The document discusses transforming an organization into a Big Data company. It outlines the challenges of digital disruption and how companies like Amazon, Apple, Google and Netflix understand customers through their digital footprints. It then discusses six challenges of Big Data including data capture, storage, analysis, visualization, IT dependence, and creating a new culture. The remainder of the document focuses on business models for Big Data and implementing Big Data strategies and projects within an organization.

1. Transforming your
organization into a Big
Data company
BIG DATA | Digital October 2015
Josep Curto | Professor, IE Business School | CEO, Delfos Research

2. 2
Agenda
Digital disruption
(or why Big Data is one of the key strategies for your organization)
Business Models
(The multiple personalities of Big Data)
Implementing Big Data
(The moment of truth)

3. 3
Digital Disruption

4. 4
Context

5. 5
Have you ever wonder….
Why Amazon, Apple, Google, Facebook and
Netflix are so successful?

6. 6
They understand one major thing

7. 7
The real you!

8. 8
In particular,…
Your digital footprint

9. 9
Let’s think for a moment
Every object, person and organization has a
digital information halo

10. 10
And the data…
… is becoming more robust, valuable and
complex

11. 11
This is the new normal
Business
changes
Technology
changes

12. 12
Based on…
S
(Social)
M
(Mobile)
A
(Analytics)
C
(Cloud)
helping to
create
personal
customer
experiences

13. 13
Challenges

14. 14
Challenge 1: Capture
Source: Bitly
http://blog.bitly.com/post/9887686919/you-just-shared-a-link-how-long-will-people-pay
Faster data, reduce
lifespan

15. 15
Challenge 2: Storage
0.8$
ZB$
*Ze#abyte:)1,000,000,000,000,000,000,000)bytes)
(#1#trillion#gigabytes)#
2009$
2020$
40##
Ze5abytes*#
Source: IDC
IDC Digital Universe Study, 2012, Sponsored by EMC
More data! what is
relevant?

16. 16
Challenge 3: Analisis
The more sources we have,
the more complex to
extract the value
Source: IDC
IDC Digital Universe Study, 2012, Sponsored by EMC
2011: 50.07 Tb/s
2012: 86.54 Tb/s
Data in transit: 856 Tb/s

17. 17
Challenge 4: Visualization
New data sources and
formats and increased
volume need new
communication techniques

18. 18
Challenge 5: IT Dependence
Technology is deeply embedded into our business processes

19. 19
Challenge 6: Create a new culture
• Attitude
• Habits
• Knowledge

20. 20
Big Data

21. What is big data
Volume Velocity Variety

22. 22
Velocity
Variety
Volume
· Batch
· Near Real Time
· Real Time
· Streaming
· Structured
· Semi-structured
· Unstructured
Volume +
Variety
Volume +
Velocity
Velocity +
Variety
· Terabyte
· Petabyte
· Exabyte
· Zettabyte
· Yottabyte
Volume + Velocity
+ Variety

23. 23
More Vs

24. The most important V

25. 25
Big Data is not new
KB
Files
Statistics
COBOL
GB
Tables
OLAP
Cubes
SQL
TB
Semi-structured
Apps
XML
PB
Dynamic
Variety
Mahout (&
other)
NoSQL
Big
Data
Analítica
Language
60s 80 - 96 97 - 07 07 - ?

26. 26
3Vs are not enough
Velocity
Variety
Volume
· Batch
· Near Real Time
· Real Time
· Streaming
· Structured
· Semi-structured
· Unstructured
Volume +
Variety
Volume +
Velocity
Velocity +
Variety
· Terabyte
· Petabyte
· Exabyte
· Zettabyte
· Yottabyte
Volume + Velocity
+ Variety
• Horizontal scalability
• Relational constraints

27. 27
Relational Constraints
Non Relational
Schema-free
Distributed
4 Types: Key-value,
column-oriented,
graph, document
Relational
Data is normalised and
static
Relational schema
Data in one repository

28. 28
Horizontal Scalability

29. What is big data (II)
Batch
Processing
(Volume)
Stream
Processing
(Velocity)
NoSQL
(Variety)

30. 30
Big Data Layers

31. 31
Hadoop is the most famous technology

32. 32
Big Data
RDBMS
Data Visualization
Predictive
Analytics
Hadoop +
Content Search &
Analytics
In-memory
Streaming
Technologies
Object & Graph
Databases
It is not the only one

