1.
Susan Etlinger
Susan Etlinger is an industry analyst with
Altimeter Group, where she focuses on data
and analytics.
She conducts independent research and has
authored two intriguing reports: “The Social
Media ROI Cookbook” and “A Framework for
Social Analytics.”
She also advises global clients on how to work
measurement into their organizational
structure and how to extract insights from
the social web which can lead to tangible
actions.
In addition, she works with technology
innovators to help them refine their roadmaps
and strategies.

2.
What is BIG DATA?
● ‘Big Data’ is similar to ‘Small Data’, but bigger in size
● But having data bigger it requires different approaches:
-Techniques, tools and architecture
● An aim to solve new problems or old problems in a better way
● Big Data generates value from the storage and processing of very large
quantities of digital information that cannot be analyzed with traditional
computing techniques.
● Walmart handles more than 1 million customer transactions every hour.
● Facebook handles 40 billion photos from its user base.
● Decoding the human genome originally took 10 years to proces; now it can be
achieved in one week.

3.
So what does this mean for Analytics?
So what does this
mean for Analytics?
Yes, the amount of data that is available to us is exploding
And Big Data Platforms and Commodity Hardware and bringing in additional capabilities
Media is rife with Big Data and Analytics
Big Data and analytics is touted as the panacea for all problems
…makes it to on top of CIO agenda AND
The Data Scientist makes it from Nerd to the most cool person!!
.

4.
Data Science
perspective
– A Data Science perspective
Big Data and AnalyticsImpact of

5.
A brief history of Data Science
Pre 1800s 1800-1900 1900-1940 1940-1960 1960 1970 1980 1990 2000 2010
▪ Text/ string search
▪ 1974 Peter Naur “Concise Survey of Computer
Methods”, Data Science, Datalogy
▪ Knuth – Art of Computer Programming.
▪ 1976 – SAS Institute
▪ 1977 The International Association for
Statistical Computing (IASC).
Computer Science
Data Technology
Visualization
Mathematics/ OR
Statistics
▪ Probability
▪ Correlation
▪ Bayes Theorem.
▪ Regression, Least
Squares
▪ Time Series.
▪ Theoretical Foundations of Modern Stats
▪ Hypothesis, DOE
▪ Mathematical Statistics.
▪ Bayesian Methods
▪ Time Series Methods (Box Cox,
Survival, etc.)
▪ Stochastic Methods.
▪ Simulation, Markov
▪ Computational Statistics.
▪ Decision Science
▪ Pattern recognition
▪ Machine learning.
▪ Liebniz – Binary Logic. ▪ Babbage, Lovelace
▪ Boolean Algebra
▪ Punch cards.
▪ Turing machines
▪ Information Theory
▪ Weiner & Cybernetics
▪ Von Neumann Architecture.
▪ Calculus
▪ Logarithms
▪ Newton-Raphson.
▪ 1989 First KDD Workshop
▪ Gregory Piatetsky-Shapiro.
▪ Sort & Search Algorithms –
Dijkstra, Kruskal, Shell Sort, …
▪ Heuristics – Simulated Annealing, …
▪ Graph Algorithms
▪ Multigrid methods
▪ Tree based methods.
▪ Database Marketing
▪ Data Mining, Knowledge Discovery
▪ “Data science, classification, and related methods.”
▪ William Cleveland: Data Science
▪ Leo Breimann: Statistical Modeling: 2 Cultures.
▪ Optimization Methods
▪ Fourier and other transforms
▪ Matrix & Generalizations
▪ Non-euclidean geometries.
▪ Applications to Military,
manufacturing,
Communications.
▪ 1962 John W. Tukey, Future of
Data Analysis
▪ Networks
▪ Assignment Problems
▪ Automation
▪ Scheduling.
▪ First IBM
Computers
▪ DBMS.
▪ Removable Disk drives
▪ Relational DBMS.
▪ Desktop, floppy
▪ SQL, OOP
▪ High level languages.
▪ William Playfair
▪ Charles Minard
▪ Florence Nightingale.
▪ Catrography
▪ Astronomical Charts.
▪ John Tukey
▪ Jacques Bertin. ▪ Edward Tufte.
▪ Grammar of Graphics
▪ Word Cloud, Tag Cloud.

