Overview of major factors in big data, analytics and data science. Illustrates the growing changes from data capture and the way it is changing business beyond technology industries.

1. Data
Big Data and Data Science
David
Lambert

2. Agenda
• Introduction to Big Data
• The V’s
• What Businesses Need to Know
• Market Research
• Analytics
• Modeling
• Data Science

3. Introduction
Census 2013: Internet and Smartphone use

4. Introduction
140,000-190,000 job shortfall by 2018
4.4 million jobs in Big Data related by 2015 in US
Gartner 2012

5. Introduction

6. Introduction
Forbes 2014

7. What Is Data
Two Basic Types of Computer Data
• Structured
• Variable or Metric information that is easily accessible
and shared between computers and databases. (time,
date, location, user ID, file.pathway, sensor)
• Unstructured
• Information that is difficult to quantify such as text,
emails, pictures, videos and other socially generated
content and is beyond typical processing power.

8. What Is Data
Structured
Unstructured

9. What Is Data

10. Share of Global Internet Searches
1.2 Trillion searches per year
40,000 searches per second
How Much Data
http://www.internetlivestats.com

11. How Much Data
http://www.internetlivestats.com

12. How Much Data
64kb for the
1969 moon launch

13. Why Now
Digital Storage Has Become Very Cheap Price for CPU Performance is Cheap

14. • Information
Connectivity
• Databases
• Machines
• Employees
• Customers
• Products
1
Basic Corporate
Interaction with
Clients
Experiences are ‘Pushed’
on Consumers by
companies. Companies
advertise and market to
what they want the
consumer to think with little
or no feedback.
2
Back and forth communication
between ISOLATED users and
producers.
Push-Pull marketing efforts evolve to
gauge consumer experience. Exclusive
Company/Customer Dialogue, costly
Surveys, and limited Focus Groups were
typical of this type of corporate
relationship.
3
Consumer community connections and
corporate relationships.
User-to-user connections grew exponentially with the rise
of social media platforms, like Facebook and the internet.
Consumer experience shifted away from getting
information from companies but rather gaining insight
through advanced social webs. The general thought
being that consumers-to-consumer interactions are less
bias and convey more knowledge about an experience
than an organizations. There Is an increased feeling of trust
in dealing with a non-partisan opinion.
4 Cloud
Server
Cloud and Mobile system
integration and infrastructure
Through the development of advanced
computer infrastructures, information growth
was so rapid that it is referred to as an
explosion of Big Data. The transition to
smartphone use over standard computer use
caused greater behavioral data to be
captured by cloud services. Smartphones act
as the most common gateway or remote to
this advanced network computers.
Cloud
Service
5
Internet of things and User Connectivity
The cost and benefit of connecting a product to a
cloud or internet service captures so much value
that companies everywhere are integrating systems
to utilize this capability. Interactions between
consumers and their products and product-to-
product interactions will become increasingly more
frequent and will boast a wave of new services to
better integrate these systems into the consumer’s
life.
Progression
Cloud
Cloud
ServiceService
Service
Service
PullPush
Web Cloud
Internet
of
Things
Server

15. Big Data
People are creating more Data
People, Products and Companies are creating ENORMOUS amounts of Data
Companies are creating more Data
Products are creating more Data

16. Big Data
The 3 Vs Chart
Innovation must be done more quickly
and effectively due to this increase in
competition, availability of servicesand
dynamically changing technology.
Old business issues are still prevalent in
BD except the speed,
scope &tempo of
business offerings have
dramatically increased. Businesses
MUST take more control over their
service offerings.
The importance of
maintaining a
consistent and
authentic
experience
throughout all operations is
much more significant with
the influence of BD.
Organizations must choose or
transition to
Attractive channels for
their objectives,
customers, and culture.
Greatest Challenge:
Simply put, Big Data
is an increase in the relationships
between Hardware & Software,
Products & Services,
Customers & Employees
within an organization or business.