33. 33
We are moving from…
Information
Systems
Data
Mobile
Data
Machine
Data
Social
Media Data
Audio,
video, text
Stream
Data
Sources
Corporate Information Factory / Data Warehousing
Storage&
Processing
Information Management
Data Governance Master Data Management
Data
Management
Analysis
Analytics
Operational
Intelligence
Business
Intelligence

34. 34
to a new architecture
Information
Systems
Data
Mobile
Data
Machine
Data
Social
Media Data
Audio,
video, text
Stream
Data
Sources
Corporate
Information
Factory / Data
Warehousing
Storage&
Processing
Information Management
Data Governance Master Data Management
Data
Management
Analysis
Analytics
Operational
Intelligence
Business
Intelligence
Big Data
NoSQL
In-
memory
MPP HPC
Data Products

35. 35
Big Data Market

36. 36
The Big Data market is growing
Source: IDC
IDC Worldwide Big Data Technology and Services 2010 – 2015 Forecast, March 2013
0
10
20
30
40
2011 2012 2013 2013 2015 2016 2017
32.4
25.7
20.4
16.1
12.6
9.8
7.4

37. 37
Hadoop is leading the way
1991 1992 1994 1995 1997 2000
Project
emerge
Community
creation
Code is
available,
community
grows
First
companies
Ecosystem
emergence
Mainstream,
M&A Starts
2006 2007 2008 2009 2012 2015

38. 38
Many tecnologies
Source: 451Research
Database Landscape Map - February 2014

39. 39
From data to business
Source: IDC

40. 40
The number of companies is growing
Source: Delfos Research
Worldwide Data Market 1836 - 2015

41. 41
But our expectations are high
Fuente: Gartner
Hype Cycle for Big Data,2012
Fuente: Gartner
Gartner HypeCycle 2013

42. 42
New needs

43. 43
A new organization

44. 44
Our data needs
Available
Accesible
Quality
Right time
Security
Information

45. 45
Which technology is required?
Data
Market
BD
BA
BI
DM
DT

46. 46
Which capacities do we need?

47. 47
How do I have to compete?
Decoding the digital
footprint

48. 48
Combining different factors
Amplificator Interface
Algorithms Data
Business model

49. 49
We need to compete on innovation
Wow moments
Great virtual
design
Great physical
design

50. 50
Business Models

51. 51
Data-driven business models

52. 52
How to generate value

53. 53
Optimization ≠ innovation

54. 54
Data-driven Business Models
Source: University of Cambridge
A Taxonomy of Data-driven Business Models used by Start-up Firms
Data-driven
Business
Model
Key Activities
Customer
Segment
Revenue
Model
Key Resources Cost Structure

55. 55
Data
the taxonomy
14
Data Sources
Internal
existing data
Self-generated
Data
External
Acquired Data
Customer
provided
Free available
Open Data
Social Media data
Web Crawled
Data
Source: University of Cambridge
A Taxonomy of Data-driven Business Models used by Start-up Firms

56. 56
Activities
Dimension: Activities
15
Key Activity
Data Generation
Crowdsourcing
Tracking & Other
Data Acquisition
Processing
Aggregation
Analytics
descriptive
predictive
prescriptiveVisualization
Distribution
Source: University of Cambridge
A Taxonomy of Data-driven Business Models used by Start-up Firms

57. 57
Value proposition
16
Offering
Data
Information/Knowledge
Non-Data
Product/Service
Source: University of Cambridge
A Taxonomy of Data-driven Business Models used by Start-up Firms

58. 58
Revenue model
17
Revenue Model
Asset Sale
Lending/Renting/Leasing
Licensing
Usage fee
Subscription fee
Advertising
Source: University of Cambridge
A Taxonomy of Data-driven Business Models used by Start-up Firms

59. 59
Customers
18
Target Customer
B2B
B2C
Source: University of Cambridge
A Taxonomy of Data-driven Business Models used by Start-up Firms

60. 60
Some types are emerging
activities and key data sources
29
Type F
Type A Type D
Type E Type B
Type C
Aggregation Analytics Data generation
Free
available
Customer
provided
Tracked&
generated Key activity
KeyDataSource
6 significant Business Model types were
identified
28
Type B: “Analytics-as-a-Service”
Type C: “Data generation & Analytics”
Type D: “Free Data Knowledge Discovery”
Type A: “Free Data Collector & Aggregator”
Type E: “Data Aggregation-as-a-Service”
Type F: “Multi-Source data mashup and analysis”
Source: University of Cambridge
A Taxonomy of Data-driven Business Models used by Start-up Firms

