. The
unprecedented rise of the internet and information technology has further
boosted the performance of businesses.
• With advancement today, we have Business Analytics tools that utilize
past and present data to give businesses the right direction for their future.

10. Dept. of MBA, Sanjivani COE, Kopargaon
Types of business analytics techniques.
Business analytics techniques can be segmented in the following four ways:
• Descriptive Analytics: This technique describes the past or present
situation of the organization's activities.
• Diagnostic Analytics: This technique discovers factors or reasons for past
or current performance.
• Predictive Analytics: This technique predicts figures and results using a
combination of business analytics tools.
• Prescriptive Analytics: This technique recommends specific solutions for
businesses to drive their growth forward.

11. Dept. of MBA, Sanjivani COE, Kopargaon
History of Business Analytics
1. Early Beginnings (1950s-1970s)
2. Rise of Business Intelligence (1980s-1990s)
3. Advent of Data Mining and Predictive Analytics (1990s-2000s)
4. Evolution into Business Analytics (2000s-Present)
5. Integration of AI and Machine Learning (2010s-Present)

12. Dept. of MBA, Sanjivani COE, Kopargaon
• In the 1950s and 1960s, computers began to gain prominence
in businesses, allowing for the collection and storage of large
amounts of data.
• The focus during this period was primarily on transaction
processing and data management rather than data analysis.
• Decision support systems (DSS) started to emerge, which
aimed to provide managers with analytical tools to aid
decision-making.
Early Beginnings (1950s-1970s)

13. Dept. of MBA, Sanjivani COE, Kopargaon
• In the 1980s, business intelligence (BI) began to gain traction. BI focused
on extracting insights from data to support strategic decision-making.
• Technologies like data warehouses and online analytical processing
(OLAP) facilitated the aggregation and analysis of data from various
sources.
• Decision support systems evolved into executive information systems (EIS)
and management information systems (MIS).
Rise of Business Intelligence (1980s-1990s)

14. Dept. of MBA, Sanjivani COE, Kopargaon
• With the exponential growth of data, the 1990s saw the emergence of data
mining and predictive analytics.
• Data mining techniques enabled organizations to uncover patterns,
relationships, and insights hidden within large datasets.
• Statistical modeling, machine learning algorithms, and other analytical
methods were applied to make predictions and drive proactive decision-
making.
Advent of Data Mining and Predictive Analytics (1990s-2000s)

15. Dept. of MBA, Sanjivani COE, Kopargaon
• As businesses recognized the value of data-driven decision-making, the
field expanded beyond traditional BI and data mining.
• Business analytics encompassed a broader range of techniques, including
descriptive, diagnostic, predictive, and prescriptive analytics.
• The proliferation of advanced analytics tools, such as big data platforms,
data visualization software, and machine learning frameworks, further
accelerated the adoption of business analytics.
• Business analytics became integral to various domains, including
marketing, finance, supply chain management, human resources, and
operations.
Evolution into Business Analytics (2000s-Present):

16. Dept. of MBA, Sanjivani COE, Kopargaon
• With the advancements in artificial intelligence (AI) and machine learning
(ML), business analytics has witnessed further transformation.
• AI and ML techniques are now utilized to automate data analysis, uncover
complex patterns, and generate actionable insights.
• Predictive and prescriptive analytics have become more sophisticated,
enabling organizations to optimize processes, detect anomalies, and make
data-driven decisions in real-time.
Integration of AI and Machine Learning (2010s-Present)

17. Dept. of MBA, Sanjivani COE, Kopargaon
Classification of Business Analytics
• Business analytics can be classified into various categories based on different
criteria.
1. Descriptive Analytics:
2. Diagnostic Analytics:
3. Predictive Analytics:
4. Prescriptive Analytics:
5. Text Analytics:
6. Social Media Analytics:

18. Dept. of MBA, Sanjivani COE, Kopargaon
1. Descriptive Analytics
• Descriptive analytics involves analyzing historical data to understand past
performance and gain insights into what has happened.
• It focuses on summarizing and reporting data to answer questions like "What
happened?" and "How did it happen?"
• Descriptive analytics techniques include data visualization, dashboards, reporting,
and basic statistical analysis.
• Tableau: Tableau is a data visualization and business intelligence platform that
helps companies create interactive dashboards and reports to gain descriptive
insights from their data. It enables users to analyze and present data in a visually
appealing and intuitive manner.

19. Dept. of MBA, Sanjivani COE, Kopargaon
2. Diagnostic Analytics
• Diagnostic analytics goes a step further than descriptive analytics and aims to
understand why something happened.
• It involves analyzing data to identify patterns, relationships, and causal factors
behind past events. Diagnostic analytics answers questions like "Why did it
happen?" and "What were the key drivers?"
• Techniques used in diagnostic analytics include data mining, root cause analysis,
correlation analysis, and hypothesis testing.
• IBM Watson Analytics: IBM Watson Analytics is an advanced analytics platform
that combines data exploration, data mining, and predictive analytics capabilities. It
helps users uncover patterns, relationships, and root causes behind business events,
enabling diagnostic analysis and informed decision-making.

20. Dept. of MBA, Sanjivani COE, Kopargaon
3. Predictive Analytics
• Predictive analytics uses historical data to make predictions and forecasts about
future events or outcomes.
• It involves applying statistical models, machine learning algorithms, and data
mining techniques to identify patterns and trends in data.
• Predictive analytics helps answer questions like "What is likely to happen?" and
"What will be the outcome if we take a particular action?"
• Examples of predictive analytics include regression analysis, time series
forecasting, and predictive modeling.
• Amazon: Amazon uses predictive analytics extensively for its recommendation
engine. By analyzing customer browsing and purchase history, as well as data from
similar users, Amazon predicts and suggests products that customers are likely to be
interested in, increasing sales and customer satisfaction.

