Big Data and Analytics - Why Should We Care?

Big Data & Analytics – So What?

A few answers by Vishwa Kolla

(Prepared for UMass Boston MBA Students)

About Vishwa Kolla

Vishwa Kolla
Sr. Consultant, Advanced Analytics & Modeling
Deloitte Consulting, Boston

MBA Carnegie Mellon University
MS University of Denver
BS BITS Pilani, India

Professional Interests Personal Interests
 Absolutely love solving a variety of business  Most recent interest - watching my 4 year old
problems using advanced, predictive analytical grow (lot of fun and lot of work)
techniques and building decision support systems  Volunteering for a non-profit organization to help
as a means it grow and shape the direction of its growth
 My engagements typically involve synthesizing Big  Outdoor activities – climbing 14ers (peaks over
Data into actionable insights 14,000 ft. high), skiing
 Some engagements include:  Traveling
 Helping F5 firm solve customer attrition  Meeting new people
 Helping Top 5 professional services firm solve  Philosophy – understanding differences between
employee attrition cultures and reasons why various cultures
 Predicting what will viewers watch and when developed and are as they are currently
on TV for a large Cable company  Coaching / Mentoring / Teaching / Helping people
 Building demand forecast models reach their highest potential
 Implementing scoring engines & building
simulators

Big Data & Analytics - Why Should We Care? Vishwa Kolla | vish.kolla@gmail.com March 27, 2013 2

Contents

What is Big Data?
Why is Big Data Important?
How does Big Data manifest in our daily lives?
Who is into Big Data?
What skills are required to master Big Data?
How can I get started?

Big Data & Analytics - Why Should We Care? March 27, 2013 3

What is Big Data?

Big data is high-volume, high-velocity and high-variety information assets
that demand cost-effective, innovative forms of information processing for
enhanced insight and decision making.

- Gartner

Source(s): (1) Gartner


What is Big Data?

Volume of data created Worldwide

 1 YB = 10^24 Bytes
Dawn of 2003 … 2012 2015
 1 ZB = 10^21 Bytes
time
 1 EB = 10^18 Bytes
 1 PB = 10^15 Bytes
 1TB = 10^12 Bytes
5 EB 2.7 ZB 10 ZB (E)  1 GB = 10^9 Bytes
Big Data Elements
Variety of data
Velocity Volume  Radio  Tweets  Wikipedia
 TV  Blogs  GPS data
 News  Photos  RFID
Variety
 E-Mails  Videos (user  POS
 Facebook and paid) Scanners
Posts  RSS feeds  …

Velocity of data
 Walmart handles 1M transactions per hour  Facebook when had a user base of 900 M
 Google processes 24PB of data per day users, had 25 PB of compressed data
 AT&T transfers 30 PB of data per day  400M tweets per day in June ’12
 90 trillion emails are sent per year  72 hours of video is uploaded to Youtube
 World of Warcraft uses 1.3 PB of storage every minute

Source(s): (1) IBM’s Understanding Big Data eBook (2) Intel’s Big Data 101, (3) The Big Data Group (4) YouTube Press statistics


How Big is Big, Really?

Source(s): (1) Mozy.com


Big Data & Analytics Ecosystem – It revolves around improving people’s lives

5  Improving people’s lives is almost always the end goal
 The uses of big data and analytics transcends industries, firms and functions

People

4  Desktop / Web / Mobile apps consume these insights
Apps &
 E.g., Desktop -> Dashboards, Web -> Movie recommendations, Mobile
Devices (Restaurant recommendations)

3 Visualization &  Visualization tools are used to better understand inherent patterns
 The data is processed, transformed and analyzed to create insights
Analytics  More often than not, scoring models are built that auto-generate insights

The format of the data is either
2 Data Store  Structured (e.g. database tables)
(Structured & Unstructured)  Un-structured (e.g., E-Mails, Blogs, Photos, Videos)

Data is generated from a wide variety of sources that are either
1 Data Providers  Instrumented (e.g. POS scanners, Video surveillance cameras)
(Instrumented & Non-instrumented)  Non-Instrumented (e.g., Facebook posts, Twitter feeds, blogs)


Contents

What is Big Data?


It is not about having a lot of data; it is about USING data effectively

Value gap as perceived by the
market. Effective use of big data
amongst other things is an
important driver of this gap

Source(s): Google finance


It is not really about Big Data, but is really about Tiny Data (i.e, INSIGHTS)

Who should I hire? What is similar to
this customer?
Given weather
Who is likely to patterns, what
attrite? should I sell?
What will demand
be in 2014?
Who is likely to Which ad will
respond to an this customer
offer? How much should I watch?
spend on marketing?

What is at the
What should What does this risk of default?
I offer? customer value?

Who is likely
Who will this How much stock to vote for the
customer watch? should I carry? democrats?


Contents

What is Big Data?


Then and Now – Marketing

Then Now

Marketing Leads Campaign Recommendations

Source(s): (1) Big Data Trends by David Feinleib


Then and Now – Selling

Then Now

One size fits all Personalization & Targeted Selling



Then and Now – IT

Then Now

Peruse through log files Interactive Dashboards



Then and Now – Customer Service

Then Now

Reactive Customer Service Pro-active Customer Service



Then and Now – Credibility

Then Now

Credit Databases Professional & Social Networks



Then and Now – Operations

Then Now

Maps Location Based Services



Then and Now – Medical Research

Then Now

Keyword searches Word Clouds



Then and Now – Fitness

Then Now

Manual tracking Focus on the goal



Contents

What is Big Data?