17. The V’s

18. The Infinite V’s
1. Volume
2. Velocity
3. Variety
4. Variability
5. Veracity
6. Visualisation
7. Value
Data Capture
Data Cleaning
Data Preparation and Reporting
Data MarketingProcess

19. Data Capture Data Cleaning Data Preparation Data Marketing
Process
What Businesses Should Know

20. What Businesses Should Know
https://infocus.emc.com/william_schmarzo/big-data-business-model-maturity-chart/
• Consultant at EMC Global
Services
• Former Vice President of
Advertising Analytics at Yahoo
Bill Schmarzo
Big Data Author and
Blogger at EMC
*** Text is excerpted or paraphrased from several articles
by Bill Schmarzo at his EMC blog.
53 41 2

21. What Businesses Should Know
1.Integrate (structured) Meta-Data with detailed
(unstructured) Behavioral Data
to provide new metrics and new dimensions against which to monitor and
optimize key business processes.
Initial Big Data Focus: Optimize Internal Business Process
https://infocus.emc.com/william_schmarzo/the-4-ms-of-big-data/
There are three big data
capabilities
that organizations canleverage
to expand their business intelligence and data warehouse
investments to optimize versus just
monitor.
2.Deploy predictive analytics to
uncover insights buried in the massive volumes
of detailed structured and unstructured data.
Having business users slice-and-dice the data to uncover insights does not work very well
when dealing with terabytes and petabytes of data.
3. Leverage real-time(or low-latency)
data feedsto accelerate organizational
processes to identify and act upon business and market opportunities in a timely
manner.

22. What Businesses Should Know
Ultimate Big Data Opportunity: Monetize External Customer Insights
As organizations advance along the maturity index, three
organizational
transformations take place to create
new monetization based upon the
customer, product and
market insights gleaned from the first three
phases of the maturity index
1. Organizations start to treat data
as an asset, not a cost of business.
2. Organizations place formal processesto capture,
inventory,refine,and protect their analytics
as intellectual property (IP). Analytics, models, processes, etc.
3. Organizational confidence in making
decisions using dataand analyticswill grow.
Organizational investments in data, analytics, people, processes, and technology will be used to justify decision
making.

23. What Businesses Should Know

24. A SHIFT FROM SURVEYS TO INTERACTION
• Surveys are difficult to…
• Scale
• Incentivize customers
• Assess validity
• Ask appropriate questions for meaningful insight
• Interaction monitoring is more successful to…
• Scale
• Incentivize customers
• Assess Validity
• Monitor Appropriate behavior for meaningful insight
Interaction Collection = Data Mining
Market Research

25. Market Research
The platform or
“skeleton” service
initially starts out with
very little information about
that particular user and
thus the experience for
that user is typically
unexciting or ambiguous.
User
Fundamental service strategy:
solutions selling, system integration
Interaction
Behavior
Knowledge
Consumer Interaction Platform

26. As the User begins to interact
with the platform, the user
provides user-defined inputs to
basic profile characteristics.
User defined variables should be
limited to varaibles that are not
easily gathered from general
usage information. Age, Gender,
income or other basic
characteristics
may be identified.
Interaction
Behavior
Knowledge
Market Research
User
Fundamental service strategy:
solutions selling, system integration
Consumer Interaction Platform

27. General activity and interaction
with the Service Platform gives
enough behavioral data to give
an extrapolated rough sketch of a
consumer’s behavioral profile.
General usage data can include
activity hours/per month,
activity duration per use, typical
activities performed during use.
Interaction
Behavior
Knowledge
Market Research
User
Fundamental service strategy:
solutions selling, system integration
Consumer Interaction Platform

28. Adoption of the service
platform into the clients
lifestyle gives a caricature-like
view of a consumer, with
exaggerated likes and
dislikes.
The consumer engages with the
platform regularly and has
customized it to their
preferences and application.
Interaction
Behavior
Knowledge
Market Research
User
Fundamental service strategy:
solutions selling, system integration
Consumer Interaction Platform

29. Immersion into the platform by the user
creates a sophisticated profile that can
be leveraged to gain unprecedented
insight and predictability into consumer
behavior. The dedicated use of a
service platform by a single user details
an almost life-like portrait of the user.
If the user is engaged in multiple
channels the data becomes even more
relevant as it is analyzed across different
markets segments and applications.Interaction
Behavior
Knowledge
Market Research
User
Fundamental service strategy:
solutions selling, system integration
Consumer Interaction Platform

30. Low
Customization
Profile Activation
Low Account Activity:
A picture, couple friends
Active Member: Moderate
Photos, Postings, Friendship
Prolonged Use:
Lots of Engagement
Interaction
Behavior
Knowledge
Consolidated
User
Profile
with
Behavior
Generate Value by
analyzing behavior
Compile
Similar
Users
into
Segment
Data
Packages
Mobile
Advertising
and
Proximity
Analytics
Service
Innovation
and
IT-­‐Enablement
through
Behavioral
Analytics.
ExactTarget
Market Research
Consumer Interaction Platform