21. Dept. of MBA, Sanjivani COE, Kopargaon
4. Prescriptive Analytics
• Prescriptive analytics takes predictive analytics a step further by suggesting optimal
actions or decisions based on the predicted outcomes.
• It involves using optimization techniques, simulation models, and decision analysis
to recommend the best course of action.
• Prescriptive analytics helps answer questions like "What should we do?" and "What
action will lead to the best outcome?"
• Applications of prescriptive analytics include resource allocation, supply chain
optimization, and decision support systems.
• UPS: United Parcel Service (UPS) uses prescriptive analytics to optimize its
package delivery routes. By analyzing factors like package weight, destination,
traffic conditions, and delivery deadlines, UPS algorithms determine the most
efficient delivery routes for drivers, minimizing fuel consumption and improving
delivery times.

22. Dept. of MBA, Sanjivani COE, Kopargaon
5. Text Analytics
• Text analytics involves extracting useful information and insights from unstructured
text data, such as customer reviews, social media posts, emails, and documents. It
includes techniques like natural language processing (NLP), sentiment analysis,
topic modeling, and text mining.
• Text analytics can be used for tasks like sentiment analysis, document
classification, and customer feedback analysis.
• Twitter: Twitter employs text analytics to analyze tweets and user sentiments. By
using natural language processing and sentiment analysis techniques, Twitter can
understand the sentiment and emotions expressed in tweets, which helps in
monitoring public opinion, identifying trends, and improving user experience.

23. Dept. of MBA, Sanjivani COE, Kopargaon
6. Social Media Analytics
• Social media analytics focuses on analyzing data from social media platforms to
gain insights into customer behavior, sentiments, and trends.
• It involves monitoring and analyzing social media content, user engagement, and
social network connections.
• Social media analytics techniques include sentiment analysis, social network
analysis, social listening, and trend analysis.
• Facebook: Facebook utilizes social media analytics to provide insights to
businesses through its Ads Manager platform. It allows businesses to target specific
demographics, track campaign performance, and measure the impact of their ads
based on user engagement, conversions, and other metrics.

24. Dept. of MBA, Sanjivani COE, Kopargaon
Application of Business Analytics
1. Sales and Marketing
2. Operations and Supply Chain
Management
3. Finance and Risk Management
4. Human Resources

25. Dept. of MBA, Sanjivani COE, Kopargaon
1. Sales and Marketing:
• Customer Segmentation: Analyzing customer data to segment the
customer base based on demographics, behavior, and preferences, enabling
targeted marketing campaigns.
• Market Basket Analysis: Identifying associations and patterns in customer
purchase data to make product recommendations and cross-selling
strategies.
• Churn Analysis: Predicting customer churn and implementing retention
strategies to minimize customer attrition.
• Campaign Optimization: Analyzing the performance of marketing
campaigns and optimizing marketing spend to maximize return on
investment (ROI).

26. Dept. of MBA, Sanjivani COE, Kopargaon
2. Operations and Supply Chain Management
• Demand Forecasting: Using historical sales data and market trends to forecast
future demand, enabling optimized inventory management and production
planning.
• Supply Chain Optimization: Analyzing supply chain data to identify bottlenecks,
optimize logistics, and streamline operations for cost reduction and improved
efficiency.
• Quality Control: Applying statistical analysis to monitor and improve product
quality, identify defects, and reduce rejections or recalls.
• Predictive Maintenance: Utilizing sensor data and predictive models to anticipate
equipment failures, optimize maintenance schedules, and minimize downtime.

27. Dept. of MBA, Sanjivani COE, Kopargaon
3. Finance and Risk Management
• Financial Analysis: Analyzing financial statements, cash flow data, and market
trends to assess the financial health of a company and make informed investment
decisions.
• Fraud Detection: Applying advanced analytics to detect patterns of fraudulent
activities and anomalies in financial transactions, reducing financial losses.
• Credit Risk Assessment: Evaluating creditworthiness of individuals and
businesses by analyzing credit history, financial ratios, and other relevant data.
• Portfolio Optimization: Optimizing investment portfolios by analyzing historical
market data, risk-return trade-offs, and asset correlations.

28. Dept. of MBA, Sanjivani COE, Kopargaon
4. Human Resources
• Workforce Planning: Analyzing HR data to forecast workforce demand,
identify skill gaps, and optimize recruitment and training strategies.
• Employee Performance Analysis: Assessing employee performance
metrics, identifying top performers, and implementing performance
improvement initiatives.
• Attrition Analysis: Identifying factors influencing employee turnover,
predicting attrition risks, and implementing retention strategies.
• Talent Analytics: Using data-driven insights to identify and retain high-
potential employees, support succession planning, and improve workforce
productivity.

29. Dept. of MBA, Sanjivani COE, Kopargaon
5. Customer Service and Experience
• Sentiment Analysis: Analyzing customer feedback and sentiment data from
various sources (e.g., surveys, social media) to understand customer satisfaction
and sentiment.
• Customer Lifetime Value (CLV) Analysis: Predicting the long-term value of
customers to prioritize marketing and service efforts towards high-value customers.
• Personalization and Recommendation: Leveraging customer data and machine
learning algorithms to provide personalized product recommendations and enhance
customer experience.
• Voice of the Customer (VoC) Analysis: Analyzing customer feedback to identify
areas for improvement, drive product/service enhancements, and enhance customer
loyalty.

30. Dept. of MBA, Sanjivani COE, Kopargaon
Thank You….