61. 61
One example: CBInsights
Data-driven
Business Model
Data Processing,
Aggregation &
Analysis
Investors, media
Subscription fee
External
Companies Data
Platform, data
scientists

62. 62
Some examples

63. 63
Big Data around the world
Credit Suisse Netflix
TescoWalMart
General Motors
Disney Metlife
Apple
Caesars
Entertainment
SpotifyHouston Rockets NFL

64. 64
The dark side of Big Data

65. 65
The dark force is strong in your
company

66. Google knows
about you more
than you think
A graph to rule
them all
NSA
66
Where is the privacy?

67. 67
Even your TV watches you!

68. 68
Responsibilities
Integrity
Confidentiality
Authenticity
Availability

69. 69
Big Data limits

70. Internal limits
Skills Culture Integration
70

71. External limits
Regulation
Data ≠
Correlation
Models ≠
Reality
71

72. 72
Implementing Big Data

73. 73
Implementing
Big Data

74. Many reasons to fail
IMPLEMENTATION
FAILURE
COST
OPERATIONAL STRATEGIC
Lack of top
management
commitment
Unavailability of
subject matters
experts
Unavailability of
key users for
UTA
Poor quality
of testing
Poor
Knowledge
Transfer
Cost
Overrun
Unrealistic
ROI
TECHNOLOGY PEOPLE
Poor
Data Quality
Over
Customization
Inadequate data
sources knowledge
Poor IT
infrastructure
Poor
ETL Quality
Poor BI
product selection
User resistance
To change
High rotation of
Project team members
Inadequate
resources
Poor user
involvement
TACTIC
Inadequate training
and education
Non-empowered
decision-makers
Poor
departmental
alignment
Inappropriate
timing to go live
Poor
communication
Unrealistic
expectations
Inadequate
functional
requirements
Inadequate project
team composition
Poor project
management
Unrealistic project
scheduling
Ineffective organizational
change management
74

75. How to start

76. Framing the problem
• Identify business needs
• Conceptualise business opportunities
• Determine Big Data type
• Define Big Data Strategy

77. Pilot and beyond
• Develop model
• Identify data set
• Build / Buy / Subscribe to big data
architecture
• Create pilot
• Scale pilot

78. Communication
• Comunicate outcome
• Identify next steps

79. 79
Your future big data project

80. 80
Data Strategies

81. 81
What we want
Time
Time to action
Lostvalue
Data latency
Analysis latency
Decision-making latency
Business Event
Data ready for analysis
Information
Action
Business Value

82. 82
When we need them
BI (mature) BA (mature)
Big Data
(emerging)
Tools Query, reporting, OLAP, alerts
Forecasting, regression and modeling
and/ or BI
Machine learning,
visualization
Focus
What happened, how many, how
often, what is the problem, what
action is needed
Why is this happening, what if these
trends continue, what will happen
next, what is the best that can happen
Capture, storing and
analyzing data: all
Use Reactive Proactive / Predictive / Prescriptive All / none
Types of
data
structured Structured / semi-structured All
Data
Complexity
Low Low / medium High
Scope Management Processes Vertical / processes

83. 83
Becoming data-driven

84. 84
Traditional Knowledge, Literacy and Skills
Computer Literacy
Analytic Proficiency
Data Proficiency
Operational Proficiency
Total Information Proficiency
Building
traditional
capabilities
and skills
Mastering
technology
Automating
clerical work
Reengineering
business
processes
Building
ubiquitous
knowledge
bases
Optimizing all
decisions
We want to create digital competencies

85. 85
We need to measure the value (McDonald 2004)
ValueCreated
Overall Success of the Initiative
Implementation
Success
• On- time
• On-budget
User Success
• User adoption
• User Satisfaction
• Data Problems
Operational Success
• Productivity
Improvements
• Process efficiency and
effectiveness
• Key Performance
Indicators
Business Success
• Return on Investment
• Economic Value Add
• Revenue increases
• Cost Savings
• Customer / Corporate
profits
• Enables Business
Strategy and
Competitive Advantage
• Create a formal, continuos process for measuring
success and value generated
• Identify and measure results of each project phase
• Establish realistic goals and expectations based on
capability / maturity

86. 86
We need to become data-driven
Source: Thomas H. Davepnort

88. Josep Curto
@josepcurto
jcurto@faculty.ie.edu