The Big Data buzz has begun; every one is into it …
WSJ Books / Articles
• Teaming up on Big Data • IBM’s E-Book
• Re-inventing society in the wake of Big Data • Deloitte E-Book
• Wanted – A few good data scientists • HBR – The management revolution
• Big Data adds nickels and dimes to Giant Wind Farm • HBR – Making Advanced Analytics work for you
• Visa uses Big Data in Fraud detection • HBR – Next best offer
• How Big Data is changing the Whole Equation of • Amazon books
Business
• Moneyball, VC Style (using Big Data)
Big Data in Various Industries
• Big Data, Big Blunders
• Healthcare
• The New Shape of Big Data
• Financial Services
• What your CEO is reading – Steam Engines Meet Big
Data • Big Data in Insurance
• Retail
A few company sites about Big Data
• Deloitte’s Big Data site Big Data in Various Functions
• PWC’s Big Data site • Marketing
• IBM’s Big Data site • Operations
• Intel’s Big Data site • HR
• Microsoft’s Big Data site • Finance
• Walmart


… and they are into it very seriously


Contents

What is Big Data?


Skills Required to Master Big Data

 Leadership
 Management
5  Administrative
 Consulting
 People
People

4  Web 2.0
Apps &  Mobile Apps
Devices  Device specific - iOS / Andriod
 Device agnostic – HTML 5.0

3 Visualization &  Effective Data visualization techniques
 Statistical & Probabilistic techniques
Analytics  Analytical methods, tools & processes

 Cloud
2 Data Store  RDBMS (SQL)
(Structured & Unstructured)  NoSQL, Hadoop

 Hardware engineering
1 Data Providers  Instrumentation & Design
(Instrumented & Non-instrumented)  Content generators (FB posts, blogs, videos, photos)


Skills Required to Master Big Data & Analytics

Customer Analytics Marketing Analytics Lifestyle & Life Stage
 Profitable growth  Pricing  Insurance Premium Pricing
opportunities  Price & demand  Detecting diseases based on
 Next best offer optimization lifestyle
 Cross-Sell  Market Mix
Indus Functi
tries Fraud Analytics Workforce Analytics Subscription Analytics ons
 Fraudulent claims  Hiring  Credit Score
 Fraudulent transactions  Growing  Analytics in the cloud
 Retaining

Statistical & Visualization Programming Genuine
Probabilistic Techniques & Trouble- Curiosity
Techniques shooting

Skills Required to Master Big Data & Analytics – Some Tools to Learn

Source(s): http://www.bigdatalandscape.com/


Skills Required to Master Big Data – Example 1 of effective visualization


Skills Required to Master Big Data – Example 2 of effective visualization

Source(s): Visual News


Contents

What is Big Data?


Navigating Big Data and Analytics is a Journey
Master of
1. Develop your
Big Data Establish eminence (by
& Analytics publishing your work)
1. Solve the same problem across
industries
Grow 2. Solve different problems across
industries
3. Apply methods across functions

1. Learn industry best practices when you get
hired into a firm
2. Surround yourself with good people and
Learn from the experts experts to accelerate your learning
3. Build / implement models under the guidance
of an expert

1. Pay attention in Probability & Statistics courses
2. Learn at least one programming language thoroughly and a few if
you can
3. Recommended minimum tool sets: R, SAS, Tableau
Foundation 4. Take advanced level analytical courses such as New Product
(School) Introduction, Optimizations, Operations Research, Data-mining,
Modeling, Forecasting & Time Series, Simulations
5. Practice solving problems end-to-end to understand the
implication of building models and implementing them in real life


Some things to watch out for

1. Big Data is not a panacea
2. Big Data is not everything for everybody
3. Big Data does not have all the answers and is directional at best if done right
4. Big Data & Analytics do not replace human intelligence ; Relying solely on Data & Analytics usually trips one up
5. There are several limitations of using Big Data & Analytics. Some are:
a) Data collection limitation -> Not all data can and is collected. One may have access to a ton of data, but very
little can be analyzed and/or is meaningful
b) Data quality limitation -> Garbage in garbage out; this is getting better every day
c) Data transformation limitations -> Raw data is rarely used. It is almost always transformed. There is no perfect
transformation
d) Measurement limitation -> Metrics cannot capture the entire picture
e) Modeling limitation -> Not every relationship can be modeled. The models mostly confirm / deny hypotheses.
Again, models need to be evaluated for their predictive strength before adoption
f) Interpretation limitation -> One needs to be careful when interpreting results and often misinterpretations of
data / metrics / model insights can be dangerous
g) Actionability limitation -> Not all insights are actionable. They may very well be interesting, but one cannot act
on most insights
h) Using / Relying on single data source / data point -> Coming to a conclusion based on a single or very few
biased data points can often happen
6. At the end of the day, to make Big Data & Analytics work for you, one needs to question the outcomes and
insights, reconcile with understanding and use the insights as illumination as opposed to for support


Summary

1. Big Data is Big. It is easy to get lost. Know and understand what you are getting into
before you leap
2. Make up your mind of where you want to play (i.e., get into the area where your
strengths lie)
3. Build a roadmap of where you want to go and how you are going to get there
4. Fill in the skill gaps
5. Surround yourself with good people. You are a sum total of who and what you interact
with
6. Have fun and enjoy what you are doing


Questions?


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