This deck contains insights on Impact of Business Intelligence, Visualization, Artificial Intelligence, Machine Learning, Deep Learning, Use Cases and how to get started...

1. Impact of Modeling, Analytics &
Visualization, AI

2. What do I aim to cover…
• Trends…
• Data Modeling, Visualization
• Impact on Business
• AI/ML, Data Science
• Impact on Business
• How to get started

3. Beforewemoveahead…
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”
-Jim Barkesdale, CEO of Netscape
Clive Humby, UK Mathematician and architect of Tesco’s Clubcard, 2006 (widely credited
as the first to coin the phrase): “Data is the new oil. It’s valuable, but if unrefined it cannot
really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable
entity that drives profitable activity; so must data be broken down, analyzed for it to have
value.”
Peter Sondergaard, SVP Gartner, 2011: “Information is the oil of the 21st century, and
analytics is the combustion engine.
Piero Scaruffi, cognitive scientist and author of “History of Silicon Valley”, 2016: “The
difference between oil and data is that the product of oil does not generate more oil
(unfortunately), whereas the product of data (self-driving cars, drones, wearables, etc) will
generate more data (where do you normally drive, how fast/well you drive, who is with you,
etc).”

4. The data volumes are exploding, more data has been created in the past two years than in the entire previous history of
the human race.
Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created
every second for every human being on the planet.
By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or
44 trillion gigabytes.
Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone),
which makes it 3.5 searches per day and 1.2 trillion searches per year.
In a recent month, over 1 billion people (10,000 Lakhs) used Facebook FB +0% in a single day.
Facebook users send on average 31.25 million messages and view 2.77 million videos every minute.
We are seeing a massive growth in video and photo data, where every minute up to 300 hours of video are uploaded to
YouTube alone.
In the coming year, a staggering 1 trillion photos (1 lakh crores) will be taken and billions of them will be shared online.
By next year, nearly 80% of photos will be taken on smart phones.
This year, over 1.4 billion smart phones will be shipped - all packed with sensors capable of collecting all kinds of data,
not to mention the data the users create themselves.
By 2020, we will have over 6.1 billion smartphone users globally (overtaking basic fixed phone subscriptions).
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5. Within five years there will be over 50 billion smart connected devices in the world, all developed to collect,
analyze and share data.
By 2020, at least a third of all data will pass through the cloud (a network of servers connected over the Internet).
Distributed computing (performing computing tasks using a network of computers in the cloud) is very
real. Google GOOGL +0% uses it every day to involve about 1,000 computers in answering a single search query,
which takes no more than 0.2 seconds to complete.
Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year — that’s
equal to reducing costs by $1000 a year for every man, woman, and child.
The White House has already invested more than $200 million in big data projects.
For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million
additional net income.
Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.
73% of organizations have already invested or plan to invest in data related projects
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6. 19 And one of my favourite facts:
At the moment less than 0.5%of all data is ever analysed
and used, just imagine the potential here.

7. Clearly… data is becoming much more valuable…
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8. Questions that are in CxOs’ minds…
How do I make sense out
of all the data collected?
How do I gain insights
about my business?
Am I using the right tools
& technologies?
Is Cloud the right choice
for the future?
Can it predict my future?
Am I able to visualize the
data available easily?
Can data help me make
my decisions?
Am I collecting right,
enough and all data?
Am I collecting
usable data?
Is the data collected usable
by my Analysts directly?

9. Data Terminologies…
Data
Data
Warehouse
Data
Modeling
Big Data
Data
Science
Data
Mining
Database

10. Data Terminologies…
Data
Data
Warehouse
Data
Modeling
Big Data
Data
Science
Data
Mining
Database
Electronic method to
store data… in a
schematic fashion
Multi-dimensional
way to store essential
data – to suit data
mining & analytics
Refers to finding
insights / intelligence /
facts hidden in data,
now replaced more
with Data Science
A fast emerging field
combining Math & Stat
techniques to find
insights, patterns &
predictions
Extract, transform
data to suit for easy
& rapid visualization
Data running in excess of
petabytes usually –
combining both
structured, unstructured
data