31. Interaction
Behavior
Knowledge
Consolidated
User
Profile
with
Behavior
Leverage Data
(Generate Value)
Compile
Similar
Users
into
Segment
Data
Packages
Many Companies reflect only a rough sketch of their customers
Prolonged Use:
Lots of Engagement
Market Research
Consumer Interaction Platform

32. Interaction
Behavior
Consolidated
User
Profile
with
Behavior
Leverage Data
Compile
Similar
Users
into
Segment
Data
Packages
Many Companies reflect only a rough sketch of their customers
Prolonged Use:
Lots of
Engagement
Consumer Interaction Platform
Market Research
Marketing based on
Behavior
It is important to note that this also applies to process.

33. Market Research

34. Analytics

35. Defining a system of metrics or variables to be monitored, compared and contrasted
within that system to determine the PREDICTIVE power of specific variables to
specific outcomes.
Analytics
Key Performance Indicator (KPI)
• ROI
• Net Profit/Profit Margin
• Customer Acquisition Cost

36. Analytics
Advertising/Sales/Marketing
Interaction/Usage Sentiment
Brand Culture
Customer Acquisition
ROI
Conversion Rate
Social Media Engagement
Distinguishing Attributes or Characteristics
Behavioral Rituals and Norms
Attitudes
Lexicon
Positioning
User Experience
Where is the interaction
Usage Time

37. Analytics
Product/Corporate Relationship
Advertising/Sales/Marketing
Interaction/Usage Sentiment
Brand Culture
Customer Acquisition
ROM
Conversion Rate
Social Media Followers/Mentions
Distinguishing Attributes or Characteristics
Behavioral Rituals and Norms
Attitudes
Lexicon
Positioning
User Experience
Where is the interaction
Usage Time
• ROI
• Net Profit/Profit Margin
• Customer Acquisition Cost
• Lifetime Value of Customer

38. Analytics

39. Analytics

40. Analytics

41. Analytics

42. Analytics

43. Analytics

44. Analytics

45. Analytics

46. Analytics
How Google uses Analytics to drive Technology

47. Analytics
How Google uses Analytics to drive Technology
• Purchased by Google in 2006
• All Music videos except #8
• Music has a lot of replay
value. Sharable
• Psy and Katy Perry Appear
twice on the top 15
http://en.wikipedia.org/wiki/List_of_most_viewed_YouTube_videos

48. How Google has Used Analytics to drive Technology
Analytics

49. Google Adwords
• Sunshine Dairy
Analytic Modeling
• Media Buy
• Geography
• Click Through-Rate
• Impressions
• Conversion Rate
• Cost Per Click
• Ad Position
• Size
• Media (Vid/Pic/Audio)
• Lifetime Value of Customer
• Creative
• Style (Color/Mood/Message)
• Short/Long Term Branding
Metrics

50. Analytic Modeling
Analytic Modeling is using the identified variables to forecast possible outcomes
for the future. These models will be compared to actual results to build better
models that more accurately predict consumer influences and outcomes on the
market.
A & B TestingForecast Method

51. Analytic Modeling
A & B TestingForecast Method
Exponential Smoothing
Simple Regression
Multiple Regression
Moving Averages
Substitution Forecasting
Hybrid Forecasting

52. Data Science
A Data Scientist is a generic term for someone
who possess the ability to do a combination of jobs which include;
Data Development Large Data Statistics
Data Analyst

53. Data Science
T - Shaped Skills

54. Data Science

55. What To Learn
Learn to Code
Software Engineering
Algorithms & Data Structures
Visualization
Data Munging
Distributed Computing
Machine Learning
Supervised (SVM, Random Forest)
Unsupervised (K-means, LDA)
Validation, Model Comparison

56. What To Learn
Linear Algebra
(Matrix Factorization)
Calculus
(Integrals, Derivatives)
Distribution
(Binomial, Poisson)
Summary Statistics
(Mean, Variance, Std Dev.)
Multivariate Analysis
Mathematics Statistics
Learn Math and Stats

57. Where To Learn

58. Online Programs
datasciencemasters.org

59. Ted Talks Taxonomy

60. Questions
?