6.
Drivers of change
Data
Availability
Technology
Ability to
Handle
Structured and
unstructured
data
Platform Cost Agility
Business
Expectation
Digital
Experience
Strategic
Initiatives
New Business Models

7.
Why Big Data?
● FB generates 10TB daily
● Twitter generates 7 TBof
Data Daily
● IBM claims 90% of
today’s store data was
generated in just the last
two years

8.
How is Big Data Different ?
1) Automatically generated by a machine
(e.g. Sensor embedded in an engine)
2) Typically an entirely new source of data
(e.g. Use of the internet)
3) Not designed to be friendly
(e.g. Text streams)
4) May not have much values
● Need to focus on the important part

9.
Three Characteristics of Big Data V3S
1. Volume (Data Quantity)
Boeing 737 generate 240 terabytes of flight data during a single flight across the US
2. Volume (Data Speed)
Machine to machine processes exchange data between billions of devices
3. Variety (Data Types)
Big Data isn’t numbers, dates and strings. It is also geospatial data, 3D data, Audio and Video and
Unstructured text, including log file and social media

10.
The Structure of Big Data
● Structured
Most traditional data sources
● Semi-structured
Many sources of big data
● Unstructured
Video data, audio data

11.
3 new insights from the video
1
Big Data = Poor
Data
● The more data you
have, the less probable
your chance of
discovering meaning --
the "why" of things.
2
To accelerate our
demise
● "We are in an age where
guided missiles are
operated by misguided
men"
● Unless we slow down
and analyze our needs
we will surely
accelerate our demise
3
Coding is not in
our control
● Big data can be a
transformative force,
and should be treated
as such

12.
Why and How these Insights relevant
to Managers in India ?

13.
1. Cost Reduction
Big data technologies such as Hadoop and cloud-based
analytics bring significant cost advantages when it comes
to storing large amounts of data – plus they can identify
more efficient ways of doing business.

14.
2. Faster, Better Decision Making
With the speed of Hadoop and in-memory analytics,
combined with the ability to analyze new sources of data,
businesses are able to analyze information immediately –
and make decisions based on what they’ve learned.

15.
3. New Products and Services
With the ability to gauge customer needs and satisfaction
through analytics comes the power to give customers
what they want. Davenport points out that with big data
analytics, more companies are creating new products to
meet customers’ needs.

16.
Decreased
response time
Customer experience
Information is becoming the
new battleground
Business expectation

17.
Analytics is playing an ever important role
Increased Focus on identifying the
customer across all channels
Segmentation to Micro segmentation
to the individual
Personalized Messaging and offers –
Increased Individual Customer Centricity
Gradual evolution of Customer Analytics
Past
▪ Customer segments who are
most likely to respond to
targeted campaigns for new
products offers
▪ Can tailor offers to specific to
each customer segment
▪ Mostly delivered through mass
mail campaigns and in store
promotions.
Now
▪ Micro segmentation
▪ Analyze customer behavior
and buying patterns across
channels
▪ Delivery through email, web,
mass mail campaigns.
Moving toward
▪ Historical individual customer behavior
and buying patterns across channels
▪ Individual customer consumption
pattern
▪ In-store basket analytics
▪ Additional dimensions Location & time
▪ Targeted Strategies to pre-empt
customers from visiting competition
▪ Instantaneous Delivery in store or a
proactive delivery via mobile to bring the
customer to store.
Segment to Individual to Individual @ time, place and behavior
You have purchased
Cheese, here are
the
offers on Bagels
You are within 2
KMs of a store
offering 50% off
garden furniture
Do you
need coffee?

18.
much of which is outside the
organization
Increased availability of data
Analytics as a Service and Data
Monetization
New service models
Decreasing Time value of data!

19.
Scalability and industrialization to address skill shortage
Key to a Great Data Scientist
Technical skills (Coding, Statistics,
Math)
+ Perseverance
+Creativity
+ Intuition
+Presentation Skills
+Business Savvy
= Great Data Scientist!
▪ Identified four Data Scientist clusters based on
how data scientists think about themselves and
their work, not
• Years of experience,
• Academic degrees, favorite tools
• Titles, pay scales, org charts.
▪ Most successful data scientists are those
with substantial, deep expertise in at
least one aspect of data science, be it
statistics, big data, or business
communication
▪ T-Shaped Skills.

20.
Presented By
Prince Barai
Data Analytics Intern at IIM, Lucknow