11. Synonymous…

12. • High level overview of DW
• Primarily for business users to
get an idea of the DW
• Very less technical details
• Extension of CDM
• Entities & Relationships are
included
• Attributes, PKs, FKs are defined
• LDM and CDM are independent of
DB Tech
• PDM represents the actual DB
• Entities = tables, Attributes =
columns
• PDM is different for different DBs
• Data Types differ from SQL to Oracle
to DB2 for example
• PDM also includes Views,
Procedures, Indexes
• Then using DDL Statements – all are
created into the DB

13. Typical Enterprise Data Architecture

14. Data Modeling in Visualization Context…
Custom Tables
Custom Columns
Calculated
Columns
Hierarchies
Relationships
Custom
Measures

15. What is this data? What can you understand?

16. What can you understand now? (Demo)

21. •What we are selling (products)
•When we are selling (year)
•Where we are selling (country)
•How we are selling (order type)

23. Benefits…
Helps organizations
identify key trends
Enables swift action
Meaningful
interpretation of the
data, cut the clutter
Increase productivity &
sales (focus on doing
your core business and
not data analysis)
Tell a story
Faster ad-hoc data
analysis
Self-service capabilities
to end users
Reduced burden on IT

24. Let the dataset change your mindset!

25. What do I aim to cover…
• Trends…
• Data Modeling, Visualization
• Impact on Business
• AI/ML
• Impact on Business
• How to get started

26. AI / ML

27. Differentiation
Data
Vision
NLP, Speech Detection, Predictive, Prescriptive, Translation, Emotion Detection, Accessibility Assistance, Facial
Recognition, Smart Surveillance, Smart IoT, Self Healing Systems, Autonomous Driving, Robotics and much more…

28. AI in Our Daily Life…

29. AI – ML in Business – Examples…

30. AI – Computer Vision, Business - Examples

31. More use cases…
Customer Support
• Chat bots
• Voice bots
• AI with RPA
Sales & Marketing
• Recommendations
• Dynamic
Segmentation
• Automated
Marketing
• Dynamic Pricing,
Discounting
• Predictions
• Prescriptive Sales
Recommendations
BFSI
• Fraud Detection
• Loan
Recommendations
• Dynamic Credit
Banding
• Preventing Money
Laundering
• UBI – Usage Based
Insurance
Security & Facilities
• Facial Recognition
• ANPR
• Smart Parking
• Entry / Exits
• AI with IoT for
Smart Energy
Utilization
• PPE Violation
Detection
• Industry 4.0
Others..
• AR and VR
• Dynamic Product
Cataloging,
Positioning
• Smart Cities
• Personalized
Learning
(Education)
• Autonomous
Driving
• Healthcare

32. Wherearewe…

33. Your journey… (with no real end)
Keep learning…
•Understand terminologies
•ETL, DW, Schema, Relationships, Visualization
•Download and try with sample datasets
•Online courses (edx, coursera, udemy, guvi)
•Certifications (MS, AWS, G)
•Medium.Com, AnalyticsIndiaMag
•Attend Meetups
•Attend Industry Events
•Build Connections
Do BI Projects
Work with Demo DataSources
Become good with Excel
Demo Environments
Interact with Team Members, Managers
Interact with Customers
Learn the domain
First get strong with data management
Then Visualization
Take part in Real Projects
Data Science / AI / ML Projects
Learn languages (P), tools
Study Real World problems solved
Interact with Customers
Perform EDA
Learn the domain
Work with demo datasets
Participate in Kaggle contests
Volunteer to solve real world problems
Work on real projects

34. Desired
Personality traits

35. SSIS SSAS
Technology Landscape
AI – Machine Learning
AI – Deep Learning
Platforms
DW/BI, Power
Platform
•Ingest
•Format
•Enrich
•Filter
•Aggregate
Data Wrangling
•Sample
•Visualize
•Correlate
Data
Exploration
•Segment
•Categorize
•Simulate
•Predict
•Validate
Analytics
•Deploy
•Score
•Integrate
Deployment
•Measure Accuracy
•Bagging &
Bootstrapping
•Iterate
Accuracy
Improvisation
Azure ML Sage Maker
Azure Cognitive
Services
Azure Cognitive
ServicesGoogle Cloud
Keras Tensor Flow
SQL Server Azure Data
Factory
Power BI Tableau Pentaho
Qlik view &
Qlik Sense

36. Thank you…
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