Bi Data in 9 verticals

1. Big Data in Verticals
A peek into digital transformation
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2. The rise of
Big Data
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3. BFSI
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4. Big Data in
BFSI Sector
Customer Segmentation
Personalized Marketing
Risk Management Analysis
Fraud Detection
Compliance Requirements
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5. Big Data
in
Banking
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6. Big Data Analytics in BFSI
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7. Healthcare
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8. Role of Big Data in Healthcare
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9. Big Data
Analytics in
Healthcare
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10. 4 Big Data Strategies in Healthcare
PROVIDING
COMPREHENSIVE,
QUALITY TRAINING
DATA
1
ELIMINATING BIAS
IN DATA AND
ALGORITHMS
2
DEVELOPING
QUALITY TOOLS
WHILE PRESERVING
PATIENT PRIVACY
3
ENSURING
PROVIDERS TRUST
AND SUPPORT
ANALYTICS TOOLS
4
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11. Big Data in
Healthcare -
Opportunities
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12. Retail
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13. Big Data
Use Cases
in Retail
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14. Nine ways
Big Data is
disrupting
Retail
Generating Recommendations:
Making Strategic Decisions:
Forecasting Trends:
Utilizing Market Basket Analysis:
Optimizing Pricing:
Listening to Social Media:
Predicting Trends:
Enhancing Customer Experience:
Retail analytics:
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15. Architecture
of Big Data in
Retail
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16. Energy
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17. Big Data in Energy
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18. Big Data
Use Cases
in Energy
Fault
detection
and
predictive
maintenance
Electric
power
quality
Load
management
It’s time to
secure the
energy gains!
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19. Big Data in
Smart Grid
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20. Education
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21. 5 Big Benefits of
Big Data in
Education
Industry
It helps you
find answers to
hard questions
It’s accessible
It can save
costs
It’s quick
It helps you
adapt
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22. Big Data
Sources in the
Education
Sector
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23. The future of
Big Data in
education
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24. Manufacturing
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25. Big Data
Challenges in
Manufacturing
Industry 4.0
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26. Big Data
Benefits in
Manufacturing
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27. Data driving
Industry 4.0
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28. eCommerce
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29. Big Data
Benefits in
eCommerce
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30. Pivotal role
of Big Data in
eCommerce
• Personalized stores: Merging search and purchase history of customers and lookalike visitors will create a much
more personalized shopping experience. This will translate to higher conversion rates and more cross-sell
opportunities.
• Personalized marketing: Marketing will become increasingly sophisticated. Merchants will send multiple email
variations based on customer segments. For example, if a customer buys only t-shirts, sending him an offer for
pants will likely be ineffective. Similarly, customers who buy only discounted goods will presumably not respond to
a full-priced offer. Marketing to both customer types requires collecting and segmenting the data.
• Increased automation: Automating repetitive tasks not only saves human resources. It also improves the
customer experience. An example is using chatbots for customer service, which can improve accuracy and
response time. Find ways to automate by asking each employee to describe repeated tasks.
• More cross-border sales: Automated language and currency translation streamlined shipping and local payment
options will help merchants penetrate global markets with little investment. Even human translators (such as on
Fiver) are becoming less expensive. And shipping platforms and plugins can calculate at checkout the exact
worldwide transit cost.
• Better forecasting: Business intelligence tools can now forecast sales, optimize prices, and predict demand—in
detail. The result is lower inventory quantities and targeted promotions based on a product’s demand. Businesses
can move faster without spending a lot of money. To start, merchants can acquire an intelligence platform or hire
a machine learning expert who can forecast in R or Python.
• Research with social media: Marketers will focus on understanding the customer and her behavior leveraging the
massive, public data on social media sites. Retailers will shift from using net promoter scores and surveys to
analyzing qualitative and quantitative info. Merchants can start by manually categorizing the opinions of
customers and prospects around products, product types and the business overall. Over time this data can be
aggregated for ongoing insights.
• More privacy laws: Governments worldwide are imposing strict privacy laws on the collection and use of
consumer data. Examples include Europe, Korea and California. More will undoubtedly come. Merchants will
spend money on legal fees, employees such as data compliance officers and consultants. Marketing capabilities
will presumably decrease, as will customer experiences.
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31. Data Science at Scale
– 5 points
• Make personalized product
recommendations
• Determine customer behaviour
and shopping patterns
• Improve customer experience
• Prevent fraud
• Winding Up
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32. Tech
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33. Types of Big
Data in Tech
• Data Storage
• Data Mining
• Data Analytics
• Data Visualization
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34. Rise of Big Data
in IT industry
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35. Big Data – Analytical Data Platforms
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36. Telecom
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37. Top Big Data applications in Telecom
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38. When Big Data
meets the Telecom
Operator
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39. Telecom
Operator –
Big Data
Source
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